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What is cloud architecture?Cloud architecture defines the fundamental components of a cloud computing environment—the front end, the back end, the networking and the delivery model—and describes how those components are combined to run a specific application or applications.Based on business needs, a cloud architecture serves as a design strategy for connecting the cloud-based infrastructure for running and deploying applications. Cloud architecture considers an organization’s workload requirements and operational costs to deliver the flexibility, scalability and cost-savings of cloud computing.Cloud architecture componentsCloud computing architecture integrates four essential components to create an IT environment that abstracts, pools and shares scalable resources across one or more cloud environments.A front-endA back-endA networkA cloud-based delivery platformCloud architectures vary based on an organization’s unique business drivers and technology requirements. Still, they all share the same goal of creating a roadmap that considers application workloads, cloud deployment models, service management and design needs.1. The front-endFront-end cloud architecture refers to the user- or client-side of the cloud computing system. It consists of graphic user interfaces (GUIs), dashboards and navigation tools that provide on-demand access to cloud services and resources. Key components include software apps and programs installed on devices (such as., mobile phone, laptop or desktop) to access the cloud platform or service. Accessing a web-based video communications application (for example, Zoom, Webex) via a laptop computer or ordering food through a mobile delivery platform (Uber Eats, DoorDash) are both examples of front-end cloud architecture capabilities.2. The back-endWhile the front-end includes all elements related to the client (for example, a visitor to an e-commerce site), the back-end (or ‘server-side’) refers to the structuring of the site and the programming of its main functionalities. It provides all of the behind-the-scenes technology (cloud servers, cloud databases, application programming interfaces (APIs) to access files) used by the CSP to support the front-end, including all the code that helps a database or web server communicate with a web browser or a mobile operating system.Back-end cloud architecture components include the following:Applications: Back-end apps are the software or platforms that deliver the client service requests on the front-end.Cloud computing service: The back-end service provides utility in cloud architecture and manages the accessibility of cloud-based resources (such as, cloud-based storage services, application development services, web services, security services, and more).Cloud runtime: Runtime provides the environment (operating system, hardware, memory) for executing or running services. Virtualization plays a crucial role in enabling multiple runtimes on the same server. (Read more about virtualization below.)Cloud storage: Cloud storage in the back-end refers to the flexible and scalable storage service and management of data stored to carry out applications.Infrastructure: Infrastructure consists of all the back-end resources or hardware (such as, servers, databases, CPU (central processing unit), network devices like routers and switches, graphics processing unit (GPU), and so on.) and all the software used to run and manage cloud-based services. In cloud-computing speak, the term infrastructure is sometimes confused with cloud architecture, but there’s a distinct difference. Like a blueprint for constructing a building, cloud architecture serves as the design plan for building cloud infrastructure.Management software: Middleware coordinates communication between the front-end and back-end in a cloud computing system. This component allows for the delivery of services in real-time to ensure smooth front-end user experiences.Security tools: Security tools provide the back-end security (also referred to as service-side security) for potential cyberattacks or system failures. Virtual firewalls protect web applications, prevent data loss and ensure backup and disaster recovery. Back-end components include encryption, access restriction and authentication protocols to protect data from breaches.3. A networkAn internet connection typically connects the front-end with the back-end functions. An intranet—a privately maintained computer network accessed only by authorized persons and limited to one institution—or an intercloud connection may also connect the back-end and front-end. A cloud network should provide high bandwidth and low latency, allowing users to continuously access their data and applications. The network must also provide agility so that access to resources can occur quickly and efficiently between servers and cloud-based environment.Other significant cloud architecture networking gear includes load balancers, content delivery networks (CDNs) and software-defined networking (SDN) to ensure data flows smoothly and securely between front-end users and back-end resources.4. Cloud-based delivery modelsThere are three main types of cloud delivery models (also known as cloud service models): IaaS, PaaS and SaaS. These models are not mutually exclusive. Most large enterprises use all three as part of their cloud delivery stack:IaaS, or Infrastructure-as-a-Service, is the on-demand access to cloud-hosted physical and virtual servers, storage and networking—the back-end IT infrastructure for running applications and workloads in the cloud. IaaS allows organizations to scale and shrink infrastructure resources as needed. This cloud-based service helps them avoid the high costs associated with building and managing an on-premises data center, providing the capacity to accommodate highly variable or ‘spiky’ workloads.PaaS, or Platform-as-a-Service, is the on-demand access to a complete, ready-to-use cloud computing platform for developing, running and managing applications. PaaS can simplify the migration of existing applications to the cloud through re-platforming (moving an application to the cloud with modifications that take better advantage of cloud scalability, load balancing and other capabilities) or refactoring (re-architecting some or all of an application using microservices, containers and other cloud-native technologies).SaaS, or Software-as-a-Service, is the on-demand access to ready-to-use, cloud-hosted application software (such as, Salesforce, Mailchimp). SaaS offloads all software development and infrastructure management to the cloud service provider. Because the software (application) is already installed and configured, users can provision the cloud-based server instantly and have the application ready for use in hours. This capability reduces the time spent on installation and configuration and speeds up software deployment.According to a Gartner report, almost two-thirds (65.9%) of enterprise IT spending will go toward Software-as-a-Service in 2025, up from 57.7% in 2022.Other popular service platforms include the following:Serverless computing (or serverless): Serverless is a cloud application development and execution model that allows developers to build and run code without provisioning or managing servers or back-end infrastructure.Business-Process-as-a-Service (BPaaS): BPaaS is a business process outsourcing platform that combines IaaS, PaaS and SaaS services.Function-as-a-Service (FaaS): FaaS is a subset of SaaS in which application code runs only in response to specific events or requests. FaaS makes it easier for DevOps and other teams to run and manage microservices applications.Key cloud architecture technologiesThe following are a few of the most critical technologies for developing cloud architecture.VirtualizationCrucial to cloud architecture, virtualization acts as an abstraction layer that enables the hardware resources of a single computer—processors, memory, storage and more—to be divided into multiple virtual computers known as virtual machines (VMs). Virtualization connects physical servers maintained by a cloud service provider (CSP) at numerous locations, then divides and abstracts resources to make them accessible to end users wherever there is an internet connection. Besides virtualizing servers, cloud technology uses many other forms of virtualization, including network virtualization and storage virtualization.AutomationCloud automation involves implementing tools and processes that reduce or eliminate the manual work associated with provisioning, configuring and managing cloud environments. Cloud automation tools run on top of virtualized environments and play an essential role in enabling organizations to take more significant advantage of the benefits of cloud computing, like the ability to leverage cloud resources on demand and scale them up and down on an as-needed basis. Automation plays a vital role in DevOps workflows, speeding up tasks related to building, testing, deploying and monitoring applications, resulting in cost savings and faster time to market.Cloud deployment modelsThere are four main cloud delivery models, each offering unique features for running workloads and optimizing business value.Public cloudA public cloud is a computing model where a cloud service provider makes computing resources (such as, software applications, development platforms, VMs, bare metal servers, and more) available to users over the public internet. CSPs sell these resources according to subscription-based or pay-per-usage pricing models.Public cloud environments are multi-tenant, where users share a pool of virtual resources automatically provisioned for and allocated to individual tenants through a self-service interface. This feature allows providers to maximize utilization of their data center hardware and infrastructure, thus offering cloud customers services for the lowest possible costs with access from anywhere.Private cloudA private cloud is a single-tenant cloud environment where all resources are isolated and operated exclusively for one organization. Private cloud combines many benefits of cloud computing with the security and control of on-premises IT infrastructure. For instance, companies that must meet strict regulatory compliance requirements, such as healthcare or financial institutions, may choose private clouds for their sensitive data using customized security measures like firewalls, virtual private networks (VPNs), data encryption and API keys.Hybrid cloudA hybrid cloud combines public cloud, private cloud and on-premises (‘on-prem’) infrastructure to create a single IT infrastructure so companies can get the best out of all computing environments to meet their business needs. Organizations favor a hybrid cloud model for its agility in moving applications and workloads across cloud environments based on technological or business goals.For instance, an enterprise with concerns surrounding sensitive data (such as, intellectual property, personally identifiable information (PII), medical records, and more) can store them in a private cloud. For other workloads, such as web hosting or content hosting, businesses may choose a public cloud setting for its cost savings and ability to scale resources up and down based on user traffic (for example, scale up during a social media campaign promoting a new product).According to the IBM Transformation Index: State of Cloud, over 77% of business and IT professionals have adopted a hybrid cloud approach.Hybrid multicloudToday, most enterprise businesses merge a hybrid cloud with a multicloud environment. A multicloud is a cloud computing model that incorporates multiple cloud services from more than one provider within the same IT infrastructure. Together, hybrid and multicloud models create a hybrid multicloud architecture that offers businesses the flexibility to create the best of both cloud computing worlds for migrating, building and optimizing applications across multiple clouds.In addition to offering the control and flexibility to choose the most cost-effective cloud service, hybrid multicloud provides the most control over where organizations can deploy and scale workloads (for example, deploy closer to edge environments), further improving performance. Each cloud provider offers its unique services. Businesses can customize a mix of network, storage and cloud solutions from different cloud providers to find the best-in-class solutions. For instance, a company may use IBM Cloud for its advanced data and artificial intelligence (AI) capabilities, Microsoft Azure for its compliance and security features and Google Cloud for its global networking reach.

What is Data Science: Lifecycle, Applications and PrerequisitesIntroductionData science is an essential part of many industries today, given the massive amounts of data that are produced, and is one of the most debated topics in IT circles. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction. In this article, we’ll learn what is data science, its applications, and how you can become a data scientist.What Is Data Science?Data science is the domain of study that deals with vast volumes of data using modern tools and techniques, including essential data science skills, to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models. The data used for analysis can come from many different sources and presented in various formats.The Data Science LifecycleNow that you know what is data science, next up let us focus on the data science lifecycle. Data science’s lifecycle consists of five distinct stages, each with its own tasks:Capture: Data Acquisition, Data Entry, Signal Reception, Data Extraction. This stage involves gathering raw structured and unstructured data.Maintain: Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture. This stage covers taking the raw data and putting it in a form that can be used.Process: Data Mining, Clustering/Classification, Data Modeling, Data Summarization. Data scientists take the prepared data and examine its patterns, ranges, and biases to determine how useful it will be in predictive analysis.Analyze: Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis. Here is the real meat of the lifecycle. This stage involves performing the various analyses on the data.Communicate: Data Reporting, Data Visualization, Business Intelligence, Decision Making. In this final step, analysts prepare the analyses in easily readable forms such as charts, graphs, and reports.Data Science PrerequisitesHere are some of the technical concepts you should know about before starting to learn what is data science.1. Machine Learning: Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.2. Modeling: Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.3. Statistics: Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.4. Programming: Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.5. Database: A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.Who Oversees the Data Science Process?1. Business ManagersThe business managers are the people in charge of overseeing the data science training method. Their primary responsibility is to collaborate with the data science team to characterise the problem and establish an analytical method. A data scientist may oversee the marketing, finance, or sales department, and report to an executive in charge of the department. Their goal is to ensure projects are completed on time by collaborating closely with data scientists and IT managers.2. IT ManagersFollowing them are the IT managers. If the member has been with the organisation for a long time, the responsibilities will undoubtedly be more important than any others. They are primarily responsible for developing the infrastructure and architecture to enable data science activities. Data science teams are constantly monitored and resourced accordingly to ensure that they operate efficiently and safely. They may also be in charge of creating and maintaining IT environments for data science teams.3. Data Science ManagersThe data science managers make up the final section of the tea. They primarily trace and supervise the working procedures of all data science team members. They also manage and keep track of the day-to-day activities of the three data science teams. They are team builders who can blend project planning and monitoring with team growth.What is a Data Scientist?If learning what is data science sounded interesting, understanding what does this job roles is all about will me much more interesting to you. Data scientists are among the most recent analytical data professionals who have the technical ability to handle complicated issues as well as the desire to investigate what questions need to be answered. They're a mix of mathematicians, computer scientists, and trend forecasters. They're also in high demand and well-paid because they work in both the business and IT sectors. On a daily basis, a data scientist may do the following tasks:Discover patterns and trends in datasets to get insightsCreate forecasting algorithms and data modelsImprove the quality of data or product offerings by utilising machine learning techniquesDistribute suggestions to other teams and top managementIn data analysis, use data tools such as R, SAS, Python, or SQLTop the field of data science innovationsWhat Does a Data Scientist Do?You know what is data science, and you must be wondering what exactly is this job role like - here's the answer. A data scientist analyzes business data to extract meaningful insights. In other words, a data scientist solves business problems through a series of steps, including:Before tackling the data collection and analysis, the data scientist determines the problem by asking the right questions and gaining understanding.The data scientist then determines the correct set of variables and data sets.The data scientist gathers structured and unstructured data from many disparate sources—enterprise data, public data, etc.Once the data is collected, the data scientist processes the raw data and converts it into a format suitable for analysis. This involves cleaning and validating the data to guarantee uniformity, completeness, and accuracy.After the data has been rendered into a usable form, it’s fed into the analytic system—ML algorithm or a statistical model. This is where the data scientists analyze and identify patterns and trends.When the data has been completely rendered, the data scientist interprets the data to find opportunities and solutions.The data scientists finish the task by preparing the results and insights to share with the appropriate stakeholders and communicating the results.Why Become a Data Scientist?You learnt what is data science. Did it sound exciting? Here's another solid reason why you should pursue data science as your work-field. According to Glassdoor and Forbes, demand for data scientists will increase by 28 percent by 2026, which speaks of the profession’s durability and longevity, so if you want a secure career, data science offers you that chance. So, if you’re looking for an exciting career that offers stability and generous compensation, then look no further!Uses of Data ScienceData science may detect patterns in seemingly unstructured or unconnected data, allowing conclusions and predictions to be made.Tech businesses that acquire user data can utilise strategies to transform that data into valuable or profitable information.Data Science has also made inroads into the transportation industry, such as with driverless cars. It is simple to lower the number of accidents with the use of driverless cars. For example, with driverless cars, training data is supplied to the algorithm, and the data is examined using data Science approaches, such as the speed limit on the highway, busy streets, etc.Data Science applications provide a better level of therapeutic customisation through genetics and genomics research.Where Do You Fit in Data Science?Now that you know the uses of Data Science and what is data science in general, let's see all the opportunity that this feild offers to focus on and specialize in one aspect of the field. Here’s a sample of different ways you can fit into this exciting, fast-growing field.Data ScientistJob role: Determine what the problem is, what questions need answers, and where to find the data. Also, they mine, clean, and present the relevant data.Skills needed: Programming skills (SAS, R, Python), storytelling and data visualization, statistical and mathematical skills, knowledge of Hadoop, SQL, and Machine Learning.Data AnalystJob role: Analysts bridge the gap between the data scientists and the business analysts, organizing and analyzing data to answer the questions the organization poses. They take the technical analyses and turn them into qualitative action items.Skills needed: Statistical and mathematical skills, programming skills (SAS, R, Python), plus experience in data wrangling and data visualization.Data EngineerJob role: Data engineers focus on developing, deploying, managing, and optimizing the organization’s data infrastructure and data pipelines. Engineers support data scientists by helping to transfer and transform data for queries.Skills needed: NoSQL databases (e.g., MongoDB, Cassandra DB), programming languages such as Java and Scala, and frameworks (Apache Hadoop).Applications of Data ScienceThere are various applications of data science, including:1. HealthcareHealthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.2. GamingVideo and computer games are now being created with the help of data science and that has taken the gaming experience to the next level.3. Image RecognitionIdentifying patterns is one of the most commonly known applications of data science. in images and detecting objects in an image is one of the most popular data science applications.4. Recommendation SystemsNext up in the data science applications list comes Recommendation Systems. Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.5. LogisticsData Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.6. Fraud DetectionFraud detection comes the next in the list of applications of data science. Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.7. Internet SearchInternet comes the next in the list of applications of data science. When we think of search, we immediately think of Google. Right? However, there are other search engines, such as Yahoo, Duckduckgo, Bing, AOL, Ask, and others, that employ data science algorithms to offer the best results for our searched query in a matter of seconds. Given that Google handles more than 20 petabytes of data per day. Google would not be the 'Google' we know today if data science did not exist.8. Speech recognitionSpeech recognition is one of the most commonly known applications of data science. It is a technology that enables a computer to recognize and transcribe spoken language into text. It has a wide range of applications, from virtual assistants and voice-controlled devices to automated customer service systems and transcription services.9. Targeted AdvertisingIf you thought Search was the most essential data science use, consider this: the whole digital marketing spectrum. From display banners on various websites to digital billboards at airports, data science algorithms are utilised to identify almost anything. This is why digital advertisements have a far higher CTR (Call-Through Rate) than traditional marketing. They can be customised based on a user's prior behaviour. That is why you may see adverts for Data Science Training Programs while another person sees an advertisement for clothes in the same region at the same time.10. Airline Route PlanningNext up in the data science and its applications list comes route planning. As a result of data science, it is easier to predict flight delays for the airline industry, which is helping it grow. It also helps to determine whether to land immediately at the destination or to make a stop in between, such as a flight from Delhi to the United States of America or to stop in between and then arrive at the destination.11. Augmented RealityLast but not least, the final data science applications appear to be the most fascinating in the future. Yes, we are discussing something other than augmented reality. Do you realise there's a fascinating relationship between data science and virtual reality? A virtual reality headset incorporates computer expertise, algorithms, and data to create the greatest viewing experience possible. The popular game Pokemon GO is a minor step in that direction. The ability to wander about and look at Pokemon on walls, streets, and other non-existent surfaces. The makers of this game chose the locations of the Pokemon and gyms using data from Ingress, the previous app from the same business.Example of Data ScienceHere are some brief example of data science showing data science’s versatility.Law Enforcement: In this scenario, data science is used to help police in Belgium to better understand where and when to deploy personnel to prevent crime. With only limited resources and a large area to cover data science used dashboards and reports to increase the officers’ situational awareness, allowing a police force that’s spread thin to maintain order and anticipate criminal activity.Pandemic Fighting: The state of Rhode Island wanted to reopen schools, but was naturally cautious, considering the ongoing COVID-19 pandemic. The state used data science to expedite case investigations and contact tracing, enabling a small staff to handle an overwhelming number of concerned calls from citizens. This information helped the state set up a call center and coordinate preventative measures.Challenges of a Data ScientistSome of the common challenges that a data scientist faces, include:Handling large and messy datasets that require cleaning and organization.Selecting the right tools and techniques for analysis.Ensuring accurate and unbiased results.Communicating complex findings to non-technical stakeholders.Aligning data projects with business goals.Keeping up with rapidly evolving technologies.Managing data privacy and security concerns.Data Science vs Business IntelligenceData Science and Business Intelligence (BI) are both data-driven fields but differ in focus and approach. Data Science emphasizes predictive and prescriptive analytics, using advanced techniques like machine learning and AI to forecast trends and provide actionable recommendations. It deals with raw, unstructured, and large datasets to solve complex problems and discover new opportunities.On the other hand, Business Intelligence focuses on descriptive analytics, analyzing structured data from databases to generate reports, KPIs, and dashboards that summarize past and present performance. While Data Science is exploratory and future-oriented, BI is analytical and operational, helping business managers and executives make informed decisions based on historical data insights.FAQs1. What is data science in simple words?Data science, in simple words, is the field of study that involves collecting, analyzing, and interpreting large sets of data to uncover insights, patterns, and trends that can be used to make informed decisions and solve real-world problems.2. What is data science used for?Data science is used for a wide range of applications, including predictive analytics, machine learning, data visualization, recommendation systems, fraud detection, sentiment analysis, and decision-making in various industries like healthcare, finance, marketing, and technology.3. What’s the difference between data science, artificial intelligence, and machine learning?Artificial Intelligence makes a computer act/think like a human. Data science is an AI subset that deals with data methods, scientific analysis, and statistics, all used to gain insight and meaning from data. Machine learning is a subset of AI that teaches computers to learn things from provided data.4. What does a data scientist do?A data scientist analyzes business data to extract meaningful insights.5. What kinds of problems do data scientists solve?Data scientists solve issues like:Loan risk mitigationPandemic trajectories and contagion patternsEffectiveness of various types of online advertisementResource allocation6. Do data scientists code?Sometimes they may be called upon to do so.7. What is the data science course eligibility?If you wish to know anything about our data science course, please check out Data Science Bootcamp and Data Science master’s program.8. Can I learn data science on my own?Data science is a complex field with many difficult technical requirements. It’s not advisable to try learning data science without the help of a structured learning program.

What is software development?Software development refers to a set of computer science activities that are dedicated to the process of creating, designing, deploying and supporting software.Software itself is the set of instructions or programs that tell a computer what to do. It is independent of hardware and makes computers programmable.The goal of software development is to create a product that meets user needs and business objectives in an efficient, repeatable and secure way. Software developers, programmers and software engineers develop software through a series of steps called the software development lifecycle (SDLC). Artificial intelligence-powered tools and generative AI are increasingly used to assist software development teams in producing and testing code.Modern enterprises often use a DevOps model—a set of practices, protocols and technologies used to accelerate the delivery of higher-quality applications and services. DevOps teams combine and automate the work of software development and IT operations teams. DevOps teams focus on continuous integration and continuous delivery (CI/CD), processes that use automation to deploy small, frequent updates to continually improve software performance.So much of modern life—business or otherwise—relies on software solutions. From the phones and computers used for personal tasks or to complete our jobs, to the software systems in use at the utility companies that deliver services to homes, businesses and more. Software is ubiquitous and software development is the crucial process that brings these applications and systems to life.Types of softwareTypes of software include system software, programming software, application software and embedded software:System software provides core functions such as operating systems, disk management, utilities, hardware management and other operational necessities.Programming software gives programmers tools such as text editors, compilers, linkers, debuggers and other tools to create code.Application software (applications or apps), such as office productivity suites, data management software, media players and security programs help users complete specific tasks. Applications also refer to web and mobile applications such as those used to shop on retail websites or interact with content on social media sites.  Embedded software is used to control devices not typically considered computers including telecommunications networks, cars, industrial robots and more. These devices and their software, can be connected as part of the Internet of Things (IoT).Software can be designed as custom software or commercial software. Custom software development is the process of designing, creating, deploying and maintaining software for a specific set of users, functions or organizations.In contrast, commercial off-the-shelf software (COTS) is designed for a broad set of requirements, enabling it to be packaged and commercially marketed and distributed.Who develops software?Programmers, software engineers and software developers primarily conduct software development. These roles interact, overlap and have similar requirements, such as writing code and testing software. The dynamics between them vary greatly across development departments and organizations.Programmers (coders)Programmers, or coders, write source code to program computers for specific tasks such as merging databases, processing online orders, routing communications, conducting searches or displaying text and graphics. They also debug and test software to make sure the software does not contain errors.Programmers typically interpret instructions from software developers and engineers and use programming languages such as C++, Java™, JavaScript and Python to implement them.Software engineers Software engineers design, develop, test and maintain software applications. As a managerial role, software engineers engage in problem solving with project managers, product managers and other team members to account for real-world scenarios and business goals. Software engineers consider full systems when developing software, making sure that operating systems meet software requirements and that various pieces of software can interact with each other.Beyond the building of new software, engineers monitor, test and optimize applications after they are deployed. Software engineers oversee the creation and deployment of patches, updates and new features.Software developers Like software engineers, software developers design, develop and test software. Unlike engineers, they usually have a specific, project-based focus.A developer might be assigned to fix an identified error, work with a team of developers on a software update or to develop a specific aspect of a new piece of software. Software developers require many of the same skills as engineers but are not often assigned to manage full systems.Steps in the software development processThe software development life cycle (SDLC) is a step-by-step process that development teams use to create high-quality, cost-effective and secure software. The steps of the SDLC are:PlanningAnalysisDesignImplementationTestingDeploymentMaintenanceThese steps are often interconnected and might be completed sequentially or in parallel depending on the development model an organization uses, the software project and the enterprise. Project managers tailor a development team’s workflows based on the resources available and the project goals.The SDLC includes the following tasks, though the tasks might be placed in different phases of the SDLC depending on how an organization operates.Requirements managementThe first step of planning and analysis is to understand what user needs the software should be designed to meet and how the software contributes to business goals. During requirements management, analysis or requirements gathering, stakeholders share research and institutional knowledge such as performance and customer data, insights from past developments, enterprise compliance and cybersecurity requirements and the IT resources available.This process enables project managers and development teams to understand the scope of the project, the technical specifications and how tasks and workflows are organized.Developing a designAfter establishing project requirements, engineers, developers and other stakeholders explore the technical requirements and mock up potential application designs. Developers also establish which application programming interfaces (APIs) will connect the application with other applications, systems and user interfaces. Sometimes existing APIs can be used, other times new APIs are needed.Building a modelIn this step, teams build an initial model of the software to conduct preliminary testing and discover any obvious bugs. DevOps teams can use modeling language such as SysML or UML to conduct early validation, prototyping and simulation of the design.Constructing codeUsing the knowledge gained by modeling, software development teams begin to write the code that turns the designs into a functioning product. Traditionally writing code is a manual process, but organizations are increasingly using artificial intelligence (AI) to help generate code and speed the development process.TestingQuality assurance (QA) is run to test the software design. The tests look for flaws in the code and potential sources of errors and security vulnerabilities. DevOps teams use automated testing to continuously test new code throughout the development process.DeployingA software integration, deployment or release means that the software is made available to users. Deployment involves setting up database and server configurations, procuring necessary cloud computing resources and monitoring the production environment. Development teams often use infrastructure as code (IaC) solutions to automate the provisioning of resources. Such automations help simplify scaling and reduce costs.Often organizations use preliminary releases, such as beta tests, before releasing a new product to the public. These tests release the product to a selected group of users for testing and feedback and enable teams to identify and address unforeseen issues with the software before a public release.OptimizationAfter deployment, DevOps teams continue to monitor and test the performance of the software and perform maintenance and optimization whenever possible. Through a process called continuous deployment, DevOps teams can automate the deployment of updates and patches without causing service disruptions.Documentation Keeping a detailed accounting of the software development process helps developers and users troubleshoot and use applications. It also helps maintain the software and develop testing protocols.Software development modelsSoftware development models are the approach or technique that teams take to software development. They dictate the project workflow, how tasks and processes are completed and checked, how teams communicate and more.When selecting a model for development, project managers consider the scope of the project, the complexity of the technical requirements, the resources available, the size and experience of the team, the deadline for release and the budget.Common software development models include:WaterfallWaterfall is a traditional software development model that sets a series of cascading linear steps from planning and requirements gathering through deployment and maintenance. Waterfall models are less flexible than agile methodologies. Development can be delayed if a step is not completed and it is often costly and time-consuming to revert to previous steps if an issue is discovered. This process can be valuable for simple software with few variables.V-shapedThis model creates a V-shaped framework with one leg of the “V” following the steps of the SDLC and the other leg dedicated to testing. Like the waterfall approach, V-shaped models follow a linear series of steps.The main difference is that V-shaped development has associated testing built into each step that must be completed for development to proceed. Robust software testing can help identify issues in code early but has some of the same shortcomings of the waterfall effect—it is less flexible and can be difficult to revert to a previous step.IterativeThe iterative model focuses on repeated cycles of development, with each cycle addressing a specific of requirements and functions. Each cycle or iteration of development adds and refines functions and is informed by previous cycles. The principles of the iterative model, mainly the cyclical nature of working, can be applied to other forms of development.AgileThis iterative approach to software development breaks larger projects into smaller “sprints” or consumable functions and delivers rapidly on those functions through incremental development. A constant feedback loop helps find and fix defects and enables teams to move more fluidly through the software development process.DevOpsThe DevOps approach is a further development of the agile model. DevOps combines the work of development and IT operations teams and uses automation to optimize the delivery of high-quality software. DevOps increases visibility across teams and prioritizes collaboration and input from all stakeholders throughout the software development lifecycle.It also uses automation to test, monitor and deploy new products and updates. DevOps engineers take an iterative approach, meaning software is continuously tested and optimized to improve performance.Rapid application development (RAD)This process is a type of agile development that places less emphasis on the planning stage and focus on an adaptive process influenced by specific development conditions. RAD prioritizes receiving real-world user feedback and making updates to software after deployment rather than trying to plan for all possible scenarios.SpiralA spiral model combines elements of both waterfall and iterative approaches. Like the waterfall model, a spiral development model delineates a clear series of steps. But it also breaks down the process into a series of loops or “phases” that give development teams more flexibility to analyze, test and modify software throughout the process.The visual representation of these models takes the form of a spiral, with the beginning planning and requirements gathering step as the center point. Each loop or phase represents the entire software delivery cycle. At the start of each new phase, teams can modify requirements, review testing and adjust any code as needed. The spiral model offers risk-management benefits and is ideal for large, complex projects.LeanA type of agile development, lean development takes principles and practices from the manufacturing world and applies them to software development. The goal of lean development is to reduce waste at every step of the SDLC. To do this, lean models set a high standard for quality assurance at every step of development, prioritize faster feedback loops, remove bureaucratic processes for decision making and delay the implementation of decisions until accurate data is available.While traditional agile development is largely focused on the optimization of software, lean development is also concerned with the optimization of development processes to achieve this goal.Big bangUnlike all other development models, big band development does not begin with a robust planning phase. It is based on time, effort and resources—meaning work begins when the time, personnel and funding are available. Developers create software by incorporating requirements as they filter in throughout the process.Big bang development can be a quick process, but due to the limited planning phase, it risks the creation of software that does not meet user needs. Because of this, the big bang model is best suited for small projects that can be updated quickly.Types of software developmentUsing software development to differentiate from competition and gain competitive advantage requires proficiency with the techniques and technologies that can accelerate software deployment, quality and efficacy.There are different types of software development, geared toward different parts of the tech stack or different deployment environments. These types include:Cloud-native developmentCloud-native development is an approach to building and deploying applications in cloud environments. A cloud-native application consists of discrete, reusable components known as microservices. These microservices act as building blocks used to compile larger applications and are often packaged in containers.Cloud-native development and practices like DevOps and continuous integration work together because of a shared emphasis on agility and scalability. Cloud-native applications enable organizations to take advantage of cloud computing benefits such as automated provisioning through infrastructure as code (IaC) and more efficient resource use.Low-code developmentLow-code is a visual approach to software development that enables faster delivery of applications through minimal hand-coding. Low-code software development platforms offer visual features that enable users with limited technical experience to create applications and make a contribution to software development.Experienced developers also benefit from low-code development by using built-in application programming interfaces (APIs) and prebuilt code components. These tools promote faster software development and can eliminate some of the bottlenecks that occur, such as when project managers or business analysts with minimal coding experience are involved in the development process.Front-end developmentFront-end development is the development of the user-facing aspect of software. It includes designing layouts and interactive elements and plays a large role in the user experience. Poor front-end development resulting in a frustrating user experience can doom software, even if it’s technically functional.Back-end development Back-end development is concerned with the aspects that the user doesn’t see, such as building the server-side logic and infrastructure that software needs to function. Back-end developers write the code that determines how software accesses, manages and manipulates data; defines and maintains databases to make sure they work with the front end; sets up and manage APIs and more.Full-stack developmentA full-stack developer is involved in both front and back-end development and is responsible for the entire development process. Full-stack development can be a useful in bridging any divide between the technical aspects of running and maintaining software and the user experience, creating a more holistic approach to development.AI and software developmentArtificial intelligence (AI) tools play an increasingly important role in software development. AI is used to generate new code, review and test existing code and applications, help teams continuously deploy new features and more. AI solutions are not a subsitute for human development teams. Rather, these tools are used to enhance the development process, creating more productive teams and stronger software.Code generationGenerative AI can create code snippets and full functions based on natural language prompts or code context. Using large language model (LLM) technologies, natural language processing (NLP) and deep learning algorithms, technical professionals train generative AI models on massive datasets of existing source code. Through this training, AI models begin to develop a set of parameters—an understanding of coding language, patterns in data and the relationship between different pieces of code. An AI-powered code generator can help developers in several ways, including:AutocompletionWhen a developer is writing code, generative AI tools can analyze the written code and its context and suggest the next line of code. If appropriate, the developer can accept this suggestion. The most obvious benefit is that this helps save the developer some time. This can also be a useful tool for developers working in coding languages they are not the most experienced in or haven’t worked with in a while.Writing original codeDevelopers can directly prompt AI tools with specific plain language prompts. These prompts include specifications such as programming language, syntax and what the developer wants the code to do. Generative AI tools can then produce a snippet of code or an entire function; developers then review the code making edits when needed. These corrections help to further train the model.Translating code and application modernizationGenerative AI tools can translate code from one programming language to another, saving developers time and reducing the risk of manual errors. This is helpful when modernizing applications, for example, translating COBOL to Java.AI-powered code generation can also help automate the repetitive coding involved when migrating traditional infrastructure or software to the cloud.TestingDevelopers can prompt generative AI tools to build and perform tests on existing pieces of code. AI tools can create tests that cover more scenarios more quickly than human developers. AI-powered monitoring tools can also provide a real-time understanding of software performance and predict future errors.Also, through their ability to analyze large datasets, AI tools can uncover patterns and anomalies in data which can be used to find potential issues. When AI tools uncover issues, whether through testing or monitoring, they can automate the remediation of errors and bugs. AI helps developers proactively address issues with code and performance and maintain the smooth operation of software.DeploymentGenerative AI helps DevOps teams optimize the continuous integration/continuous delivery pipeline (CI/CD). The CI/CD pipeline enables frequent merges of code changes into a central repository and accelerates the delivery of regular code updates. CI/CD helps development teams continuously perform quality assurance and maintain code quality and AI is used to improve all aspects of this process.Developers can use AI tools to help manage changes in code made throughout the software development lifecycle and make sure that those changes are implemented correctly. AI tools can be used to continue monitoring software performance after deployment and suggest areas for code improvement. In addition, AI tools help developers deploy new features by seamlessly integrating new code into production environments without disrupting service. They can also automatically update documentation after changes have been made to software.

What is Robotics Engineering?Robotics engineering is a multidisciplinary field including electrical, mechanical, and computer engineering. It deals with designing, building, operating, and engineering robots and robotic systems based on theoretical understanding and practical application.Robotics engineering covers a broad spectrum of tasks composed of conceptualizing designs, developing systems, and crafting operational algorithms. Robotics engineers play a critical role in every step of the lifecycle of robots and robotic systems. Common tasks include evaluating the performance of robotic systems, identifying areas for enhancement, and conducting rigorous testing protocols to ensure compliance with industry standards prior to widespread deployment and utilization.Robotics engineering brings together creativity, technical know-how, and problem-solving skills. It's an exciting field that studies the latest and multidisciplinary engineering technology. Whether it's creating autonomous vehicles and drones, robotic systems that work with humans in manufacturing, or cyber-physical humanoid machines, robotics engineering sets the stage for a better tomorrow where humans and machines work together seamlessly.What Do Robotics Engineers Do?A robotics engineer develops robotic applications across many industries, including automotive, aerospace, manufacturing, defense, agriculture, and healthcare. Robotics engineers work on designing, building, and operating robots and robotic systems.DesigningRobotics engineers conceptualize robots and robotic systems, create blueprints and schematics for robots, and determine their physical structure, components, and functionalities.BuildingRobotic engineers develop robots and robotic systems using a combination of mechanical, electrical, and computer engineering principles and technologies including selection and integration of the necessary components, such as sensors, actuators, motors, and controllers.ProgrammingRobotics engineers write code to control the behavior and motions of robots and robotic systems. Programming languages, such as C++, Python, or specialized robot operating systems (ROS), are used in this task.TestingRobotic engineers run testing to confirm that robots and robotic systems operate correctly and safely as designed, built, and programmed by simulating possible application scenarios, troubleshooting technical issues, and optimizing algorithms.Operating and MaintainingRobotics engineers are also responsible for diagnosing problems, replacing faulty components, and implementing modifications to continuously enhance functionality throughout the lifecycle of robots and robotic systems.What Careers are There in Robotics Engineering?Many different types of robotics engineering are available for you to choose from, with specialties that fit an individual's interests and skills.Robotics engineers work in every sector of industry including automotive, aerospace, manufacturing, defense, agriculture, and healthcare. Some examples include but are not limited to:Aerospace and space technologyAutomationAutomotiveComputer software developmentConsumer electronicsControl systemsCyberneticsGeneral roboticsHealthcareIntelligent systems and manufacturingMedical roboticsRobots and robotic systems are used in various fields, which creates numerous opportunities for robotics engineers.What Skills Do Robotics Engineers Need?Robotics engineers need to have keen interest in working principles of components and systems as they need to be able to design, build, test, and operate robots and robotic systems. It is required that robotics engineers understand electronics, mechanics, control, and software of robotic systems. Additionally, robotics engineers are required to have skills of strong mathematics in design and analysis, computer programming for controlling robotic systems in different environments, and problem solving in operating and troubleshooting robotic systems in real world applications. Creativity is highly valuable, as is the ability to communicate in written and oral forms.How Much Do Robotics Engineers Earn?Robotics engineers are well paid, with above-average earnings in each stage of their careers from an entry-level salary of $81,743 to the top 10 percent making $137,000 (Payscale).

What is application security (AppSec)?Application security refers to the process of identifying and repairing vulnerabilities in application software—from development to deployment—to prevent unauthorized access, modification, or misuse.Application security (AppSec) is an integral part of software engineering and application management. It addresses not only minor bugs but also prevents serious application vulnerabilities from being exploited. An ongoing process rather than a single technology, application security (AppSec) is a crucial component of cybersecurity, encompassing practices that prevent unauthorized access, data breaches and code manipulation of application software. As applications have become more complex, AppSec has become increasingly important and challenging. This evolution necessitates new approaches in secure software development. DevOps and security practices must take place in tandem, supported by professionals with a deep understanding of the software development lifecycle (SDLC).At its core, application security aims to safeguard sensitive data and application code from theft or manipulation. This involves implementing security measures during application development and design phases and maintaining protection during and post-deployment.Ranging from hardware safeguards like routers to software-based defenses such as application firewalls, these measures are supplemented by procedures including regular security testing routines. Additional methods, like thorough code reviews and analysis tools, identify and mitigate vulnerabilities within the codebase. Defensive measures such as strong authentication mechanisms and encryption techniques protect against unauthorized access and cyberattacks. Regular security assessments and penetration testing further ensure proactive vulnerability management.Organizations use various strategies for managing application security depending on their needs. Factors such as cost, expertise, and the specific challenges posed by different environments (e.g., cloud security, mobile app security, and web application security for apps accessed through a browser interface) influence their methods. Some organizations choose to manage application security internally, which enables direct control over processes and tailored security measures by in-house teams.When not managed on-premises, organizations outsource application security—a part of managed security services (MSS)—to a managed security service provider (MSSP). An MSSP can provide a sophisticated security operations center (SOC), security information and event management (SIEM) solutions and access to specialized skills and application security tools. These can benefit organizations that lack internal resources and expertise. Whether managed internally or outsourced, strong security measures are essential to safeguard applications against evolving cyber threats and vulnerabilitiesWhy is application security important?Application security is important for any organization handling customer data, as data breaches pose significant risks. Implementing a strong application security program is crucial to mitigating these application security risks and reducing the attack surface. Developers strive to minimize software vulnerabilities to deter attackers targeting valuable data—whether it's customer information, proprietary secrets or confidential employee data—for nefarious purposes.In today's cloud-based landscape, data spans various networks and connects to remote servers. Network monitoring and security is vital, but safeguarding individual applications is equally important. Hackers increasingly target applications, making application security testing and proactive measures indispensable for protection. A proactive approach to application security offers an edge by enabling organizations to address vulnerabilities before they impact operations or customers.Neglecting application security can have serious consequences. Security breaches are prevalent and can lead to temporary or permanent business shutdowns. Customers entrust organizations with their sensitive information, expecting it to be kept safe and private. Failure to secure applications can result in identity theft, financial loss, and other privacy violations. These failures undermine customer trust and damage the organization’s reputation. Investing in the right application security solutions is essential to protect both organizations and their customers from potential harm.Types of application securityApplication security encompasses various features aimed at protecting applications from potential threats and vulnerabilities. These include:Authentication: Implemented by developers to verify the identity of users accessing the application. Authentication ensures that only authorized individuals gain entry, sometimes requiring multifactor authentication, a combination of factors like passwords, biometrics or physical tokens.Authorization: Following authentication, users are granted permission to access specific functionalities based on their validated identity (identity access management). Authorization verifies user privileges against a predefined list of authorized users, ensuring access control.Encryption: Applied to safeguard sensitive data during transmission or storage within the application. Particularly crucial in cloud-based environments, encryption obscures data, preventing unauthorized access or interception.Logging: Vital for tracking application activity and identifying security breaches, application log files chronicle user interactions. Logging provides a timestamped record of accessed features and user identities, which is helpful for post-incident analysis.Testing: Essential to validate the effectiveness of security measures. Through various testing methods such as static code analysis and dynamic scanning, vulnerabilities are identified and addressed to ensure strong security controls.Application security benefitsApplication security offers numerous benefits to organizations, including:Decreased disruption: Business operations can be disrupted by security issues. Ensuring application security minimizes the risk of service interruptions that lead to costly downtime.Early awareness of issues: Strong application security identifies common attack vectors and risks during the app development phase, enabling resolution before the app is launched. After deployment, the application security solution can identify vulnerabilities and alert administrators to potential issues.Enhanced customer confidence: Applications with a reputation for security and trustworthiness help increase customer confidence in the brand, which can improve brand loyalty.Improved compliance: Application security measures help organizations comply with regulatory and compliance requirements related to data security, such as GDPR, HIPAA and PCI DSS. This helps the organization avoid compliance-related penalties, fines and legal issues.Increased cost savings: Investing in application security in the development process can lead to long-term cost savings. Fixing security issues early in this phase is usually more cost-effective than addressing them after deployment. In addition, strong app security helps avoid the financial costs associated with data breaches, including investigations, legal fees and regulatory fines.Prevention of cyberattacks: Applications are frequent targets for cyberattacks including malware and ransomware, SQL injections and cross-site scripting attacks. Application security measures help organizations prevent these attacks or minimize their impact.Protection of sensitive data: Robust security measures help organizations maintain confidentiality and integrity by safeguarding sensitive data such as customer information, financial records and intellectual property from unauthorized access, modification, or theft.Reduced risks: Eliminating vulnerabilities increases the potential to ward off attacks. Proactive application security measures such as code reviews, security testing, and patch management reduce the likelihood of security incidents and minimize the impact of potential breaches.Support of brand image: A security breach can erode customer trust in an organization. By prioritizing application security, organizations demonstrate their commitment to maintaining trust and protecting customer data, which helps retain customers and attract new ones.The application security processThe application security process involves a series of essential steps aimed at identifying, mitigating and preventing security vulnerabilities.Risk assessment and planningThis initial phase involves identifying potential security risks specific to the application through thorough threat modeling. It includes assessing the application's functionality, data handling processes and potential attack vectors. Based on this assessment, a security plan is developed to outline measures needed to mitigate identified risks.Secure design and developmentDuring the design and development phase, security considerations are integrated into the application architecture and coding practices. Development teams follow secure coding guidelines and application security best practices to minimize the introduction of vulnerabilities into the codebase. This includes implementing input validation, authentication mechanisms, proper error handling and establishing secure deployment pipelines.Code review and testingComprehensive code reviews and testing are conducted to identify and address security vulnerabilities in the application code. This involves both static code analysis to identify potential flaws in the source code and dynamic testing to simulate real-world attack scenarios and assess the application's resilience to exploitation.Security testing and evaluationSecurity testing is performed to assess the effectiveness of implemented security controls and identify any remaining vulnerabilities. This happens primarily through red teaming, with capabilities like penetration testing , vulnerability scanning, and security risk assessments. This testing identifies weaknesses in the application’s defenses and ensures compliance with security standards and regulations.Deployment and monitoringOnce the application is ready for deployment, ongoing monitoring and maintenance are necessary to ensure continued security. This includes implementing logging and monitoring mechanisms to quickly detect and respond to security incidents. Regular security updates and patches are also applied to address newly discovered vulnerabilities and mitigate emerging threats.Application security testing (AST) and toolsDevelopers perform application security testing (AST) as part of the software development process to ensure there are no vulnerabilities in a new or updated version of a software application. Some of the tests and tools related to application security are:Static application security testing (SAST): This AST uses solutions that analyze application source code without executing the program. SAST can identify potential security vulnerabilities, coding errors and weaknesses in the application's codebase early in the development lifecycle. Developers can then fix these issues before deployment.Dynamic application security testing (DAST): Unlike SAST, DAST tools evaluate applications while they are running. They provide insights into the security posture of applications in production environments, simulating real-world attack scenarios to identify vulnerabilities such as input validation errors, authentication flaws and configuration weaknesses that attackers could exploit.Interactive application security testing (IAST): IAST combines SAST and DAST and improves them by focusing on dynamic and interactive testing, inspecting the application using actual user inputs and actions in a controlled and supervised environment. Vulnerabilities are reported in real time.OWASP top ten: The OWASP top ten is a list of the top ten most critical security risks facing web applications. Compiled by the Open Web Applications Security Project (OWASP), an international nonprofit organization focused on improving software security, the list provides periodically updated guidance to developers, security professionals and organizations on the most prevalent and impactful vulnerabilities that can lead to security breaches.Runtime application self-protection (RASP): RASP solutions protect applications at runtime by monitoring and observing behavior for signs of suspicious or malicious activity. They can detect and respond to attacks in real time, and some forms of RASP can block malicious actions when they are detected.Software composition analysis (SCA): SCA tools identify and manage open-source components and third-party libraries used in an application. They analyze dependencies and assess their security posture, including known vulnerabilities and licensing and compliance issues.Secure development lifecycle (SDL) tools: SDL tools integrate security into the development process. They provide developers with guidelines and automated checks to ensure security considerations are addressed throughout the software development lifecycle (SDLC).Web application firewalls (WAFs): WAFs are designed to protect web applications and their APIs by filtering and monitoring HTTP traffic between a web application and the internet at the application layer. They can detect and block common web-based attacks such as SQL injection, cross-site scripting (XSS) and cross-site request forgery (CSRF). This enables risk mitigation of data breaches and unauthorized access.These tools and technologies, along with others such as encryption, authentication mechanisms and security testing frameworks, are important for protecting applications from a wide range of security threats and vulnerabilities. Organizations often employ a combination of these tests and tools as part of their application security strategy.

What is Cyber Essentials?Cyber Essentials is a UK government scheme designed to protect companies and organisations, whatever their size, against a range of the most common cyber attacks. Most of these attacks are basic and carried out by relatively unskilled people. They have been described as the digital equivalent of a thief trying a home’s front door to see if it is unlocked. The certification scheme was launched in 2014 by the UK Department for Business, Innovation and Skills and is operated by the National Cyber Security Centre (NCSC).How can Cyber Essentials benefit your business?The scheme can benefit your business in a number of ways:1. Preventing cyber attacks:  If you fail to protect your computer systems, you’re at more risk of a cyber attack. An attack could result in your organisation losing vital data, disrupting cash flow and damaging your reputation.2. Government contracts:  Organisations bidding for some contracts with the British Government will need Cyber Essentials certification.3. Customer trust:  Becoming certified shows your customers that you take cyber security seriously and are taking the necessary steps to keep the data you hold about them safe. Displaying your credentials on your website, emails and other marketing materials shows your customers – and perspective ones – that you’re serious about cyber security.The five controls of Cyber EssentialsThere are five technical controls (a “control” is simply a way to address a risk) you will need to put in place, which are:Firewalls: Secure your internet connection with boundary and host-based firewalls.Secure Configuration: Settings, passwords and multi-factor authentication.Security Update Management: Keep your devices and software up to date.User Access Control: Protecting administrators and limiting access to data and services.Malware Protection: Viruses, allow-listing and associated techniques.Guidance from the UK National Cyber Security Centre breaks these down into finer details. These controls have been chosen as the highest priority ones from other, more detailed guidance such as the ISO27001 standard for information security, the Standard of Good Practice (from the Information Security Forum) and the IASME Cyber Assurance standard. Although, Cyber Essentials has a narrower focus, emphasising technical controls rather than more general governance and risk assessment.Cyber Essentials and the GDPRCyber Essentials is also useful for those with an eye on the GDPR – the EU’s General Data Protection Regulation – which came into effect in May 2018. The GDPR is a far-reaching regulation, intended to protect the privacy of individuals and their personal data within the European Union. The regulation specifies that “controllers” must determine their own cyber security approaches based on the personal information they hold and process. Since Brexit, the UK now has its own data protection regime, heavily based on the GDPR.While Cyber Essentials can help with this, it is not a complete solution for all GDPR obligations. But the Information Commissioner’s Office (ICO), whose job it is to uphold data protection law in the UK, recommends Cyber Essentials as “a good starting point” for the cyber security of the IT systems and networks you rely on to hold and process personal data.Standard or Plus Certification?Not everyone has the time or money needed to develop a comprehensive cyber security system, so the scheme has been designed to fit in with whatever level of commitment you are able to sustain. There are three main levels of engagement:The simplest is to familiarise yourself with cyber security terminology, gaining enough knowledge to begin securing your IT systems, without becoming certified.If you need more certainty in your cyber security (or you want to show others that you’re taking it seriously), you can apply for basic certification.For those who want to take cyber security a bit further, Cyber Essentials Plus certification is also available. The five controls are the same as for the basic level, but Plus also includes a more detailed vulnerability scan from inside your network (tested onsite), to check your devices are configured correctly.The self-assessment option (not going for certification) still gives you protection against a wide variety of the most common cyber attacks, so we’d encourage you to do this as a minimum. This is important because vulnerability to simple attacks can mark you out as a target for more in-depth unwanted attention from cyber criminals and others.Certification gives you increased peace of mind that your defences will protect against the majority of common cyber attacks simply because these attacks are looking for “soft” targets which do not have the technical controls in place. If you would like to bid for central government contracts which involve handling sensitive and personal information, or the provision of certain technical products and services, you may need to have certification, at either the basic or Plus level.Cost of becoming certifiedThe process of obtaining basic certification is relatively simple and budget friendly, depending on the size of your organisation. The scheme shows you how to address the basics and prevent the most common attacks. So far about 80% of companies and organisations with Cyber Essentials certification have chosen the basic version. It is often larger organisations that choose Cyber Essentials Plus due to the additional cost, which can be several thousand pounds

What is cloud computing?Cloud computing is on-demand access to computing resources—physical or virtual servers, data storage, networking capabilities, application development tools, software, AI-powered analytic platforms and more—over the internet with pay-per-use pricing.In simpler terms, the "cloud" doesn't refer to something floating in the sky. Instead, when you use cloud services, you're accessing remote servers, powerful mainframe computers housed in large data centers, through the internet. The cloud computing model gives you, the customer, greater flexibility and scalability compared to traditional on-premises infrastructure.Cloud computing is pivotal in our everyday lives, whether that means to access a cloud application such as Google Gmail, stream a movie on Netflix or play a cloud-hosted video game. With cloud computing, you get the computing power or storage you need, without having to own or manage the physical hardware yourself.Cloud computing has also become indispensable in business settings, from small startups to global enterprises, as it offers greater flexibility and scalability than traditional on-premises infrastructure. Its many business applications include enabling remote work by making data and applications accessible from anywhere, creating the framework for seamless omnichannel customer engagement and providing the vast computing power and other resources needed to take advantage of cutting-edge technologies such as generative AI and quantum computing.Benefits of cloud computingCompared to traditional on-premises IT, where a company owns and maintains physical data centers and servers to access computing power, data storage and other resources, cloud computing offers many benefits, including:Cost-effectivenessIncreased speed and agilityUnlimited scalabilityEnhanced strategic valueCost-effectivenessCloud computing lets you offload some or all of the expense and effort of purchasing, installing, configuring and managing mainframe computers and other on-premises infrastructure. You only pay for cloud-based infrastructure and other computing resources as you use them.Increased speed and agilityWith cloud technologies, your organization can use enterprise applications in minutes instead of waiting weeks or months for IT to respond to a request, purchase and configure supporting hardware and install software. This feature empowers users—specifically DevOps and other development teams—to help use cloud-based software and support infrastructure.Unlimited scalabilityCloud computing provides elasticity and self-service provisioning, so instead of purchasing excess capacity that sits unused during slow periods, you can scale capacity up and down in response to spikes and dips in traffic. You can also use your cloud provider’s global network to spread your applications closer to users worldwide.Enhanced strategic valueCloud computing enables organizations to use various technologies and the most up-to-date innovations to gain a competitive edge. For instance, in retail, banking and other customer-facing industries, generative AI-powered virtual agents deployed over the cloud can deliver better customer response time and free up teams to focus on higher-level work. In manufacturing, teams can collaborate and use cloud-based software to monitor real-time data across logistics and supply chain processes.Origins of cloud computingThe origins of cloud computing technology go back to the early 1960s when Dr. Joseph Carl Robnett Licklider, an American computer scientist and psychologist known as the “father of cloud computing,” introduced the earliest ideas of global networking in a series of memos discussing an Intergalactic Computer Network.However, it wasn’t until the early 2000s that modern cloud infrastructure for business emerged. In 2002, Amazon Web Services started cloud-based storage and computing services. In 2006, it introduced Elastic Compute Cloud (EC2), an offering that allowed users to rent virtual computers to run their applications. That same year, Google introduced the Google Apps suite (now called Google Workspace), a collection of SaaS productivity applications.In 2009, Microsoft started its first SaaS application, Microsoft Office 2011.By 2028, Gartner predicts cloud shifts from being an industry disruptor to becoming a business necessity and an integral part of business operations.1Cloud computing componentsThe following are a few of the most integral components of today’s modern cloud architecture:Data centersNetworking capabilitiesVirtualizationData centersCSPs own and operate remote data centers that house physical or bare metal servers, cloud storage systems and other physical hardware that create the underlying infrastructure and provide the physical foundation for cloud computing.Networking capabilitiesIn cloud computing, high-speed networking connections are crucial. Typically, an internet connection known as a wide-area network (WAN) connects front-end users (client-side interface made visible through web-enabled devices) with back-end functions (data centers and cloud-based applications and services).Other advanced cloud computing networking technologies, including load balancers, content delivery networks (CDNs) and software-defined networking (SDN), are also incorporated to help ensure data flows quickly, easily and securely between front-end users and back-end resources.VirtualizationCloud computing relies heavily on the virtualization of IT infrastructure (servers, operating system software, networking) that’s abstracted by using special software so that it can be pooled and divided irrespective of physical hardware boundaries.For example, a single hardware server can be divided into multiple virtual servers. Virtualization enables cloud providers to make maximum use of their data center resources.Cloud computing servicesInfrastructure-as-a-service (IaaS), platform-as-a-service (PaaS), software-as-a-service (SaaS) and serverless computing are the most common “as-as-service” cloud platform models. Most developers at large-scale organizations use some combination of all four.IaaS offers full control over IT infrastructure, allowing organizations to build and manage systems. PaaS builds on IaaS by providing a platform that simplifies the development and deployment of applications, handling the underlying infrastructure for you. SaaS, the most widely used cloud service, delivers ready-to-use software, removing the need for management. And serverless computing, built on IaaS and PaaS, lets you focus solely on writing code.IaaS (Infrastructure-as-a-Service)Infrastructure as a service (IaaS) provides on-demand access to fundamental computing resources—physical and virtual servers, networking and storage—over the internet on a pay-as-you-go basis.IaaS enables users to scale and shrink resources on an as-needed basis, reducing the need for high up-front capital expenditures or unnecessary on-premises or “owned” infrastructure and for overbuying resources to accommodate periodic spikes in usage.According to a report from the Business Research Company, the IaaS market is predicted to grow rapidly in the next few years, growing to USD 212.34 billion in 2028 at a compound annual growth rate (CAGR) of 14.2%.2PaaS (Platform-as-a-Service)Platform as a service (PaaS) provides software developers with an on-demand platform—hardware, complete software stack, infrastructure and development tools—for running, developing and managing applications without the cost, complexity and inflexibility of maintaining that platform on-premises.With PaaS, the cloud provider hosts everything at their data center. These include servers, networks, storage, operating system software, middleware and databases. Developers simply pick from a menu to spin up servers and environments they need to run, build, test, deploy, maintain, update and scale applications.Today, PaaS is typically built around containers, a virtualized compute model one step removed from virtual servers. Containers virtualize the operating system, enabling developers to package the application with only the operating system services it needs to run on any platform without modification and the need for middleware.Red Hat® OpenShift® is a popular PaaS built around Docker containers and Kubernetes, an open source container orchestration solution that automates cloud deployment, scaling, load balancing and more for container-based applications.SaaS (Software-as-a-Service)Software as a service (SaaS), also known as cloud-based software or cloud applications, is interactive application software hosted in the cloud. Users access SaaS through a web browser, a dedicated desktop client or an application programming interface (API) that integrates with a desktop or mobile operating system. Cloud service providers offer SaaS based on a monthly or annual subscription fee. They can also provide these services through pay-per-usage pricing.In addition to the cost savings, time-to-value and scalability benefits of the cloud, SaaS offers the following:Automatic upgrades: With SaaS, users have access to new features when the cloud service provider adds them without having to orchestrate an on-premises upgrade.Protection from data loss: Because SaaS stores application data in the cloud with the application, users don’t lose data if their device crashes or breaks.SaaS is the primary delivery model for most commercial software today. Hundreds of SaaS solutions exist, from focused industry and broad administrative (for example, Salesforce) to robust enterprise database and artificial intelligence (AI)-driven software tools.According to a study from Fortune Business Insights, the global software as a service (SaaS) market size was valued at USD 273.55 billion in 2023 and is projected to grow from USD 317.55 billion in 2024 to USD 1,228.87 billion by 2032.3Serverless computingServerless computing, or simply serverless, is a cloud computing model that offloads all the back-end infrastructure management tasks, including provisioning, scaling, scheduling and patching, to the cloud provider. This capability frees developers to focus all their time and effort on the code and business logic specific to their applications.Moreover, serverless runs application code on a per-request basis only and automatically scales the supporting infrastructure up and down in response to the number of requests. With serverless, customers pay only for the resources used when the application runs; they never pay for idle capacity.Function as a service (FaaS) is often confused with serverless computing when, in fact, it’s a subset of serverless. FaaS allows developers to run portions of application code (called functions) in response to specific events. Everything besides the code—physical hardware, virtual machine (VM), operating system and web server software management—is provisioned automatically by the cloud service provider in real-time as the code runs and is spun back down once the execution is complete. Billing starts when execution starts and stops when execution stops.Types of cloud computingPublic cloudA public cloud is a type of cloud computing in which a cloud service provider makes computing resources available to users over the public internet. These include SaaS applications, individual virtual machines (VMs), bare metal computing hardware, complete enterprise-grade infrastructures, and development platforms. These resources might be accessible for free or according to subscription-based or pay-per-usage pricing models.The public cloud provider owns, manages and assumes all responsibility for the data centers, hardware and infrastructure on which its customers’ workloads run. It typically provides high-bandwidth network connectivity to help ensure high performance and rapid access to applications and data.Public cloud is a multi-tenant environment where all customers pool and share the cloud provider’s data center infrastructure and other resources. In the world of the leading public cloud vendors, such as Amazon Web Services (AWS), Google Cloud, IBM Cloud®, Microsoft Azure and Oracle Cloud, these customers can number in the millions.Most enterprises have moved portions of their computing infrastructure to the public cloud since public cloud services are elastic and readily scalable, flexibly adjusting to meet changing workload demands. The promise of greater efficiency and cost savings through paying only for what they use attracts customers to the public cloud. Others seek to reduce spending on hardware and on-premises infrastructure.Private cloudA private cloud is a cloud environment where all cloud infrastructure and computing resources are dedicated to one customer only. Private cloud combines many benefits of cloud computing—including elasticity, scalability and ease of service delivery—with the access control, security and resource customization of on-premises infrastructure.A private cloud is typically hosted on-premises in the customer’s data center. However, it can also be hosted on an independent cloud provider’s infrastructure or built on rented infrastructure housed in an offsite data center.Many companies choose a private cloud over a public cloud environment to meet regulatory compliance requirements. Large-scale entities such as government agencies, healthcare organizations and financial institutions often opt for private cloud settings for workloads that deal with confidential documents, personally identifiable information (PII), intellectual property, medical records, financial data or other sensitive data.By building private cloud architecture according to cloud-native principles, organizations can quickly move workloads to a public cloud or run them within a hybrid cloud (see below) environment whenever ready.Hybrid cloudA hybrid cloud is just what it sounds like: a combination of public cloud, private cloud and on-premises environments. Specifically (and ideally), a hybrid cloud connects a combination of these three environments into a single, flexible infrastructure for running the organization’s applications and workloads.At first, organizations turned to hybrid cloud computing models primarily to migrate portions of their on-premises data into private cloud infrastructure and then connect that infrastructure to public cloud infrastructure hosted off-premises by cloud vendors. This process was done through a packaged hybrid cloud solution such as Red Hat OpenShift or middleware and IT management tools to create a “single pane of glass.” Teams and administrators rely on this unified dashboard to view their applications, networks and systems.Today, hybrid cloud architecture has expanded beyond physical connectivity and cloud migration to offer a flexible, secure and cost-effective environment that supports the portability and automated deployment of workloads across multiple environments. This feature enables an organization to meet its technical and business objectives more effectively and cost-efficiently than with a public or private cloud alone. For instance, a hybrid cloud environment is ideal for DevOps and other teams to develop and test web applications. This frees organizations from purchasing and expanding the on-premises physical hardware needed to run application testing, offering faster time to market. Once a team has developed an application in the public cloud, they can move it to a private cloud environment based on business needs or security factors.A public cloud also allows companies to quickly scale resources in response to unplanned spikes in traffic without impacting private cloud workloads, a feature known as cloud bursting. Streaming channels such as Amazon use cloud bursting to support the increased viewership traffic when they start new shows.MulticloudMulticloud uses two or more clouds from two or more different cloud providers. A multicloud environment can be as simple as email SaaS from one vendor and image editing SaaS from another. But when enterprises talk about multicloud, they typically use multiple cloud services—including SaaS, PaaS and IaaS—from two or more leading public cloud providers.Organizations choose multicloud to avoid vendor lock-in, have more services to select from and access more innovation. With multicloud, organizations can choose and customize a unique set of cloud features and services to meet their business needs. This freedom of choice includes selecting “best-of-breed” technologies from any CSP (as needed or as they emerge), rather than being locked into offering from a single vendor. For example, an organization can choose AWS for its global reach with web hosting, IBM Cloud for data analytics and machine learning (ML) platforms and Microsoft Azure for its security features.A multicloud environment also reduces exposure to licensing, security and compatibility issues resulting from "shadow IT"— any software, hardware or IT resource used on an enterprise network without the IT department’s approval and often without IT’s knowledge or oversight.The modern hybrid multicloudToday, most enterprise organizations use a hybrid multicloud model. Besides the flexibility to choose the most cost-effective cloud service, hybrid multicloud offers the most control over workload deployment, enabling organizations to operate more efficiently, improve performance and optimize costs.According to an IBM Institute for Business Value study, the value derived from a full hybrid multicloud platform technology and operating model at scale is two-and-a-half times the value derived from a single-platform, single-cloud vendor approach.Yet the modern hybrid multicloud model comes with more complexity. The more clouds that you use—each with its own management tools, data transmission rates and security protocols—the more difficult it can be to manage your environment. With over 97% of enterprises operating on more than one cloud and most organizations running 10 or more clouds, a hybrid cloud management approach has become crucial.Hybrid multicloud management platforms provide visibility across multiple provider clouds through a central dashboard where development teams can see their projects and deployments, operations teams can monitor clusters and nodes, and the cybersecurity staff can monitor for threats.Cloud securityTraditionally, security concerns have been the primary obstacle for organizations considering cloud services, mainly public cloud services. Maintaining cloud security demands different procedures and employee skill sets than legacy IT environments. Some cloud security best practices include the following:Shared responsibility for security: Generally, the cloud service provider is responsible for securing cloud infrastructure, and the customer is responsible for protecting its data within the cloud. However, it’s also essential to clearly define data ownership between private and public third parties.Data encryption: Data should be encrypted while at rest, in transit and in use. Customers need to maintain complete control over security keys and hardware security modules.Collaborative management: Proper communication and clear, understandable processes between IT, operations and security teams help ensure seamless cloud integrations that are secure and sustainable.Security and compliance monitoring: IT, operations and security teams must understand all regulatory compliance standards applicable to their industry and establish active monitoring of all connected systems and cloud-based services to maintain visibility of all data exchanges across all environments—on-premises, private cloud, hybrid cloud and at the edge.Cloud security management toolsCloud security is constantly changing to keep pace with new threats. Today’s CSPs offer a wide array of cloud security management tools, including:Identity and access management (IAM): IAM tools and services automate policy-driven enforcement protocols for all users attempting to access both on-premises and cloud-based services.Data loss and prevention (DLP): DLP services combine remediation alerts, data encryption and other preventive measures to protect all stored data, whether at rest or in motion.Security information and event management (SIEM): SIEM is a comprehensive security orchestration solution that automates threat monitoring, detection and response in cloud-based environments. SIEM technology uses artificial intelligence (AI)-driven technologies to correlate log data from various sources (for example, network devices, firewalls) across multiple platforms and digital assets. This allows IT teams to successfully apply their network security protocols, enabling them to react to potential threats quickly.Automated data and compliance platforms: Automated software solutions provide compliance controls and centralized data collection to help organizations adhere to regulations specific to their industry. Regular compliance updates can be baked into these platforms so organizations can adapt to ever-changing regulatory compliance standards.Cloud sustainabilitySustainability in business refers to a company’s strategy to reduce negative environmental impact from their operations in a particular market, and it has become an essential corporate governance mandate. Gartner predicts that 50% of organizations will adopt sustainability-enabled monitoring by 2026 to manage energy consumption and carbon footprint metrics for their hybrid cloud environments.4As companies strive to advance their business sustainability objectives, cloud computing has evolved to play a significant role in helping them reduce their carbon emissions and manage climate-related risks. For instance, traditional data centers require power supplies and cooling systems, which depend on large amounts of electrical power. By migrating IT resources and applications to the cloud, organizations only enhance operational and cost efficiencies and boost overall energy efficiency through pooled CSP resources.All major cloud players have made net-zero commitments to reduce their carbon footprints and help clients reduce the energy they typically consume using an on-premises setup. For instance, IBM is driven by sustainable procurement initiatives to reach NetZero by 2030.Cloud use casesAccording to an International Data Corporation (IDC) forecast, worldwide spending on public cloud services is expected to double by 2028.5 Here are some of the main ways businesses can benefit from cloud computing:Migrate existing applications to the cloudScale infrastructureEnable business continuity and disaster recoveryBuild and test cloud-native applicationsSupport edge and IoT environmentsUse cutting-edge technologiesScale infrastructureOrganizations can allocate resources up or down quickly and easily in response to changes in business demands.Enable business continuity and disaster recoveryCloud computing provides cost-effective redundancy to protect data against system failures and provide the physical distance required to apply disaster recovery strategies and recover cloud data and applications during a local outage or disaster. All of the major public cloud providers offer disaster recovery as a service (DRaaS).Build and test cloud-native applicationsFor development teams adopting agile, DevOps or DevSecOps, the cloud offers on-demand, scalable resources that streamline the provisioning of development and testing environments, eliminating bottlenecks such as manually setting up servers and enabling teams to focus on building and testing cloud-native applications and their dependencies more efficiently.Support edge and IoT environmentsThe cloud can address latency challenges and reduce downtime by bringing data sources closer to the edge. It supports Internet of Things (IoT) devices (for example, patient monitoring devices, sensors on a production line) to gather real-time data.

Cyber Attacks in Nigeria in 2026In early 2026, Nigeria has emerged as the most targeted country for cyber attacks in Africa. Organisations in the country faced an average of 4,701 cyber attacks per week in January 2026, a 12% increase from the previous year.The most common and impactful cyber attacks currently affecting Nigeria in 2026 include:1. AI-Powered Phishing and Social Engineering Cybercriminals are increasingly using Generative AI to automate and scale deception. Highly Personalized Scams: Attackers use AI to create convincing emails, fake voice calls (deepfakes), and tailored malware with minimal effort.Deepfakes: Audio and video deepfakes are now regularly used to impersonate senior executives or regulators to authorize fraudulent transfers.Prevalence: Phishing remains the primary entry point for over 90% of data breaches in the country. 2. Ransomware as a "Leverage" ToolRansomware has evolved from simple file-locking to a tool for operational disruption and data extortion. Target Sectors: Banking, healthcare, and education are the most frequent victims.Shift in Strategy: Instead of just encrypting files, attackers now focus on stealing sensitive data to use as leverage for months, even if a ransom is not initially paid.Impact: Ransomware assaults saw a massive 287% increase in frequency leading into 2026. 3. Business Email Compromise (BEC)BEC remains one of the most financially damaging threats to Nigerian businesses. Credential Loss: Credential theft is now the No. 1 effect of phishing, allowing attackers to hijack legitimate business accounts.Method: Attackers monitor internal communications for weeks before sending a perfectly timed, fraudulent invoice or wire transfer request. Attack Surfaces and TechniquesRansomware as a Service (RaaS): Ransomware has evolved into "cyber kidnapping," where hackers break into systems, lock them entirely, and steal data for extortion. Ransom demands often exceed 500 million naira.Data Breaches & Email Compromise: Over 281,000 Nigerian email accounts were breached between January and March 2026, an 18% increase from late 2025. High-profile incidents included alleged breaches at the Corporate Affairs Commission (CAC), Remita, and Sterling Bank.Third-Party & Supply Chain Attacks: Increased interconnectedness in the financial technology sector (payment apps, online banking) allows attackers to exploit weak links in third-party services.Man-in-the-Middle (MITM) Attacks: Hackers are increasingly intercepting financial transactions, particularly in online banking platforms.Insider Threats: A significant percentage of attacks are originating from within organizations, highlighting the need for "zero trust" security architectures.Online Dating/Romance Scams: Still prevalent, these scams target individuals to steal money and cryptocurrencyRecent incidents have shown a growing focus on the systems that power the economy.Key Targets in 2026:Financial institutions, fintech companies, government portals (e.g., CAC), and academic institutions (e.g., Lagos State University) are heavily targeted to gain access to data like NINs and BVNs, which are then sold on the dark webLearn Cybersecurity at Vsasf Tech ICT Academy Enugu and become a certified Cyber expert. Join our intensive practical classes today to develop new skills in Penetration Testing, Ethical Hacking, Cyber Threat Analysis, Network Security, Application Security, Cloud Security, Incident Responder, Digital Forensics etc. Register now through this linkFor more information call or WhatsApp 08031936721

What is AI? Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language. They can learn from new information and experience. They can make detailed recommendations to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car).But in 2024, most AI researchers and practitioners—and most AI-related headlines—are focused on breakthroughs in generative AI (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to first understand the technologies on which generative AI tools are built: machine learning (ML) and deep learning.Machine learningA simple way to think about AI is as a series of nested or derivative concepts that have emerged over more than 70 years:Directly underneath AI, we have machine learning, which involves creating models by training an algorithm to make predictions or decisions based on data. It encompasses a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks.There are many types of machine learning techniques or algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines (SVMs), k-nearest neighbor (KNN), clustering and more. Each of these approaches is suited to different kinds of problems and data.But one of the most popular types of machine learning algorithm is called a neural network (or artificial neural network). Neural networks are modeled after the human brain's structure and function. A neural network consists of interconnected layers of nodes (analogous to neurons) that work together to process and analyze complex data. Neural networks are well suited to tasks that involve identifying complex patterns and relationships in large amounts of data.The simplest form of machine learning is called supervised learning, which involves the use of labeled data sets to train algorithms to classify data or predict outcomes accurately. In supervised learning, humans pair each training example with an output label. The goal is for the model to learn the mapping between inputs and outputs in the training data, so it can predict the labels of new, unseen data.Deep learningDeep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers.These multiple layers enable unsupervised learning: they can automate the extraction of features from large, unlabeled and unstructured data sets, and make their own predictions about what the data represents.Because deep learning doesn’t require human intervention, it enables machine learning at a tremendous scale. It is well suited to natural language processing (NLP), computer vision, and other tasks that involve the fast, accurate identification complex patterns and relationships in large amounts of data. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.Deep learning also enables:Semi-supervised learning, which combines supervised and unsupervised learning by using both labeled and unlabeled data to train AI models for classification and regression tasks.Self-supervised learning, which generates implicit labels from unstructured data, rather than relying on labeled data sets for supervisory signals.Reinforcement learning, which learns by trial-and-error and reward functions rather than by extracting information from hidden patterns.Transfer learning, in which knowledge gained through one task or data set is used to improve model performance on another related task or different data set.Generative AIGenerative AI, sometimes called "gen AI", refers to deep learning models that can create complex original content—such as long-form text, high-quality images, realistic video or audio and more—in response to a user’s prompt or request.At a high level, generative models encode a simplified representation of their training data, and then draw from that representation to create new work that’s similar, but not identical, to the original data.Generative models have been used for years in statistics to analyze numerical data. But over the last decade, they evolved to analyze and generate more complex data types. This evolution coincided with the emergence of three sophisticated deep learning model types:Variational autoencoders or VAEs, which were introduced in 2013, and enabled models that could generate multiple variations of content in response to a prompt or instruction.Diffusion models, first seen in 2014, which add "noise" to images until they are unrecognizable, and then remove the noise to generate original images in response to prompts.Transformers (also called transformer models), which are trained on sequenced data to generate extended sequences of content (such as words in sentences, shapes in an image, frames of a video or commands in software code). Transformers are at the core of most of today’s headline-making generative AI tools, including ChatGPT and GPT-4, Copilot, BERT, Bard and Midjourney.How generative AI worksIn general, generative AI operates in three phases:Training, to create a foundation model.Tuning, to adapt the model to a specific application.Generation, evaluation and more tuning, to improve accuracy.TrainingGenerative AI begins with a "foundation model"; a deep learning model that serves as the basis for multiple different types of generative AI applications.The most common foundation models today are large language models (LLMs), created for text generation applications. But there are also foundation models for image, video, sound or music generation, and multimodal foundation models that support several kinds of content.To create a foundation model, practitioners train a deep learning algorithm on huge volumes of relevant raw, unstructured, unlabeled data, such as terabytes or petabytes of data text or images or video from the internet. The training yields a neural network of billions of parameters—encoded representations of the entities, patterns and relationships in the data—that can generate content autonomously in response to prompts. This is the foundation model.This training process is compute-intensive, time-consuming and expensive. It requires thousands of clustered graphics processing units (GPUs) and weeks of processing, all of which typically costs millions of dollars. Open source foundation model projects, such as Meta's Llama-2, enable gen AI developers to avoid this step and its costs.TuningNext, the model must be tuned to a specific content generation task. This can be done in various ways, including:Fine-tuning, which involves feeding the model application-specific labeled data—questions or prompts the application is likely to receive, and corresponding correct answers in the wanted format.Reinforcement learning with human feedback (RLHF), in which human users evaluate the accuracy or relevance of model outputs so that the model can improve itself. This can be as simple as having people type or talk back corrections to a chatbot or virtual assistant.Generation, evaluation and more tuningDevelopers and users regularly assess the outputs of their generative AI apps, and further tune the model—even as often as once a week—for greater accuracy or relevance. In contrast, the foundation model itself is updated much less frequently, perhaps every year or 18 months.Another option for improving a gen AI app's performance is retrieval augmented generation (RAG), a technique for extending the foundation model to use relevant sources outside of the training data to refine the parameters for greater accuracy or relevance.Benefits of AI AI offers numerous benefits across various industries and applications. Some of the most commonly cited benefits include:Automation of repetitive tasks.More and faster insight from data.Enhanced decision-making.Fewer human errors.24x7 availability.Reduced physical risks.Automation of repetitive tasksAI can automate routine, repetitive and often tedious tasks—including digital tasks such as data collection, entering and preprocessing, and physical tasks such as warehouse stock-picking and manufacturing processes. This automation frees to work on higher value, more creative work.Enhanced decision-makingWhether used for decision support or for fully automated decision-making, AI enables faster, more accurate predictions and reliable, data-driven decisions. Combined with automation, AI enables businesses to act on opportunities and respond to crises as they emerge, in real time and without human intervention.Fewer human errorsAI can reduce human errors in various ways, from guiding people through the proper steps of a process, to flagging potential errors before they occur, and fully automating processes without human intervention. This is especially important in industries such as healthcare where, for example, AI-guided surgical robotics enable consistent precision.Machine learning algorithms can continually improve their accuracy and further reduce errors as they're exposed to more data and "learn" from experience.Round-the-clock availability and consistencyAI is always on, available around the clock, and delivers consistent performance every time. Tools such as AI chatbots or virtual assistants can lighten staffing demands for customer service or support. In other applications—such as materials processing or production lines—AI can help maintain consistent work quality and output levels when used to complete repetitive or tedious tasks.Reduced physical riskBy automating dangerous work—such as animal control, handling explosives, performing tasks in deep ocean water, high altitudes or in outer space—AI can eliminate the need to put human workers at risk of injury or worse. While they have yet to be perfected, self-driving cars and other vehicles offer the potential to reduce the risk of injury to passengers.AI use cases The real-world applications of AI are many. Here is just a small sampling of use cases across various industries to illustrate its potential:Customer experience, service and supportCompanies can implement AI-powered chatbots and virtual assistants to handle customer inquiries, support tickets and more. These tools use natural language processing (NLP) and generative AI capabilities to understand and respond to customer questions about order status, product details and return policies.Chatbots and virtual assistants enable always-on support, provide faster answers to frequently asked questions (FAQs), free human agents to focus on higher-level tasks, and give customers faster, more consistent service.Fraud detectionMachine learning and deep learning algorithms can analyze transaction patterns and flag anomalies, such as unusual spending or login locations, that indicate fraudulent transactions. This enables organizations to respond more quickly to potential fraud and limit its impact, giving themselves and customers greater peace of mind.Personalized marketingRetailers, banks and other customer-facing companies can use AI to create personalized customer experiences and marketing campaigns that delight customers, improve sales and prevent churn. Based on data from customer purchase history and behaviors, deep learning algorithms can recommend products and services customers are likely to want, and even generate personalized copy and special offers for individual customers in real time.Human resources and recruitmentAI-driven recruitment platforms can streamline hiring by screening resumes, matching candidates with job descriptions, and even conducting preliminary interviews using video analysis. These and other tools can dramatically reduce the mountain of administrative paperwork associated with fielding a large volume of candidates. It can also reduce response times and time-to-hire, improving the experience for candidates whether they get the job or not.Application development and modernizationGenerative AI code generation tools and automation tools can streamline repetitive coding tasks associated with application development, and accelerate the migration and modernization (reformatting and replatorming) of legacy applications at scale. These tools can speed up tasks, help ensure code consistency and reduce errors.Predictive maintenanceMachine learning models can analyze data from sensors, Internet of Things (IoT) devices and operational technology (OT) to forecast when maintenance will be required and predict equipment failures before they occur. AI-powered preventive maintenance helps prevent downtime and enables you to stay ahead of supply chain issues before they affect the bottom line.AI challenges and risks Organizations are scrambling to take advantage of the latest AI technologies and capitalize on AI's many benefits. This rapid adoption is necessary, but adopting and maintaining AI workflows comes with challenges and risks.Data risksAI systems rely on data sets that might be vulnerable to data poisoning, data tampering, data bias or cyberattacks that can lead to data breaches. Organizations can mitigate these risks by protecting data integrity and implementing security and availability throughout the entire AI lifecycle, from development to training and deployment and postdeployment.Model risksThreat actors can target AI models for theft, reverse engineering or unauthorized manipulation. Attackers might compromise a model’s integrity by tampering with its architecture, weights or parameters; the core components that determine a model’s behavior, accuracy and performance.Operational risksLike all technologies, models are susceptible to operational risks such as model drift, bias and breakdowns in the governance structure. Left unaddressed, these risks can lead to system failures and cybersecurity vulnerabilities that threat actors can use.Ethics and legal risksIf organizations don’t prioritize safety and ethics when developing and deploying AI systems, they risk committing privacy violations and producing biased outcomes. For example, biased training data used for hiring decisions might reinforce gender or racial stereotypes and create AI models that favor certain demographic groups over others.AI ethics and governance AI ethics is a multidisciplinary field that studies how to optimize AI's beneficial impact while reducing risks and adverse outcomes. Principles of AI ethics are applied through a system of AI governance consisted of guardrails that help ensure that AI tools and systems remain safe and ethical.AI governance encompasses oversight mechanisms that address risks. An ethical approach to AI governance requires the involvement of a wide range of stakeholders, including developers, users, policymakers and ethicists, helping to ensure that AI-related systems are developed and used to align with society's values.Here are common values associated with AI ethics and responsible AI:Explainability and interpretabilityAs AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm came to a result. Explainable AI is a set of processes and methods that enables human users to interpret, comprehend and trust the results and output created by algorithms.Fairness and inclusionAlthough machine learning, by its very nature, is a form of statistical discrimination, the discrimination becomes objectionable when it places privileged groups at systematic advantage and certain unprivileged groups at systematic disadvantage, potentially causing varied harms. To encourage fairness, practitioners can try to minimize algorithmic bias across data collection and model design, and to build more diverse and inclusive teams.Robustness and securityRobust AI effectively handles exceptional conditions, such as abnormalities in input or malicious attacks, without causing unintentional harm. It is also built to withstand intentional and unintentional interference by protecting against exposed vulnerabilities.Accountability and transparencyOrganizations should implement clear responsibilities and governance structures for the development, deployment and outcomes of AI systems. In addition, users should be able to see how an AI service works, evaluate its functionality, and comprehend its strengths and limitations. Increased transparency provides information for AI consumers to better understand how the AI model or service was created.Privacy and complianceMany regulatory frameworks, including GDPR, mandate that organizations abide by certain privacy principles when processing personal information. It is crucial to be able to protect AI models that might contain personal information, control what data goes into the model in the first place, and to build adaptable systems that can adjust to changes in regulation and attitudes around AI ethics.Weak AI vs. Strong AI In order to contextualize the use of AI at various levels of complexity and sophistication, researchers have defined several types of AI that refer to its level of sophistication:Weak AI: Also known as “narrow AI,” defines AI systems designed to perform a specific task or a set of tasks. Examples might include “smart” voice assistant apps, such as Amazon’s Alexa, Apple’s Siri, a social media chatbot or the autonomous vehicles promised by Tesla.Strong AI: Also known as “artificial general intelligence” (AGI) or “general AI,” possess the ability to understand, learn and apply knowledge across a wide range of tasks at a level equal to or surpassing human intelligence. This level of AI is currently theoretical and no known AI systems approach this level of sophistication. Researchers argue that if AGI is even possible, it requires major increases in computing power. Despite recent advances in AI development, self-aware AI systems of science fiction remain firmly in that realm.History of AI The idea of "a machine that thinks" dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of AI include the following:1950Alan Turing publishes Computing Machinery and Intelligence (link resides outside ibm.com). In this paper, Turing—famous for breaking the German ENIGMA code during WWII and often referred to as the "father of computer science"—asks the following question: "Can machines think?"From there, he offers a test, now famously known as the "Turing Test," where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since it was published, it remains an important part of the history of AI, and an ongoing concept within philosophy as it uses ideas around linguistics.1956John McCarthy coins the term "artificial intelligence" at the first-ever AI conference at Dartmouth College. (McCarthy went on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw and Herbert Simon create the Logic Theorist, the first-ever running AI computer program.1967Frank Rosenblatt builds the Mark 1 Perceptron, the first computer based on a neural network that "learned" through trial and error. Just a year later, Marvin Minsky and Seymour Papert publish a book titled Perceptrons, which becomes both the landmark work on neural networks and, at least for a while, an argument against future neural network research initiatives.1980Neural networks, which use a backpropagation algorithm to train itself, became widely used in AI applications.1995Stuart Russell and Peter Norvig publish Artificial Intelligence: A Modern Approach (link resides outside ibm.com), which becomes one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems based on rationality and thinking versus acting.1997IBM's Deep Blue beats then world chess champion Garry Kasparov, in a chess match (and rematch).2004John McCarthy writes a paper, What Is Artificial Intelligence? (link resides outside ibm.com), and proposes an often-cited definition of AI. By this time, the era of big data and cloud computing is underway, enabling organizations to manage ever-larger data estates, which will one day be used to train AI models.2011IBM Watson® beats champions Ken Jennings and Brad Rutter at Jeopardy! Also, around this time, data science begins to emerge as a popular discipline.2015Baidu's Minwa supercomputer uses a special deep neural network called a convolutional neural network to identify and categorize images with a higher rate of accuracy than the average human.2016DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves). Later, Google purchased DeepMind for a reported USD 400 million.2022A rise in large language models or LLMs, such as OpenAI’s ChatGPT, creates an enormous change in performance of AI and its potential to drive enterprise value. With these new generative AI practices, deep-learning models can be pretrained on large amounts of data.2024The latest AI trends point to a continuing AI renaissance. Multimodal models that can take multiple types of data as input are providing richer, more robust experiences. These models bring together computer vision image recognition and NLP speech recognition capabilities. Smaller models are also making strides in an age of diminishing returns with massive models with large parameter counts.