Evolving Applications of Low-Code/No-Code

The LC/NC market is expected to grow 20% in 2023 alone and reach an estimated value of $26.9 billion. This technology has gained popularity in recent years as a means of closing skill gaps and making app and web development more efficient. However, it still lacks the flexibility of custom apps designed more traditionally by skilled developers.

Current applications for LC/NC development sit somewhere between off-the-shelf and custom solutions. How will these applications change in the next few years? Here are some of the areas in which developers can expect to see change.

Robotic Process Automation (RPA)

Robotic Process Automation is one of today’s most common applications for Low-Code/No-Code platforms. LC/NC is a great fit for RPA because it usually requires simplifying something that already exists, such as automating a specific workflow.

Low-Code/No-Code developers already know what they need from an app they want to build, so they can shortcut the process without significant User Experience (UX) design. The LC/NC approaches give new developers the tools to build and integrate a straightforward RPA app in the minimum turnaround time possible.

In the future, LC/NC platforms may include more advanced RPA capabilities, and may be able to integrate data from more sources or handle more tasks in a single app. This particular use case may lean more toward No-Code platforms, since automation will soon be necessary for more jobs. As more people without coding experience will seek the ability to use automation, the demand for RPA-specific No-Code platforms will increase.

Simple Web and App Development

The main apps and tools for which Low-Code/No-Code approaches are currently ideal are typically simple in scope and limited in distribution. Most often, a user develops an app solely for in-house use, for their own personal use, or for a one-time event or conference.

For example, Low-Code/No-Code is commonly used for replacing legacy systems. Digital transformation spending is expected to total $3.4 trillion worldwide by 2026. Businesses must evolve their operations and technology to keep up, but that can be difficult without a large development team. Low-Code/No-Code platforms allow companies to upgrade technologies and workflows without in-house developers.

Low-Code/No-Code development platforms aren’t intended for large-scale applications, nor are they ideal for supporting hundreds of users or managing massive quantities of data. In the future, this could change as the technology becomes more capable. For example, Artificial Intelligence (AI) could make it easier to create complex apps without requiring coding knowledge.

Challenges and Innovations in Low-Code/No-Code

How will the capabilities of Low-Code/No-Code platforms evolve in the future? What new applications are emerging? They will increasingly shift toward zero necessary IT involvement in the development process as AI makes it possible for nearly anyone to create original, customized code.

Generative AI-Powered Coding

Generative AI is changing the game in app and web development. Platforms like ChatGPT are opening the door for anyone to try developing their own app or website with zero prior experience. Users can type in a text prompt explaining what they want, and ChatGPT will do its best to generate code that fits the bill. It can also help debug code that users copy and paste into the prompt window.

Of course, platforms like ChatGPT are not foolproof. They do make mistakes, and users have found flaws and gaps in AI-generated code. As of 2023, ChatGPT-4 excels with small, specific chunks of code but breaks down when asked to write an entire application. It can deliver customized code, but only piecemeal. Developers still need to know what’s required and how it fits with the rest of their apps.

Platforms like ChatGPT could evolve into full-scale app development tools in the future. In many ways, AI is the ultimate Low-Code/No-Code platform. Users type in what they want the code to do and let the AI do the rest. Businesses will likely be able to function with small teams of developers who verify and implement it.

Greater Emphasis on Cybersecurity

One of the pitfalls of today’s Low-Code/No-Code platforms is a minimal ability to customize security features. The lack of visibility into the coding going on behind the scenes simplifies development but blinds developers to potential security risks. Additionally, people with no coding knowledge or experience using LC/NC approaches  may not be aware of important security features they should have or red flags to watch out for.

In the future, Low-Code/No-Code platforms will see more emphasis on cybersecurity. For example, the Online Worldwide Application Security Project (OWASP) has developed a framework of 10 key security protocols for Low-Code/No-Code apps. Developers can use it to learn about important security risks and features and how to address them in their development process.

The security options in Low-Code/No-Code platforms themselves will also grow in the years ahead. The global cost of cybercrime is expected to hit $11.5 trillion in 2023 and more than double that by 2027. There will be more demand for advanced security features as security threats grow. For example, developers might begin including AI threat-monitoring tools.

Clearer Intellectual Property Standards

Intellectual Property rights are a growing concern in coding and development, especially since AI can write functional code. When anyone can automate coding, who is really writing it? Who is the developer of new Low-Code/No-Code apps, and who has the IP rights to these programs and any profits made?

These questions must be resolved as Low-Code/No-Code platforms gain in popularity, particularly in the context of growing geopolitical complications surrounding IP rights. For instance, the war in Ukraine led Russia to implement a 0% license fee on IP content from “unfriendly countries” like the U.S. and European nations.

Code and apps can be subject to IP laws, not just content such as books and movies. Low-Code/No-Code platforms may soon be able to develop apps on the same level of customization and precision a professional developer could deliver, and the industry will need to decide who has the IP rights to these new apps—the people using the platforms, or those who designed them.

How Will Low-Code/No-Code Impact Developers?

Low-Code/No-Code technology’s role in the software development industry is also evolving. Everyone is wondering what the future holds for professional software developers today. The combination of AI and Low-Code/No-Code platforms leads many to wonder if they will become obsolete. While this will not happen anytime soon, the developer role is shifting.

Low-Code/No-Code platforms and AI like ChatGPT are tools, like any other technology. They can help developers do their jobs more efficiently and easily but cannot replace the expertise people can provide.

Resolving the skills shortage is one specific area where Low-Code/No-Code platforms will help developers. Coders and programmers are in high demand in all areas of computer science today.

For example, the shortage of cybersecurity professionals leaves many businesses ill-equipped to handle rising cybercrime rates. Similarly, over 37% of recruiters report struggling to find enough developers with the necessary skills for their businesses’ needs. However, young people continue to show a strong interest in computer science, indicating a growing talent pool.

Demand for software development skills continues to grow faster than the available talent pool can keep up with. Low-Code/No-Code platforms will help businesses fill those shortages. Smaller teams of developers can use them to work more efficiently and operate at the same level as a larger group.

Similarly, developers may not need to do much manual coding in the future. Their roles may shift toward designing, testing, and maintaining apps. Meanwhile, Low-Code/No-Code platforms and AI will do the bulk of the actual code-writing process. As a result, developers will be able to roll out apps faster and with less budget required.

Low-Code/No-Code Is Innovating Software Development

Low-Code/No-Code software development platforms are transforming how new apps, tools, and websites are created. Now anyone can get into software development, regardless of prior coding experience.

Low-Code/No-Code platforms will become more capable in the years ahead thanks to the advanced capabilities of AI models like ChatGPT. IP rights and cybersecurity will become important concerns as adoption grows. Professional developers will remain vital to the industry for the foreseeable future, although their roles will evolve to adapt to Low-Code/No-Code processes.

With rapid IT modernization, businesses are eager to adopt effortless application-building processes. Low-Code/No-Code platforms are modular approaches to application development that help users develop custom solutions without writing code line by line. As a result, even non-technical users can build and deploy apps, regardless of their coding experience.

The Low-Code/No-Code market continues to grow exponentially as the platforms enable faster application development with minimum coding and investment. Gartner reports that 70% of new applications will be developed using Low-Code or No-Code technologies by 2025.

If you plan to integrate LC/NC platforms into your teams’ workflows, this guide will help you decide whether LC/NC is the right choice.

What are Low-Code/No-Code Platforms?

While the terms Low-Code and No-Code are often used interchangeably, there are a few key differences between the two.

Low-Code framework is based on Graphical User Interfaces (GUI) that can process simple logic and offer drag-and-drop features. It does not require extensive coding. As it eliminates this bottleneck, users with basic technical aptitude can also design and update apps, reducing the overall coding time, app development cycle, and costs.

No-Code platforms require zero coding experience or technical skills. Any business user can take on application projects using this framework. Using only visual tools, No-Code platforms help create a fully functional app by dragging and dropping software components.

Advantages of LC/NC Platforms

The primary aim of LC/NC platforms is to remove the complexities associated with coding. They effectively address evolving business needs and thus are in high demand. Here are the top benefits of LC/NC platforms:

Less Reliance on Core Development Team

LC/NC tools drastically reduce the dependency on core development teams. Organizations looking to implement digital workflows no longer have to wait for approvals from their developers. With LC/NC tools, business users can rapidly build apps to automate their processes. As a result, the core IT team can focus on larger, contextual business problems and create solutions around them.

Limited Specialized Experience

LC/NC platforms help democratize technology. These tools empower business users and can play a crucial role in overcoming the growing developer shortage. Even a user without coding experience can design applications using the Low-Code/No-Code framework. This is especially beneficial for small and medium-scale businesses looking to digitize their processes on a budget.

Bridge the Gap Between Business and IT

LC/NC platforms create an agile environment that promotes collaboration between business and IT teams. As a result, developers are better equipped to understand the business problem, while non-technical users become more aware of the functionalities they require in their business apps. This clarity allows both teams to ask the right questions and collaborate more effectively to achieve better business outcomes.

Increased Productivity and Efficiency

LC/NC platforms offer pre-configured modules and functionalities that significantly reduce the development effort. This approach also lowers IT infrastructure investments and accelerates the development and launch cycle.

Limitations of LC/NC Platforms

Despite the numerous advantages of LC/NC platforms, they do not necessarily replace core development. Here are a few of the downsides to using these platforms:

Suitable Only for Simple Applications/Proof of Concept

Low-Code or No-Code platforms have built-in templates and components. These can help develop simple applications or Minimum Viable Products (MVP). However, if you need advanced features in your applications or if you need to scale your solution, core development would be necessary.

Limited Functionality/Design Choices

Since LC/NC platforms come with pre-built functionalities and modules, you may not get enough flexibility to customize the application. Plus, depending on your chosen LC/NC platforms, you’ll likely need to settle with available design options.

Security Concerns

When businesses rely on LC/NC platform providers, they also expose their data and systems, raising security concerns. If any security flaws are encountered, non-technical users may be unable to fix the issue on their own.

Top 5 Low-Code/No-Code Platforms

The top five LC/NC platforms each offer strong capabilities for individuals and business users.

Microsoft Power Apps

Technical giant Microsoft offers the cloud-based platform Power Apps to help business users build and deploy applications quickly.

Type of Platform
Microsoft Power Apps is a Low-Code platform that accelerates the app-building process. Integrated with Microsoft Dataverse, it links all your data with Microsoft 365, Dynamics 365, and Azure capabilities.

Applications
Microsoft Power Apps help with application modernization, streamlining migration projects, extending existing development capabilities, centralizing data, and automating manual processes.

Special Features
Microsoft Power Apps has a robust set of features that includes:

• AI copilot for rapid automation
• Process automation for simplified app building
• Drag-and-drop user interface
• Extensive integration with powerful connectors

Pricing
Microsoft Power Apps has pay-as-you-go plans that start at $0.30 per website per month for anonymous users. Subscription plans start at $75 for 500 anonymous users per website. For more details, check the pricing page at https://powerpages.microsoft.com/en-us/pricing/

Appian

Appian is an industry-leading software company that helps businesses with process automation and digital innovation.

Type of Platform
Gartner has recognized Appian as a top choice for Low-Code application platforms. It combines intelligent automation and Low-Code development to help businesses in their application-building processes.

Applications
Appian delivers hyper-automation that enables businesses to build and launch smart applications faster. The platform can help in creating native mobile apps as well as enterprise-wide systems. It is suitable for businesses of all sizes.

Special Features
This Low-Code platform is equipped with many features:

• Integration with native deployment tools like Jenkins
• End-to-end process automation
• Faster data design with data fabric
• Appian guarantee to deliver the first project in eight weeks

Pricing
Appian offers free trial services with a guided learning experience. There are other fully featured plans like Application, Platform, and Unlimited. The Application plan starts at $2 per user per month. The standard usage plan starts at $75. Get the details of different plans at https://appian.com/products/platform/pricing.html

Caspio

The American software company Caspio helps businesses create sophisticated cloud-based applications through its No-Code platform.

Type of Platform
Caspio offers a platform for No-Code application development with proprietary workflows to address unique business requirements.

Applications
Caspio can accelerate business transformation with minimum efforts and investment. It can also set up app protection and help with online application deployment.

Special Features

• Extensive integration possibilities with AWS, Paypal, Google Drive, Box, and more
• Massive scalability with AWS infrastructure
• Intuitive tools for data visualization and analysis
• Enterprise-grade security and regulatory compliance

Pricing
Caspio offers free service for light usage. It has other plans, including Explore, Build, Grow, and Corporate. The minimum charge is about $128 per month. You can get pricing plans at https://www.caspio.com/pricing

Mendix

Siemens is the parent organization of Mendix, which offers a highly productive platform for designing mobile and web applications.

Type of Platform
Mendix is a Low-Code platform that accelerates enterprise app delivery with unmatched expertise. With an intuitive visual User Interface (UI) and drag-and-drop functionalities, it can accelerate the app development lifecycle and also automate the involved processes.

Applications
Mendix helps businesses with application lifecycle management, secure data integration, continuous delivery support, app features extension, and other functionalities.

Special Features
Some of the unique features of the Mendix platform include the following:

• Model-driven development for reduced human intervention
• Streamlined digital initiatives for workflow automation
• Robust version control to work on different app features
• Collaborative project management with Mendix Developer Portal

Pricing
Mendix pricing is based on the number of apps to be built: One, or Unlimited. It offers free trial versions for both. Basic, Standard, and Premium plans start at about $63 per month for one app. Check pricing details at https://www.mendix.com/pricing/

Zoho Creator

The multinational technology company Zoho Corporation offers an app-building platform, Zoho Creator. This full-stack product helps businesses create customized applications easily.

Type of Platform
Zoho Creator is a Low-Code application development platform that requires minimal coding. It is user-friendly and has many built-in features and functions.

Applications
Zoho Creator is an excellent choice for developing custom applications quickly from pre-built blocks. Zoho offers multi-platform access as it is also compatible with iOS and Android devices. It also helps create insightful reports to optimize business processes.

Special Features

• Abstracts 90% of complexities in the application development cycle
• Drag-and-drop elements for instant app-building
• Unified data service for centralized management
• Data-structure visualization with cross-functional analytics

Pricing
Zoho Creators offer a 15-day free trial service. It has monthly and yearly pricing plans that start at $11. It also offers flexible pricing options if you need a tailored plan. Learn more about the pricing here: https://www.zoho.com/creator/pricing.html

Choosing the Right LC/NC Platforms

While both Low-Code and No-Code platforms offer programmers and non-programmers the ability to rapidly build apps and deploy them as a service, choosing between them depends on several factors.

When evaluating the LC/NC platforms, consider the following:

• Objective: Low-Code platforms are typically preferable for complex use cases and business-critical requirements that demand on-premises or cloud integrations. On the other hand, No-Code platforms are ideal for a narrow set of use cases with limited scalability requirements.
• Integration Capabilities: LC/NC platforms should offer connectors to integrate external databases and other business applications.
• Automation: It is critical to evaluate the platforms based on automation capabilities. Look for tools that offer Artificial Intelligence/Machine Learning-assisted development to automate repetitive tasks and enhance productivity.
• Hosting: Your LC/NC vendor must offer flexible hosting options to avoid costly lock-ins. It could be on-premises, on-cloud, or hybrid. Some vendors even allow hosting applications on your public/private cloud. Such an arrangement enables better control over your Continuous Improvement/Continuous Delivery (CI/CD) pipelines.
• Security: Choosing LC/NC platforms that offer robust security features is critical. When evaluating different platforms, look for vendor certifications such as Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standard (PCI-DSS), General Data Protection Regulation (GDPR), ISO/IEC 27001, and more.
• Vendor support: Your vendor should offer comprehensive support to resolve queries quickly. Without good support services, your citizen developers will likely reach out to your core development teams and occupy them with easily avoidable tasks.
• Cost: When starting out, it is better to evaluate each LC/NC platform through the free tier. Some vendors also offer pay-as-you-go models that will allow you to control your costs based on the frequency of usage.

Low-Code/No-Code movement is accelerating rapidly, allowing organizations to design, build, and ship custom applications quickly and with minimal technical expertise. These platforms drastically reduce the dependency on core developers while empowering business users to innovate faster.

For organizations looking to achieve their digital transformation goals, it is a good time to embrace LC/NC platforms and bolster growth.

A survey from Appian shows that 82% of companies struggle to attract and retain the software engineers they need. Developers engaged by companies are also finding it challenging to meet deadlines. In one study, 47% of software engineers who responded said they lacked the tools to build apps and products quickly enough to meet their deadlines.

Low-Code and No-Code (LC/NC) development enables businesses to address these challenges more efficiently. These innovative application development approaches help generate automated code using Graphical User Interfaces (GUIs) like drag-and-drop features and pull-down menu interfaces to make it possible for enterprises to speed up app development by a factor of 10.

Enterprises with no or limited technical resources can leverage LC/NC to create, modify, and deploy applications faster. While the terms Low-Code and No-Code are often used interchangeably, the two approaches have key differences. If you’re planning to integrate LC/NC in your development processes, it’s essential to understand these differences to identify the development approach that will best meet your specific project requirements.

What is Low-Code?

Low-Code is a middle path between manual coding and no coding. Low-Code developers can add their own code over automatically generated code, which helps them customize and integrate their applications.

Pros of Low-Code

• Helps businesses develop applications by depending less on Information Technology (IT) teams.
• Allows faster delivery of software solutions by reducing the time and effort of manual coding.
• Helps solve the market limitations of talented developers by facilitating development by people with little-to-no coding ability.
• Empowers digital transformation and innovation by allowing quick development, testing, and deployment of applications that leverage such next-generation technologies as cloud computing, Artificial Intelligence (AI), Robotic Process Automation (RPA), data analytics, and the Internet of Behaviors (IoB).

Cons of Low-Code

• Can promote “Shadow IT,” the unauthorized development and deployment of applications and IT services beyond those recognized by an enterprise.
• Can limit application customization due to the constraints of features and libraries.
• Proprietary Low-Code platforms, or those that are incompatible with other platforms, can lead to vendor lock-in and portability issues.
• Platforms require frequent updates and audits.
• Applications must be properly tested and verified to prevent security and compliance risks.

What is No-Code?

As the name implies, No-Code is a software development approach that requires zero coding skills to build applications quickly. In other words, it is a hands-off approach that depends solely on visual tools.

Pros of No-Code

• Cost-effective; it allows businesses to build applications without the need to hire developers or outsource software development projects.
• Eliminates the time and effort of manual coding and testing.
• Applications are easily customizable, as users can easily change and update using such simple visual tools as drag-and-drop.
• Easily accessible, regardless of a user’s coding skills or background.

Cons of No-Code

• Functionality is dependent on platform capabilities and, in most cases, offers limited functionality and doesn’t support specific IT requirements.
• May not comply with industry standards or regulations and may expose sensitive data, causing security issues.
• Lacks flexibility and is difficult to integrate with other platforms or systems.
• Restricts users from easily migrating or exporting applications.
• Can affect the reliability and speed of applications with code bloat, leading to poor performance.

Low-Code vs. No-Code: Differences and Similarities

Working Principle

• Low-Code: Platforms have easy-to-use GUIs that let users automate codes. Therefore, they can easily customize applications by adding their logic to auto-generated code.
• No-Code: Development approach solely depends on GUIs, which enables users to build applications without coding. It restricts the users from adding their own code over auto-generated code.

Target Users

• Low-Code: Suited for people with minimal coding skills who want to accelerate their application development process. It is also the best fit for enterprises that want to build customized applications without depending on highly skilled coders or developers.
• No-Code: Allows people with no coding skills to easily build simple, standalone applications. Businesses that want to build self-service applications and dashboards can also take this approach.

System (Open or Closed)

• Low-Code: Has an open system that allows users to access and modify the underlying code. Low-Code applications or platforms can be easily integrated with existing systems and external plugins.
• No-Code: Has a closed system that doesn’t allow users to access and modify the underlying code. No-Code applications or platforms can offer only limited integration with existing systems and external plugins.

When to Use Low Code vs. No Code

Here are the four major use cases for Low-Code development platforms:

UI Design

With minimal coding by using GUIs, people can use Low-Code methodology to create engaging User Interfaces (UIs) that go well with multiple devices, platforms, and Operating Systems.

API Generation

Low-Code platforms can help anyone create Application Programming Interfaces (APIs) for both legacy and new applications by analyzing existing application code and auto-generating API code.

UX Design

Low-Code platforms help users create engaging User Experience (UX) through an enterprise’s products and services, such as web portals, mobile applications, or Progressive Web Apps (PWAs).

Containerization

Low-Code platforms let users speed up the development and deployment of containerized applications to multiple environments like the public cloud. Low-Code applications can easily integrate with open-source containerized application deployment and management systems like Kubernetes.

Here are the four major use cases for the No-Code development approach:

Business Intelligence (BI) Reporting

No-Code platforms help BI analysts and developers create reporting tools that transform raw data into meaningful insights by using GUIs and pre-built templates.

Process Automation

No-Code empowers developers to automate such repetitive tasks as data entry and invoice processing without the need for coding.

Interactive Web Portals

No-Code platforms let companies create interactive web portals that offer self-service solutions to customers, such as submitting claims, paying bills, or generating quotes by using interactive themes and layouts and integrating with other platforms.

The Future of Low-Code and No-Code

Three major trends and predictions that show the future is bright for LC/NC approaches: widespread adoption, the rise of amateur programmers, and convergence with other innovative technologies.

Gartner estimates that by 2026, developers outside of conventional IT teams will make up 80% of users of Low-Code tools, up from 60% in 2021. That means more non-technical people will start building applications using these technologies. These amateur programmers are also known as “citizen developers.”

Low-Code and No-Code technologies are already getting integrated with such innovative technologies as AI, blockchain, the Internet of Things (IoT), Augmented Reality (AR), and Virtual Reality (VR).

This convergence will lead to more innovation and interactive applications. For example, bringing Low-Code or No-Code together with AI can automate tasks, provide recommendations, generate code, and enhance UX.

However, the LC/NC movement also comes with a few challenges or limitations, such as security, scalability, customization, and integration. While these approaches may not solve every software development problem, they successfully demonstrate how several development phases can be simplified.

In the future, more enterprises and individuals will embrace Low-Code and No-Code tools as they become more widely available and their benefits become more apparent within the community of developers and IT leaders.

There are several clear benefits for students and educators to utilizing AI education technology:

• Personalized learning programs adapted to each student’s ability and goals
• On-demand tutoring via AI chatbots and software-driven tutors
• Automation that can cut through bureaucratic red tape — for example, automated chatbots that can answer frequently asked questions
• 24/7 access to learning from anywhere
• Time-management benefits for teachers, due to smart automation of tedious, time-consuming tasks, like record-keeping and grading

Below are listed some of the leading ways the education sector is using AI. 

For more information, also see: What is AI? 

Benefits of AI Use in Education

• More Inclusive Learning
• Quick Grade and Feedback
• New Intelligent Tools
• Prepare for Future Careers

More Inclusive Learning

Not every student learns the same way. Teaching methods should be focused on the student, and artificial intelligence can help schools make that possible.

With the ability to include all students and extend personalized learning, students can learn and master topics in a way they can understand. Language can also be taught in a more healthy way, and “[AI] can help non-native speakers to improve their language skills through interactive conversations,” says Melissa Loble, Chief Customer Experience Officer at Instructure.

With AI, students can understand and retain information in a way they can master and memorize what they need to succeed. 

Quick Grade and Feedback

Teachers spend many hours grading students’ work, taking away time to create other lesson plans to directly benefit students.

With new AI technology, grading and giving feedback is able to happen with teachers’ and schools’ approval. Automating the task is extremely beneficial for teachers, schools, and students who will be able to see what they do not understand in their lessons.

New Intelligent Tool

AI is growing at a rapid rate in many school situations were it is clearly needed. While some are worried about AI, others have decided to use the tool in the classroom.

“AI should be a tool to use in the classroom the same as a calculator: it helps get to the endpoint but doesn’t get us there without knowing what buttons to push,” says Jenn Breisacher, CEO of Student-Centered World, “Student-led, inquiry-based, and hands-on assignments need to become the norm not only because of AI technology that will only get wiser from here but also because that is what is sticking with Generation Z and Generation Alpha.”

Making AI a tool in the classroom and beyond can extend practices in technology and other educational situations.

Prepare for Future Careers

Education leads to careers in many fields. From the medical industry to commercial trucking companies, AI can be used for both training and company practices. 

“AI can now create realistic diagnostic images, such as X-rays or CT scans, with interesting variations and ‘conditions’ included in them. These images can provide a wide range of challenging cases for medical students to learn from without compromising patient privacy,” says Bob Rogers, CEO of Oii.

Melissa Loble, Chief Customer Experience Officer at Instructure agrees, “These tools are capable of generating marketing content, populating legal applications, and enabling non-designers to create artwork that meets their needs.” Furthermore, “These tools will only become more advanced and ubiquitous in the near future.”

For more information, also see: AI and Deep Learning

Examples of AI in Education

1. Gradescope

The Gradescope platform speeds up the grading process, benefiting both teachers and students.

Students upload assignments to the platform, and Gradescope sorts and groups answers and assigns a grade. The application of AI decreases the time educators spend grading by 70% or more, according to the company.

The platform delivers a detailed analysis of student performance that can pinpoint individualized tutoring and teaching needs. 

2. Content Technologies, Inc. (CTI)

CTI is a prominent AI research and development company that focuses on customized education content by applying deep learning AI techniques.

CTI’s software can analyze course materials, textbooks, syllabi, and other resources to create textbooks, study guides, and multiple-choice tests. 

The company is also using AI to power tools like Cram101 and JustTheFacts101. Cram101 synthesizes textbooks into nuggets of information, generating complete study guides with summaries, practice tests, and flashcards. JustTheFacts101 is a tool that can highlight the most important information from virtual textbooks to create high-level chapter summaries. 

3. Brainly

Brainly is an online space that offers a supportive message board setting for peer-to-peer learning and homework help — the site’s motto is, “For students. By students.”

Students can ask questions, find study partners, and learn from one another collaboratively. While Brainly does rely on human moderators to verify questions and answers, the platform also applies machine learning (ML) algorithms that can automatically filter spam and low-quality content, like incorrect answers, freeing up moderator time. 

In a partnership with Rutgers University, Brainly also developed a machine learning approach that matches students based on skill sets. For example, a student who has correctly answered advanced algebra questions may be matched with a student who needs additional help with algebra assignments. 

4. Thinkster Math

Thinkster Math applies machine learning and AI to analyze student achievement on math problems.

As students solve problems through the app, it tracks each step and then delivers progress reports about how students handled various skills, like long division or multiplication.

Thinkster Math is used in classrooms and as an online tool that matches math tutors to students to create personalized learning programs based on student strengths and challenges. 

5. Duolingo

Duolingo is aimed at a broader audience than many other edtech tools.

The language-learning app uses AI to help anyone progressively build foreign language skills. As language learners work through various mini-quizzes and other testing tools, Duolingo adapts and evolves as their skill levels increase. 

Duolingo reports it currently has 120 million users learning 19 distinct languages through the app. 

For more AI companies: 100 Top Artificial Intelligence (AI) Companies

Bottom Line: AI and Education

AI is beneficial for modern education as technology and tools grow. Benefits include more personalized learning, time-management benefits for teachers, intelligent new tools, and preparation for future jobs.

The educational technology sector will include even more AI-powered solutions and benefits education as it grows as a useful tool.

For more information, also see: AI Software and Tools 

For companies, individual data models are built around the specific needs and requirements of the organization, and they can be visualized on various levels of abstraction depending on the information that needs to be extracted for the dataset. This type of work is often done by a team of data engineers, data analysts, and data architects, along with database administrators who are familiar with both the original database and the organization’s needs.

Before implementing a data modeling framework into your company’s information systems, it’s important to first understand what makes a database useful and usable for information extraction and how it can help you map out the connections and workflows needed at the database level.

This article can help you gain a thorough and wide-scale understanding of how data modeling works, what its various types are, and how it can benefit your business.

Table of Contents

• 3 Types of Data Modeling Categories
• 4 types of Data Modeling Infrastructure
• How Data Modeling Works
• What are the Features of Data Modeling
• 5 Benefits of Data Modeling
• Top 4 Data Modeling Tools

3 Types of Data Modeling Categories

There are different types of data modeling techniques that can be divided into three main categories: conceptual, logical, and physical. Each type serves a specific purpose depending on the format of data used, how it’s stored, and the level of abstraction needed between various data points.

Conceptual Data Model

Conceptual data models, also referred to as conceptual schemas, are high-level abstraction forms of representing data, but they’re also the most simple. This approach doesn’t go in-depth into the relationship between the various data points, simply offering a generalized layout of all of the most prominent data structures.

Thanks to their simple nature, conceptual data models are often used in the first stages of a project. They also don’t require a high level of expertise and knowledge in databases to understand, making them the perfect option to use in shareholder meetings.

Key Differentiators

High-abstraction conceptual data models are used to showcase what data is in the system. Generally, they include surface-level information about the data such as classes, characteristics, relationships, and constraints. They’re suitable for gaining an understanding of a project’s scope and defining its basic concepts.

Pros

• Starting point for future models.
• Defines the scope of the project.
• Includes shareholders in the early design process.
• Offers a broad view of the information system.

Cons

• Low returns on time and effort.
• Lacks deep understanding and nuance.
• Not suited for larger systems and applications.
• Insufficient for the later stages of a project.

Examples

There are countless applications of conceptual data modeling outside of the need for developing or improving an information system. It can be used to showcase the relations between different systems or steps ofr a process.

For an order management system, an abstract diagram can help present the relationship between the various operations that go on when a customer places an order. It can also draw a clear relationship between the storefront — digital or physical — and the invoicing system, order fulfillment department, and order delivery.

Logical Data Model

Logical data models, also referred to as logical schemas, are an expansion on the basic framework laid out in conceptual models, but it considers more relational factors. It includes some basic annotations regarding the overall properties or data attributes, but it still lacks an in-depth focus on actual units of data.

Key Differentiators

This model is particularly useful in data warehousing plans, as it’s completely independent of the physical infrastructure and can be used as a blueprint for used data in the system. It allows for a visual understanding of the relationship between data points and systems without being too invested in the physicality of the system.

Pros

• Performs feature impact analysis.
• Easy to access and maintain model documentation.
• Speeds up the information system development process.
• Components can be recycled and readapted according to feedback.

Cons

• The structure is difficult to modify.
• Lack of in-depth details of data point relations.
• Errors are difficult to spot.
• Time- and energy-consuming, especially for larger databases.

Examples

Logical data modeling is more suitable for databases with a number of complex components and relationships that would need mapping. For instance, using logical modeling to map an entire supply chain, you can have easy access to not only the attribute names but also the type of data and its indicators for mandatory and non-nullable columns.

This approach to data representation is considered database-agnostic, as the data types are still abstract in the final presentation.

Physical Data Model

Physical data models, also referred to as physical schemas, are a visual representation of data design as it’s meant to be implemented in the final version of the database management system. They’re also the most detailed of all data modeling types and are usually reserved for the final steps before database creation.

Key Differentiators

Physical data models conceptualize enough detail about data points and their relationships to create a schema or a final actionable blueprint with all the needed instructions for the database built. They represent all rational data objects and their relationships, offering a high-detail and system-specific understanding of data properties and rules.

Pros

• Reduces incomplete and faulty system implementations.
• High-resolution representation of the database’s structure.
• Direct translation of model into database design.
• Facilitates detection of errors.

Cons

• Requires advanced technical skills to comprehend.
• Complex to design and structure.
• Inflexible to last-minute changes.

Examples

Physical data modeling is best used as a roadmap that guides the development of a system or application. By being a visual representation of all contents of a database and their relations, it enables database administrators and developers to estimate the size of the system’s database and provide capacity accordingly.

4 Types of Data Model Infrastructure

In addition to the three primary types of data modeling, you can choose between several different design and infrastructure types for the visualization process. Choosing the infrastructure would determine how the data is visualized and portrayed in the final mapping. For that, there are four types you can pick from.

Hierarchical Data Model

Hierarchical data models are structured in a way that resembles a family tree, where the data is organized in parent-child relationships. This type allows you to differentiate between records with a shared origin, in which each record can be identified by a unique key belonging to it, determined by its place in the tree structure.

Key Differentiators

Hierarchical data modeling is most known for its tree-like structure. Data is stored as records and connected through identifiable links that represent how they influence and relate to one another.

Pros

• Simple and easy to understand.
• Readable by most programming languages.
• Information can be removed and added.
• Fast and easy to deploy.

Cons

• Structural dependence.
• Can be bloated with duplicate data.
• Slow to search and retrieve specific data points.
• Cannot describe relations more complex than direct parent-child links.

Examples

Hierarchical data modeling is best used with easily-categorized data that can be split into parent-child relations.

One example where this is highly beneficial is for the fulfillment of sales, in which numerous items exist under the same name but can be differentiated by associating with one sale order at a time. In this scenario, the sale order is the parent entity, and the items are the child.

Relational Data Model

Unlike hierarchical data models, relational data models aren’t restricted to the parent-child relationship model. Data points, systems, and tables can be connected to each other in a variety of manners. This type is ideal for storing data that needs to be retrieved quickly and easily with minimal computing power.

Key Differentiators

Relational data models can be differentiated by checking whether they follow ACID characteristics, which are atomicity, consistency, isolation, and durability.

Pros

• Simplicity and ease of use.
• Maintains data integrity.
• Supports simultaneous multi-user access.
• Highly secure and password-protected.

Cons

• Expensive to set up and maintain.
• Performance issue with larger databases.
• Rapid growth that’s hard to manage.
• Requires a lot of physical memory.

Examples

Relational data models are best suited for use with serial information that’s related but can be beneficial separately.

One example is maintaining a database of members, customers, or users of an establishment. The structure of rows and columns can be used to store the first and last names, birth dates, Social Security numbers, and contact information that are grouped within one another as relating to a single individual.

Entity-Relationship (ER) Data Model

Entity-relationship data models, also referred to as entity relationship diagrams (ERDs), are a visual way of representing data that relies on graphics depicting the relationship between data points, usually people, real-world objects, places, and events, in the information system.

This type is most commonly used to better understand and analyze systems in order to capture the requirements of a problem domain or system.

Key Differentiators

ER data models are best used to develop the base design of a database as it delves into the basic concepts and details required for implementation, all using a visual representation of the data and relationships.

Pros

• Simple and easy to understand.
• Compatibility with database management systems (DBMSs).
• More in-depth than conceptual modeling.

Cons

• Difficult to expand and upscale.
• Retains some ambiguity.
• Only works best for relational databases.
• Long-winded and wordy.

Examples

ER diagrams represent how databases are related as well as the flow of processes from one part of the system to the next. The overall representation resembles a flowchart but with added special symbols to better explain the various relations and operations occurring in the system.

One prominent example of ER models is used with public institutions like universities to help them better categorize and parse their demographic of students. ER diagrams showcase student names and connect them with their taken courses, mode of transportation, and occupation.

Object Oriented Data Model

Object oriented data models are a variation on conceptual data modeling that instead uses objects to make complicated real-world data points more legible by grouping entities into class hierarchies. Similarly to conceptual modeling, they’re most often used in the early stages of developing a system, especially data-heavy multimedia technologies.

Key Differentiators

Instead of focusing solely on the relationship between data points and objects, object-oriented data modeling centers the data of the real-world object, clustering them along with all related data, such as all personal information and contact information of an individual.

Pros

• Easy to store and retrieve data.
• Integrates with object-oriented programming languages.
• Improved flexibility and reliability.
• Requires minimal maintenance efforts.

Cons

• Lacks a universal data model.
• Highly complex.
• Higher chances of performance issues.
• Lack of adequate security mechanisms.

Examples

Object-oriented data models allow businesses to store customer data by separating individual attributes into various tables but without losing the links between them.

An object in the data model represents the type of customer, which can then be followed in either direction to collect the remainder of the customer’s information without having to involve unnecessary parts of the database.

How Data Modeling Works

Data modeling is the process of visualizing the relationship between and the locations of various data points by a data modeler — usually a database administrator or data architect that works in close proximity to the data. The first and most important step of data modeling is determining the right type for the applications.

Depending on whether you’re using conceptual, logical, or physical data modeling, the resulting diagram could carry varying degrees of simplicity, detail, and abstraction. Identifying user access patterns can also help to determine the most critical parts of the database to represent in order to adhere to your business’s needs.

Before concluding the data modeling process, it’s important to run a handful of test queries to identify the validity of the data model.

What are the Features of Data Modeling

When it comes to searching for a suitable data modeling tool or picking out the appropriate data modeling approach, there are functionalities and capabilities you should expect. The following are some of the key features of any approach to data modeling.

Data entities and their attributes

Entities are abstractions of real pieces of data. Attributes are the properties that characterize those entities. You can use them to find similarities and make connections across entities, which are known as relationships.

Unified modeling language (UML)

UML are the building blocks and best practices for data modeling. They’re a standard modeling language that help data professionals visualize and construct appropriate model structures for their data needs.

Normalization through unique keys

When building out relationships within a large dataset, you’ll find that several units of data need to be repeated to illustrate all necessary relationships. Normalization is the technique that eliminates repetition by assigning unique keys or numerical values to different groups of data entities.

With this labeling approach, you’ll be able to normalize, or list only keys, instead of repeating data entries in the model every time entities form a new relationship.

5 Benefits of Data Modeling

Data modeling offers several distinct benefits to enterprises as part of their data management.

Improves data quality

Data modeling allows you the opportunity to clean, organize, and structure data beforehand. This enables you to identify duplicates in data and set up monitoring to ensure its long-term quality.

Saves time and energy

Despite being an added step that may need to be repeated multiple times throughout the project’s development process, modeling a database before work begins sets up the scope and expectations for the project.

Clear-cut data modeling ensures you don’t end up spending more time and resources on a step than is necessary and justified by the data itself.

The inclusion of nontechnical departments

The early stages of a project’s development are oftentimes too abstract for individuals with little to no technical experience to fully understand.

The visual nature of data modeling, especially conceptual data modeling, allows for more collaboration and discussions among shareholders and nontechnical departments such as marketing and customer experience.

Promotes compliance with regulations

Privacy and security regulations need to be included from the earliest stages of a system’s development. Data modeling enables developers to fit all of the necessary parts for compliance into the design’s infrastructure.

By understanding how data points relate and interact with one another, you can better set the bar for secure and safe data governance.

Improves project documentation

Documentation is essential to encapsulate the development process of a system and helps with solving any future problems or inconsistencies that may arise as well as with training future employees. By building an in-depth data model early on in the development process, you’ll be able to include that into the system’s documentation to allow for a deeper understanding of how it works.

Top 4 Data Modeling Tools

Data modeling has become a pillar of the growing data governance market, particularly because of the streamlined data visibility data models allow enterprises to provide to non-data professionals within their organizations.

The data governance market is expected to grow at a compound annual growth rate of over 21% between 2021 and 2026, with an estimated value of $5.28 billion by 2026, according to a study by ReportLinker. Much of this growth will be attributed to increasing global data regulations, most notably the General Data Protection Regulation (GDPR) in the EU.

This highly lucrative market has been the driving factor of countless tech services providers creating their own data modeling tools — some open source and free to use.

Enterprise Architect

Enterprise Architect is a graphical tool designed for multi-user access, suitable for both beginner and advanced data modelers. Through a number of built-in capabilities ranging from data visualization, testing, and maintenance to documentation and reporting, it can be used to visually represent all of the data in your system’s landscape.

Apache Spark

Apache Spark is an open-source processing system for large data management and modeling. It can be used completely free of charge with no licensing costs, providing users an interface for programming clusters with implicit fault tolerance and parallelism.

Oracle SQL Developer Data Modeler

The Oracle SQL Developer Data Modeler is part of the Oracle environment. While not open source, it’s free to use for developing data models and creating, browsing, and editing conceptual, logical, and physical data models.

RapidMiner

RapidMiner is an enterprise-grade data science platform and tool that can be used to collect, analyze, and visually represent data. It’s perfect for beginner and less-experienced users with a user-friendly interface.

It integrates seamlessly with a wide variety of data source types, ranging from Access, Teradata, and Excel to Ingres, MySQL, and IBM DB2 to name a few. Furthermore, it’s capable of supporting detailed data analytics across a broad artificial intelligence (AI) life cycle.

Bottom Line: Data Modeling

Data modeling is an approach to visually representing data in graphs and diagrams that vary in abstraction, level of detail, and complexity. There are multiple types and approaches to data modeling, but its primary benefit is to help conceptualize and lead the development of a database-reliant system.

From free, open-source tools to enterprise-ready solutions and platforms, you can automate and simplify the bulk of the data modeling process, making it more accessible to smaller teams and urgent projects on a limited budget.

The following case studies can help companies see how network segmentation is being used by organizations in different industries.

5 network segmentation case studies

1. ServiceNow
2. Oil and Gas Refinery
3. Modern Woodmen of America
4. Clothing Manufacturer
5. Children’s Mercy Kansas City

1. ServiceNow

ServiceNow is a leading IT service management provider. Joel Duisman, the company’s principal IT security architect, recognized the need to improve an existing network segmentation strategy. He wanted to strengthen the protection of the company’s core services and domain controllers.

He chose service provider Illumio to meet those needs and moved forward with a phased rollout. The ServiceNow IT team appreciated how Illumio offered real-time visibility and gave consistently high protection in a multicloud environment.

“I sleep better at night knowing that Illumio closes the doors on potential attacks against our domain controllers. The demonstrable risk to the environment is noticeably lessened,” Duisman says.

Industry: IT services

Network segmentation product: Illumio Secure Cloud

Outcomes:

• Improved compliance with client audits
• Provided flexibility across cloud and on-premises data
• Enhanced protection of multiple systems without interruptions

2. Oil and Gas Refinery

Leaders at a major oil and gas refinery were experiencing unexplained data loss that made it more challenging to track emissions and otherwise stay in compliance with industry regulations. They hired the Champion Technologies team to troubleshoot after they couldn’t pinpoint the problem themselves.

Champion Technologies performed an in-depth site survey to compare the refinery’s current setup to best practices. Network segmentation was one of the recommended improvements. The providers also updated network components and provided monitoring software. These improvements give employees a better understanding of what’s happening on their network and ensure they get timely alerts to avoid regulatory fines.

Industry: Oil and gas

Network segmentation product: Champion Technologies provided Layer 2 network switches, Syslog software and segmented network

Outcomes:

• Stopped a known data loss problem
• Improved network security
• Tightened industry compliance

3. Modern Woodmen of America

Modern Woodmen of America is a fraternal financial services organization that aims to bring clarity through services, such as retirement planning and life insurance.

The organization uses a self-service portal that members can access anytime and anywhere. However, its traffic management system only handled virtual infrastructures, leaving a significant segment of traffic unmanaged. The company worked with service provider 27 Virtual to transition to VMware NSX-T and solve that problem.

“The inability to set up segmentation policies and east-west firewalling across dev, stage, and prod environments created a security gap that could be exploited by sophisticated threat actors,” says Zach Lotz, senior network engineer, Modern Woodmen of America.

“Once an attacker gained access, they’d have free reign to spread throughout the network.”

However, migrating to VMware NSX-T caused notable changes.

“The best part of segmentation with NSX-T is the ability to start broad — development versus production — and then go more granular as needed, even down to the application level,” Lotz says.

“This allows us to lock down our network to the point where only known traffic can communicate while everything else is blocked. Any anomaly is quickly identified and dropped.”

Industry: Financial services

Network segmentation product: 27 Virtual assisted the client in switching to VMware NSX-T for its network segmentation needs

Outcomes:

• A more modernized network infrastructure
• Secure 24/7 access to apps by staff and members
• Better security against unknown traffic

See more: Network Segmentation vs. Microsegmentation

4. Clothing Manufacturer

A clothing manufacturer approached Burwood Group because of a need to get back into payment card industry (PCI) compliance. The service provider performed a network discovery process to learn more about the manufacturer’s apps and how people used them. The team suggested a network segmentation strategy after completing that assessment.

This change allowed the company to go from more than 1,600 security policies to 234. Network segmentation also made it easier to stay compliant and be more proactive about cybersecurity.

Industry: Manufacturing

Network segmentation provider: Burwood Group

Outcomes:

• Decreased security rules while reducing vulnerabilities
• Improved the company’s cybersecurity posture
• Minimized overall business risks

5. Children’s Mercy Kansas City

Children’s Mercy Kansas City is a 700-bed medical facility with a growing and varied collection of connected medical assets.

People also collaborated with multiple departments but lacked cohesive data security policies to follow when doing so.

Leaders chose Medigate by Claroty to enhance network segmentation capabilities and accommodate rapid growth. The product gave a risk-scored asset inventory to show people vulnerabilities within the facility’s connected devices.

“Medigate has been a necessary investment,” says Tarunjeet “T.J.” Mann, chief information security officer, Children’s Mercy Kansas City.

“They have provided the means for us to protect and monitor every connected device in a hospital at machine speeds.”

The solution also auto-generated security policies for each network segment, reducing potential threats and giving people better network oversight.

Industry: Health care

Network segmentation product: Medigate By Claroty

Outcomes:

• Better asset visibility
• The elimination of numerous manual and outdated workflows
• More effective collaboration among staff

Bottom Line

These case studies show examples of how network segmentation is being used in various industries: IT services; oil and gas; financial services; manufacturing; and health care.

Clients selected a range of providers serving the network segmentation market for implementations: Illumnio; Champion Technologies; VMware; Burwood Group; and Claroty.

Together, the organizations’ network segmentation solutions improved numerous aspects of their networks:

• Provided flexibility across cloud and on-premises data
• Stopped a known data loss problem
• Better security against unknown traffic
• Decreased security rules while reducing vulnerabilities
• The elimination of numerous manual and outdated workflows

See more: 5 Top Network Segmentation Trends

At a high level, NDR tools examine traffic for unusual or unexpected traffic and network behaviors that could indicate an imminent cybersecurity attack or data breach. NDR provides enterprises with the ability to broadly analyze network threats originating from many sources, including those that have no previous signature, including those appearing in cloud environments.

What Technology Is Used For NDR?

NDR products can utilize multiple technologies to analyze network traffic, but most frequently, machine learning and behavioral analytics. These technologies continuously analyze raw traffic and flow records to create models (or a “baseline”) of expected network behavior.

When NDR detects anomalous, unexpected network activity that goes against this expected baseline, these systems respond by transmitting a flag to network security teams for review. Depending on how filters are set up, the potentially analogous network traffic is either blocked or allowed to pass through and restricted or permitted after analysts review alert flags.

It is important to distinguish NDR as a network security tool from more traditional rules-based network security approaches like standalone SIEM (security information and event management), which strictly rely on predetermined rules.

Modern NDR analyzes raw network traffic logs versus “looking back” at the traffic that has already come across the network — as a result, modern NDR as a standalone product or used in conjunction with legacy network security tools can provide much more comprehensive coverage. NDR can also gather network traffic data from existing network infrastructure, including firewalls.

Some of the most noted NDR technologies:

• Darktrace
• Vectra AI
• Cisco Stealthwatch
• Awake Security Platform
• ExtraHop Reveal(x)
• Blue Hexagon
• RSA NetWitness Network
• IronNet IronDefense

What Is The Environment Of Network Detection And Response Software?

NDR is well-suited for enterprise networking environments, including those that serve a distributed workforce across multiple locations. NDR helps to centralize and manage the unwieldy task of monitoring huge amounts of network traffic flowing in and out of an enterprise network at lightning-fast speeds.

Typically, NDR software is installed at the local level but managed cybersecurity providers are increasingly offering “as-a-service” products that are hosted and managed remotely. In either case, SOC teams must be able to respond to alerts and make or recommend frequent adjustments to NDR settings.

NDR Software Core Functionality and Benefits of NDR Software

At its heart, NDR is intended to further protect enterprise networks that are already being monitored and protected in other ways. NDR is rarely used on a standalone basis — instead, it is a core component of a unified network security approach that adds technology like machine learning and other AI-driven enhancements to the mix.

Advanced NDR solutions give enterprises insights into network traffic not available through traditional security tools, from all directions, not just ingress and egress traffic. In effect, NDR can detect anomalous network traffic behaviors that remain inside a network, too, as well as traffic entering and exiting cloud environments.

True NDR can be an improvement over NTA tools that trigger an excessive amount of false positive flags. Enterprises may find it is worth the investment to partner with a company that has the capability and knowledge to access advanced AI technology, which is better able to sift true threats from likely false positive threats. This can be a marked advantage for SOCs where analysts are spending precious time sorting through mountains of false positive flags.

One significant benefit of bringing an NDR solution on board is its ability to help protect against ransomware, which has emerged as one of the biggest, most difficult-to-overcome cyberthreats of this century. Today’s ransomware attackers don’t even need to be tech-savvy to deploy attacks, thanks to the advent of Ransomware-as-a-Service (RaaS).

Ransomware attackers can also easily leverage AI to overcome various network security protections. A system that can establish a baseline of expected network behavior and then compare any network traffic against it has a significantly higher chance of overcoming and preventing ransomware in general (though no current product on the market can claim to completely eliminate this threat).

While most NDR products fall short of providing authentic real-time protection, near-real-time NDR is becoming the norm.

Bottom Line

Modern enterprise network security teams face a cyber security landscape where sophisticated attacks are constantly being refined by bad actors who are often well-versed in the latest tools available on the market. Enhanced NDR is much more robust than legacy tools leftover from years past and may well be an appropriate investment for future-facing enterprises, especially those with goals to scale in the coming years. These tools can be quite challenging for cybercriminals to overcome, making it all the more likely that a bad actor moves to an easier target.

Enterprises relying on legacy tools may not need to start from scratch in order to take advantage of the benefits of NDR. Many tools can be used in tandem with older systems, including those with on-premise hardware connected to cloud environments. These hybrid setups may benefit the most from the addition of complementary NDR.

Web application firewall technology is a critical part of a company’s cybersecurity efforts. To help organizations keep up with this long-standing solution, here are some of the top trends in the web application firewall market:

1. WAF Growth Surge

Web application firewalls may not be the most cutting-edge technology around. Yet, they continue to play a vital role in enterprise storage and represent a high-growth segment of the security market. Market research numbers show demand for overall firewall solutions, including WAFs, with 14% annual growth, according to Dell’Oro Group.

“Firewalls are foundational to good enterprise network security hygiene, and we do not foresee any solution fully displacing them over the next five years,” said Mauricio Sanchez, an analyst at Dell’Oro Group.

Sanchez pointed out that web application firewall revenue surged by over 30% during 2022 and estimated annual revenue in excess of $2 billion for the year. Three top WAF vendors, Akamai, Cloudflare, and F5 Networks, now represent over half the market by revenue, according to Dell’Oro Group.

See more: 5 Top Firewall Trends

2. WAFs No Longer Enough

While a WAF remains an important security tool, it relies on signatures to identify and block suspicious activity, according to Pete Klimek, director of technology, office of the CTO, Imperva.

For most digital businesses, this is not enough to stop the growing number of automated and complex security threats. Automated fraud, business logic attacks, and other forms of API abuse don’t rely on known attack patterns, making them difficult for a web application firewall to identify and block.

Further, as businesses leverage the cloud, applications grow more complex and monolithic applications have decomposed into APIs, microservices, and serverless functions. In addition, many web application firewall offerings are challenging to deploy in hybrid or cloud-native environments.

As a result, organizations are now looking to invest in web application and API Protection (WAAP), said Klimek. With a unified, single-stack approach, a cloud-based WAAP provides multiple layers of security in the forms of WAF, API security, distributed denial of service (DDoS) protection, and advanced bot protection.

‘WAAP can be deployed in nearly any environment and equips security teams with a singular view of their attack landscape, giving them the ability to identify initial signs of malicious behavior and mitigate multi-vector attacks,” Klimek said.

3. Identity-Based Approach

Mike Kiser, director of strategy and standards at SailPoint, concurs that web application firewalls are no longer enough.

Kiser regards them as a barrier to entry for enterprises. Organizations have been combining web application firewalls with other protections that are edge-focused, such as bot mitigation and API security, he said. Ultimately, these capabilities can help protect applications, but he believes their impact is limited on their own. Design-level choices must be made to adequately protect the identity-centric security model of the application layer.

“This is most effectively accomplished through a consistent approach to identity: limiting the impact of a compromised account, being able to detect strange user behavior and lateral movement, and being able to govern the use of identity with an audit trail are key,” Kiser said.

See more: 10 Best Identity and Access Management (IAM) Solutions

4. WAF Sophistication

Michael Tremante, product manager at Cloudflare, has a slightly different take. He thinks web application firewalls are gradually becoming more sophisticated, and computationally expensive, anomaly detection systems.

His rationale? Traditional attacks are well understood and although still very much used by attackers, process flow anomalies both in end user and API-based interfaces are a recent focus point.

For example, automatically being able to detect and alert on whenever a user has performed an online banking currency transaction outside of normal expected steps/time taken. in real-time, not doing log post-processing. Doing this at scale is the next challenge being solved.

“More sophisticated attackers and bots are driving the barrier higher,” Tremante said.

“Traditional on-premise WAFs are not able to handle these detections. For large environments that have more data, it’s expensive. Native cloud-based WAFs are better suited, assuming they have the technology in place, to sustain this demand.”

5. Don’t Forget Patching

Application firewall attacks are very common to large and small businesses for a variety of reasons.

While a web application firewall acts as a proxy, which manages the traffic between an application server and its clients, attackers have become smarter and more proficient. They routinely look for vulnerabilities in this space.

Robert Anderson Jr., Chairman and CEO, Cyber Defense Labs, said that cybercriminals continue to exploit unpatched systems, including unpatched firewalls and web application firewall software.

“It is important to automate and customize patching for Windows, macOS, and Linux and everything else,” Anderson said.

“As companies still do not make sure all the patches have been taken and are fixed, they continue to suffer from large-scale ransomware and intellectual property (IP) theft.”

See more: Top 12 Web Application Firewall (WAF) Solutions

The field of vulnerability management includes automated scans, configuration management, regular penetration testing, patching, keeping track of various metrics, and reporting. The category has been evolving rapidly within cybersecurity, and here are some of the top trends in the vulnerability management market:

1. More Than Scans

Vulnerability management is all about identifying, prioritizing, and remediating vulnerabilities in software.

As such, it encompasses far more than the running of vulnerability scans repeatedly to look for known weaknesses lurking within the infrastructure. Traditionally, vulnerability management also includes patch management and IT asset management. It addresses misconfiguration or code issues that could allow an attacker to exploit an environment as well as flaws or holes in device firmware, operating systems, and applications running on a wide range of devices.

“These vulnerabilities can be found in various parts of a system, from low-level device firmware to the operating system all the way through to software applications running on the device,” said Jeremy Linden, senior director of product management, Asimily.

See more: A holistic approach to vulnerability management solidifies cyber defenses

2. Vulnerability Management Broadens

Some analysts and vendors stick strictly to the NIST definition when they’re talking about vulnerability management. Others include security information and event management (SIEM) with vulnerability management as part of larger suites. And a few combine it with threat intelligence, which prioritizes actions and helps IT to know what to do and in what order.

Gartner recently originated the new term attack surface management (ASM). The analyst defines ASM as the “combination of people, processes, technologies, and services deployed to continuously discover, inventory, and manage an organization’s assets.”

ASM tools are said to go beyond vulnerability management. The aim is to improve asset visibility, understand potential attack paths, provide audit compliance reporting, and offer actionable intelligence and metrics.

3. Vulnerability Management as a Service

The as-a-service trend has invaded so many areas of IT, so it’s no wonder that vulnerability management as a service has emerged.

“With more than 20K vulnerabilities found and published in a single year, vulnerability management has become an enormous task,” said Michael Tremante, product manager, Cloudflare.

“This is made worse for large enterprises who also have the challenge of not necessarily knowing the full set of software components being used internally by the organization, potentially putting the company at risk. A big trend is adoption of managed services/SaaS environments, as they are externally managed, and offloading of vulnerability management to third parties.”

Thus, a growing set of products are hitting the market that help companies tackle vulnerability management via managed services of one kind or another.

See more: Vulnerability Management as a Service (VMaaS): Ultimate Guide

4. Container Vulnerabilities

The container security market is growing steadily. It is expected to be worth more than $2.5 billion by 2025, according to analyst firm KuppingerCole.
Containers and Kubernetes have become largely synonymous with modern DevOps methodologies, continuous delivery, deployment automation, and managing cloud-native applications and services.

However, the need to secure containerized applications at every layer of the underlying infrastructure — from bare-metal hardware to the network to the control plane of the orchestration platform itself — and at every stage of the development life cycle — from coding and testing to deployment and operations — means that container security must cover the whole spectrum of cybersecurity and then some, said KuppingerCole.

Vulnerability management platforms are gradually adopting features aimed squarely at containerized environments. Several vendors have announced new container vulnerability scanning and vulnerability management features. Expect these to become a barrier to entry in the near future.

See more: Securing Container and Kubernetes Ecosystems

5. Autonomous Endpoint Approach

Due to the way the threat landscape is evolving, the way vulnerability management platforms are shifting, and the fast pace of innovation as evidenced by containerization, digitalization, and the cloud, a new approach is needed, according to Ashley Leonard, CEO, Syxsense.

“Businesses possess incredibly powerful processors inside storage equipment, servers, and desktops, which are underutilized in many cases” Leonard said.

“Many of the tasks managed today by the cloud could be better performed at the endpoint — and we will begin to see some functions decentralized onto endpoints to take advantage of this untapped compute potential.”

For example, Syxsense has been incorporating more features into its vulnerability management tools. This includes more orchestration and automation capabilities, stronger endpoint capabilities, and mobile device management. These augment existing patch management, vulnerability scanning, remediation, and IT management capabilities.

See more: 12 Top Vulnerability Management Tools

A vulnerability is defined by the International Organization for Standardization (ISO) 27002, as “a weakness of an asset or group of assets that can be exploited by one or more threats.” Threats are defined as whatever can exploit a vulnerability, and damage can be caused by the open vulnerability being exploited by a threat. Here are some of the top trends in the vulnerability scanning market:

1. Government Warning

The importance of vulnerability scanning was underscored in a recent directive by the Cybersecurity and Infrastructure Security Agency (CISA) of the FBI.

The directive made it mandatory for government entities to do continuous vulnerability scanning on all network appliances. They have been given until April 3, 2023 to comply.

They are required to list any vulnerabilities found across all assets running on their systems. This has to be done every 14 days, and scanning should be done regularly within these 14-day windows. Further, all vulnerability detection signatures used by these agencies are to be updated at an interval no greater than 24 hours from the last vendor-released signature update. Mobile devices are included in these requirements.

Clearly, government systems have suffered badly due to undetected and un-remediated vulnerabilities. Enterprises and SMBs are no different. They would do well to heed these CISA directives.

2. Constant Alertness

Robert Anderson Jr., chairman and CEO, Cyber Defense Labs, believes vulnerability scanning has not been thorough enough in the enterprise.

While vulnerability management is supposed to be constantly looking at and protecting all endpoints, workstations, laptops, servers, virtual machines (VMs), web servers, and databases, Anderson said that most companies only cover what they deem is important.

“Companies need to constantly be looking for vulnerabilities that may be used as an attack path by an adversary,” Anderson said.

“Continual scanning is now being utilized by most large companies that we partner with. The need for unified and constant visibility of your distributed IT network irrespective of endpoints is imperative in today’s cyberthreat environment.”

3. Golden Oldies

Zero-day attacks get the lion’s share of attention — and understandably. After all, they represent newly discovered vulnerabilities and exploits for which there is currently no remedy, although their publication means remedies will be issued rapidly.

Yet, well-known and sometimes quite old vulnerabilities continue to exist in many enterprises.

For example, Log4J has been well known for more than a year. Yet, cybercriminals continue to exploit it.

“As the Log4j vulnerability shows, discovering, mitigating, and fixing vulnerabilities as soon as possible is more important than ever to good cyber hygiene,” said Michelle Abraham, an analyst at IDC.

“Leaving vulnerabilities without action exposes organizations to endless risk, since vulnerabilities may leave the news but not the minds of attackers.”

Unpatched vulnerabilities even older than Log4j are lurking inside many companies. Some as far back as a decade old. When cybercriminals find these, they know they have an easy route into the enterprise. Vulnerability scanners need to be employed to find these, and organizations need to ensure they are patched immediately.

See more: Cybersecurity Agencies Reveal the Top Exploited Vulnerabilities

4. Update Your Vulnerability Databases

Part of the solution to not missing aging vulnerabilities is to ensure vulnerability scanners use a database of known issues to look for vulnerabilities, misconfigurations, or code flaws that pose potential cybersecurity risks.

Further, that database needs to be complete and regularly updated.

Popular scanners are missing at least 3.5% of all ransomware vulnerabilities, according to the Ivanti ”Ransomware Report.” As well as keeping databases and vulnerability signatures up to date, some recommend using multiple scanners.

5. Include Penetration Testing

Vulnerability scanning is essentially a process of checking out where weaknesses may lie by assessing internal systems, applications, misconfigurations, and cloud dependencies.

Penetration testing takes a different approach. It is generally accomplished by ethical hackers who try to penetrate the network, find holes, and exploit known or unknown vulnerabilities. More organizations are supporting vulnerability scanning with pen testing to ensure they find everything.

For those that lack internal resources, vulnerability scanning and penetration testing are now available as a service. This is a growing trend. Penetration testing-as-a-service (PTaaS) platforms have emerged that remove the burden of testing from IT or the need to hire outside hackers.

See more: 5 Top Penetration Testing Trends

6. Personal Information

Personally identifiable information (PII) is very much in the spotlight. Cybercriminals seek it, as it provides them with data they can sell, compromise, or use to hack into systems and scam people and organizations.

Similarly, organizations are constantly looking for PII, so they can ensure it is protected and they don’t fall afoul of privacy and compliance mandates. Accordingly, vulnerability scanners are emerging that look for PII as well as vulnerabilities.

“As the awareness of better privacy for customers’ sensitive data is rising, so does the number of solutions that help gain insights around privacy posture using scanning tools,” said Gil Dabah, co-founder and CEO at Piiano.

“Vulnerabilities recognized by scanning tools are including additional findings that are privacy related.”

Imagine a company with hundreds or thousands of developers that decide to harden the security of the PII they are collecting to decrease the risk of data exfiltration due to a breach. Such a task, when done manually, can take weeks.

With code scanning tools, a team can get a list of all PII the organization collects, verify that the data you collect is aligned with your privacy policy, and better protect high-risk PII, such as SSNs. New tools help find PII, but they also give you insights regarding where you collect each PII, what processes you are doing with the data, and where you store it.

7. SBOMs

A software bill of materials or SBOM is an inventory of ingredients that make up different software components. They are being used to be able to drill down into exactly where vulnerabilities may lurk.

Take the recent Log4j vulnerability. As it related to Java libraries, few realized how pervasive those libraries were. Organizations thought they have patched or addressed all needed areas to combat Log4J. Yet, there were more hiding in all sorts of nooks and crannies of the enterprise. SBOMs make it easier to know what contains which software elements, so it is easier to address vulnerabilities.

“The move toward automated, formally structured, machine-readable SBOMs is clear,” said Alex Rybak, senior director of product management, Revenera.

“More and more software companies expect SBOMs to include all third-party, including open-source and commercial, software that’s used in their applications. An SBOM that provides a single, actionable view is essential, so that when a vulnerability is detected, the supplier can quickly assess the impact to their portfolio of applications and expedite remediation plans.”

8. Supply Chain Attacks

Major cyberattacks have made it clear that vulnerabilities within the software supply chain were a vital element of security scans.

Cyberattackers gained a foothold by exploiting an outdated build server with a known vulnerability. Since those well-publicized breaches, further examples include RCE vulnerability (CVE 2021-22205) and dozens of vulnerable Jenkins plugins. They demonstrate the importance of securing development tools and their ecosystems.

“Organizations have expanded their vulnerability scanning efforts from COTS, cloud, and source code to include the software delivery pipeline itself,” said Andrew Fife, VP of marketing, Cycode.

“While much of the hype around software supply chain attacks has been directed at traditional software composition analysis, which focuses on the delivered application, the reality is that the majority of attacks start elsewhere.”

9. Scanning for BEC And BAC

Business application compromise (BAC) is where cyberattackers target cloud access identity providers, like OKTA or OneLogin, that are often used by business applications to provide a single sign-on (SSO) experience to users.

Attackers compromise the user OKTA login via phishing and overcome multi-factor authentication (MFA) by brute force, pushing notifications in the hope that the user accidentally approves one of them.

Business email compromise (BEC) most often happens in Microsoft 365. Criminals send an email message that appears to come from a known source making a legitimate request. With original deployments of Office 365 tenants, Microsoft by default enables IMAP and POP3 in O365 Exchange as well as BasicAuthentication. IMAP and POP3 don’t support MFA, so even if you have MFA enabled, attackers can still access these mailboxes.

“Disable legacy protocols, like IMAP and POP3, immediately, especially if you’ve gone through the process to enable MFA,” said A.N. Ananth, president and chief strategy officer, Netsurion.

“Once you turn those off, strongly consider disabling BasicAuthentication to prevent any pre-auth headaches on your Office 365 tenants.”

To address BAC, Ananth said to be alert for multiple identity provider sessions from the same user with multiple, non-mobile operating systems. Alert for potential brute force push requests. As a result of this type of threat, scanners are now checking for such vulnerabilities.

See more: Simple Guide to Vulnerability Scanning Best Practices

10. Automated Remediation

The norm has long been that multiple tools are needed to bridge the scanning and remediation gap. Scanners find out what might be wrong. Other tools, and plenty of manual effort, are required to address the problems and safeguard the enterprise.

But that is changing according to Ashley Leonard, CEO of Syxsense. His company, for example, offers a single agent that automates the management of endpoints and reduces the attack surface.

“We are seeing solutions hitting the market that combine the necessary functionality to remediate threats that are blended: threats that require the application of a patch as well as configuration changes,” Leonard said.

“This ties in with threat prioritization whereby both patch and security threats are given different levels of risk based on the specifics of their environments. And finally, we are seeing software designed to bring about intelligent endpoints that can automatically maintain an endpoint in a desired state.”

See more: 22 Best Vulnerability Scanner Tools