A Web Application Firewall (WAF) is a security tool that provides visibility into network traffic and blocks malicious traffic. It enables security teams to control what enters a network. The Next-Generation Firewall has been expanding the scope of WAF protection: by inspecting and managing traffic across deeper levels. Let’s unpack how both work, and explore how a hybrid setup represents one of the best security options today.
Placing a WAF in front of a web application creates a protective barrier between the application and the Internet.
A WAF functions as a reverse proxy by routing client traffic through itself before it reaches the internal server or database. When positioned between the user and the internal server, the WAF:
The simplicity of this shows the benefits of WAF: the fact that policies can be modified quickly and easily enables a swift response to new attack methods. Today’s WAFs are often cloud-based, which makes them easier to deploy, edit, and pay for – but just because a WAF is cloud-based, it doesn’t make it next-gen.
Given the cloud-based deployment that many WAFs enjoy, they’re well-positioned to benefit from AI developments.
AI now provides a number of benefits to WAF tools, which allows AI-powered WAFs to ingest more data and uphold secure network access in new efficient ways.
Because WAFs rely so much on the rules they’re built with, they are typically highly demanding to maintain.
This is fortunately solved with the rise of NLP-driven WAF dashboards. By entering a natural language prompt, analysts can create custom or rate-limiting rules tailored to their needs. For instance, instead of manually configuring complex rule-matching criteria, they can input something like:
…and the WAF will generate the rules for them.
This creates a strong foundation for further refinement, and reduces the maintenance time demanded by traditional WAF rulesets.
The AI elephant in the room is that large AI models often rely on vast networks of scraped data. As a result, more companies are choosing to opt out of allowing bots and scrapers to access their content. A common approach is placing a robots.txt file in the website’s root directory to guide crawlers on:
However, this method is largely ineffective, as 99% of crawlers ignore the robots protocol. While WAFs are well-positioned to block bots, they require constant updating to remain effective.
AI-powered WAFs take a more advanced approach by employing behavioral analysis, a technique commonly used in Next-Gen Firewalls (NGFWs). Instead of relying solely on static rules, they monitor how a site’s legitimate users interact with elements on a page, creating a baseline for normal behavior.
When a bot or web scraper visits the site, the WAF detects unusual patterns, such as rapid site interactions or automated form submissions. Once suspicious activity is identified, the WAF can:
By leveraging AI and real-time analysis, modern WAFs provide a dynamic and adaptive defense against bots, making it significantly harder for scrapers to access protected content.
Web applications today, and the architecture they’re deployed on, incorporate many open-source modules and hybrid multi-cloud configurations. This is great for performance and cost optimization, but can drastically limit a WAF’s ability to identify and prevent advanced attacks.
NGFWs use the same reverse-proxy architecture of traditional WAFs, while combining some of the AI capabilities we just covered – chiefly by learning the normal behavior of the web application it’s protecting.
This lends a degree of application awareness to the NGFW, and is supported by capabilities like:
In traditional firewalls’ stateful packet inspection, the device only reviews basic packet header information, such as:
Deep Packet Inspection (DPI), however, goes further by analyzing a broader range of metadata and examining the actual data within each packet that the device interacts with. With DPI, an NGFW can control how specific data moves, decide on its routing paths, and manage how it is processed across the network.
This forms the foundation of intrusion prevention systems.
Combining DPI and an analytical engine, NGFWs are able to classify data based on the specific application being accessed, and the user accessing it.
Once a traffic flow is linked to a particular application, NGFWs categorize it based on various criteria:
Recognizing these risks enables organizations to apply targeted security measures based on a risk assessment of each application. As a result, NGFWs offer a higher-level overview of how policies can be used and applied.
Rather than allowing any data packets from authenticated users, NGFWs assess the risk of every request.
The differences between WAF, AI-powered WAF, and NGFW can be thought of as largely generational: they’ve all built upon the basic structure of a firewall to make it more efficient, actionable, and better adapted to today’s dynamic threat landscape.
To illustrate, let’s look at how each type of WAF handles cross-site scripting (XSS) threats.
XSS attacks exploit browser handling of site scripts, allowing attackers to execute malicious JavaScript on user devices and potentially steal sensitive information. All types of WAFs are positioned at the network edge, making them the first line of defense when a client sends data to a web application.
Traditional WAFs rely on static indicators to detect malicious scripts, such as:
However, attackers have evolved their techniques, replacing HTML script tags with alternative elements like <body> tags and onload attributes to bypass detection.
Legacy WAFs counter each new vector with pre-configured attack templates. Each new attack type demands a unique rule, placing the burden on security administrators to continually update WAFs.
NGFWs change this dynamic by comparing expected user behavior against the real-time actions being taken against a server – and a network as a whole.
Adding an AI-powered WAF into the mix leads to the best security possible.
AI-WAF’s machine learning capabilities are pre-trained on extensive attack data – it’s possible for a hybrid NGFW to automatically detect any scripts being hidden in URLs. The NGFW can then deploy an automated response, like blocking the IP address that the script originated from.
This hybrid approach is the best of both NGFW and AI-WAF capabilities – as long as the WAF tool you choose supports both.
Check Point’s CloudGuard WAF enhances web application security by blocking all OWASP threats. It offers automated, real-time threat detection with integrated threat intelligence and helps your security team to identify vulnerabilities quicker than ever before. The WAF also features adaptive security policies that adjust to changes in application behavior, reducing manual tuning and response times.
With cloud-native architecture, it’s deployable across multi-cloud environments, supporting seamless scalability and easy integration without extra hardware.
See how our application security chalks up to others on the market with GigaOm’s 2024 Report, or start exploring our highest-powered Next Generation Firewall with a demo.
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