The Evolution of WAFs

Web application firewalls (WAFs) have been around for a while, and they have evolved from basic network-protecting networks to more adaptable, integrated solutions. They are essential components of fully integrated security systems, and as part of these systems, they are highly effective at blocking attacks. Recent developments in AI have made WAFs increasingly sophisticated, further contributing to your application security.

With the development of AI and its increasing accessibility, many attacks are becoming more difficult to detect. However, if an attacker can use AI to avoid your security tools and exploit your vulnerabilities, you can use it to inform your Web Application Firewall. AI can make your WAF more sensitive to unusual activity patterns, more adaptable, and better able to block threats.

The Origins of the WAF

WAFs began as simple, rule-based systems designed to identify certain threats like cross-site scripting and SQL injection attacks. When packets or requests come in from the web, the WAF has traditionally relied on rules based on known attack patterns to filter and block illegitimate traffic.

While traditional firewalls perform a similar purpose, the WAF fills a critical role in contemporary security infrastructure. Traditional firewalls are typically hosted locally to protect a network or group of devices in a single office or other environment.

As cloud deployments and integrations became more common, WAFs, which are typically cloud-hosted, also became more common as they could protect a web application more effectively than a traditional firewall. They are now one of the top security options for web applications.

The Evolution of the WAF

To keep ahead of the curve, however, WAFs have continued to evolve. WAFs have incorporated machine learning and are now starting to use AI for improved detection and efficiency. As web traffic grows (and bot traffic makes up an increasingly large percentage of it), WAFs must be able to efficiently shut down threats.

However, the risk of false positives means that WAFs must be highly accurate. Without interfering with legitimate traffic, they need to be able to detect and block known patterns of suspicious activity as well as potential zero-day attacks. This is a tall order given that zero-day attacks are not a strength of rules-based WAFs.

This is where AI and machine learning come in. To increase the likelihood that threats will be detected, some WAF solutions now use machine learning to improve adaptability and sensitivity to evasive bots. These solutions can make real-time rule changes to better respond to threats.

Additionally, the influence of AI and machine learning have enabled WAFs to improve their capabilities in the following ways:

  • Better databases. Traditionally, WAFs relied on known attack patterns to block unusual activity. With AI, WAFs are able to store and analyze information on potential attack signatures. Things like IP addresses and unusual requests or responses could indicate suspicious traffic, so WAFs now flag these.
  • Profiling. Using behavioral analysis, WAFs can now quickly identify when a traffic pattern is atypical for an application’s users. WAFs also profile applications to determine normal activity patterns and correct inputs. So, even if unusual activity doesn’t match a pattern of a known attack, the WAF can still block activity that doesn’t match typical or appropriate use.
  • Speed. Firewalls have historically relied on local hosting, but WAFs protect cloud-based assets. So, WAFs can also be based in the cloud. There are a few advantages to this, and one of these is speed. WAFs can sometimes slow down traffic, which can be frustrating to legitimate traffic, but integrating content delivery networks (CDNs) can mitigate this problem. CDNs send responses to users from the closest data center.
  • Flexibility. Cloud hosting and increased adaptability from AI gives WAFs more flexibility than they once had. WAFs can be combined with DDoS protection so that an application is not overwhelmed by traffic during a DDoS attack. More customization of rules and policies is also possible.

The Future of the WAF

As AI advances further, it can enhance WAF security, but it will also create new security threats for WAFs. Attackers will use AI to create more sophisticated and evasive bots, among other threats. Fortunately, even as attackers built sneaker bots, you can use AI to better arm your WAF.

The essential advantage of a WAF is that it keeps malicious traffic outside of your application or network. So, malicious traffic generally is blocked from accessing the application. This provides a layer of insulation from these threats, so although attackers will also likely be using AI to find your vulnerabilities, using AI and machine learning to bolster your WAF will go a long way toward mitigating the threat.

Even if attackers are able to use AI to create high-level, zero-day threats, your AI-informed WAF should be equipped to keep up. Additionally, increased security tool integration and broad AI use is likely to increase WAF efficacy. Fully integrated solutions that include RASP, DDoS protection, analytics, and other tools are your best bet for highly effective application security.