By combining traditional security measures with artificial intelligence (AI) and machine learning (ML), security teams have been able to improve their responses to attacks, decrease illegitimate traffic, and lower the risk of a security breach within web applications. One of the tools effectively evolving with the help of is the WAF.
A Web Application Firewall (WAF) traditionally involved security professionals creating rules and policies to govern web traffic, but contemporary versions utilize AI and machine learning to improve and add to the original policies. This allows the WAF to be more efficient and independent, thereby improving its ability to prevent and respond to cyberattacks.
What is a WAF?
A web application firewall (WAF) filters malicious traffic to prevent it from accessing your website or web apps. As a barrier between the web and the application, a WAF intercepts all traffic, using pattern recognition and rules to distinguish between legitimate customer traffic and illegitimate traffic or bots. WAFs are also responsible for stopping unauthorized data exfiltration by identifying malicious traffic and preventing that traffic from returning with your data.
WAFs are very effective at detecting known attack types, and they are an essential part of web application protection. However, as cyberattacks become more complex, a basic WAF is no longer enough. As a result, many contemporary WAF solutions have begun implementing automation, AI, and ML to improve accuracy and efficiency. While security professionals in the past would have needed to manually update rules periodically, newer WAFs are increasingly independent.
The Need for AI in the Modern WAF
Cyberattacks are rapidly evolving, making it challenging for security professionals to keep up. Traditional WAFs that do not use AI and ML still require substantial input from security teams, which can slow down response times and overextend the teams responsible for managing the environment. Adding AI and ML to the WAF solution helps identify sophisticated, subtle, and rapidly-evolving attacks quickly and accurately. Although security professionals are still able to monitor and manage traffic, create rules and policies, and take action during attacks, they can depend on the WAF to address issues without constant supervision.
By leveraging AI and ML, the WAF solution can accelerate response times and improve the likelihood of sophisticated attack detection. The AI built into a WAF can react more quickly than a security team by automatically establishing precise, accurate rules and policies when novel attacks occur. Because of the machine learning component, the modern WAF is able to improve its rules and responses over time based on data and performance scores from past traffic. Security professionals can contribute to training the automated WAF solution to monitor traffic and keep tabs on the whole environment. Rather than getting bogged down in combating each new attack, the human team can focus on fine-tuning and improving overall security.
AI can also be used to test a WAF’s defenses with novel attacks. It can be difficult to determine, prior to an attack, what a novel attack might be and where the WAF’s weaknesses are. Much like traditional penetration testing, AI can be used to put pressure on weak points in security to determine where the WAF could fail and what sorts of novel attacks could slip past web application defenses. The WAF’s machine learning component can then analyze that data and make any necessary adjustments.
Staying Ahead of Cyber Threats
To stay ahead of attackers and threats, your organization needs an AI-enhanced WAF. Alone, security teams and traditional firewalls are not able to keep up with the rapidly changing attacks; however, AI and ML can independently keep your WAF ahead of the game without substantial human intervention. The WAF trains itself and automatically implements rules based on previously established policies and feedback, which is much more efficient than your security team manually implementing new rules every time a novel attack is discovered.
Without an effective WAF solution, applications are vulnerable to attack, which could involve access challenges for customers or data breaches. Differentiating between legitimate and illegitimate traffic is essential for organizations utilizing web applications, as is minimizing false positives generated by the WAF. When this error occurs, the WAF blocks legitimate traffic, which may cause your company to lose a sale or customer. Implementing an AI-enhanced WAF can help solve this problem. AI-informed WAFs are far less likely to incorrectly label traffic and will keep your operations running smoothly.
A WAF is an essential part of keeping cyber threats at bay. Whether you need it for keeping malicious attacks out or keeping your application available for legitimate users, the AI-informed WAF will automate the filtering process, allowing security teams to focus on other vulnerabilities and infrastructure projects rather than creating rules and policies for web traffic. Ultimately, this leads to faster response times, more precise results, and improved overall security.
