In the competitive IT environment companies are forced to run faster to survive. Frequent software updates often conflict with security audit processes, which in turn may result in missed software vulnerabilities.
To help businesses protect their web applications and APIs, Wallarm introduces a unique approach to revealing security flaws. It uses machine learning to analyze the normal user behavior of the web application, block abnormal user requests, and see if these requests expose vulnerabilities.
By creating a profile of what legitimate behavior looks like, Wallarm can easily distinguish between regular user activity and malicious attacks from hackers. When Wallarm detects behavior that falls outside of a normal user profile, it will automatically block the user.
Additionally, many security products available on the market can only alert security teams of any malicious behavior detected, but are unable to determine which ones are critical. Contrary, Wallarm is able to pinpoint attacks by blocking the malicious user and then testing it against the web application to see if a critical vulnerability exists. If it does, Wallarm alerts the security team immediately, helping them prioritize which security issues to focus on. This makes all the difference for large companies facing thousands of attacks a day. For more details, please visit Wallarm’s website.