Scalability : Most companies anticipate that their data volumes will grow. Therefore, the WAF they use should scale proportionally to the expected data growth in order to maintain the required throughput in the future.
In addition to the cost of handling false positives when using the application behavior learning mechanism, WAF users must consider the ease of use of the solution.
Reporting and Regulatory Compliance: WAF reporting must comply with applicable regulatory requirements and security standards.
Artificial Intelligence in WAF
To detect not only known exploits but also zero-day vulnerabilities, a WAF must use AL-based protection mechanisms that compare user and application actions with expected ones and identify anomalies in them.
Solutions that use such technologies monitor kenya whatsapp data responses to certain impacts over time, extrapolate the accumulated data, and “learn” to “understand” what acceptable responses to expect in the future. Anomalous behavior can lead to blocking incoming traffic to a web application.
Problems with using AL . The problem with using a WAF with AL capabilities is that any behavior that does not fit the profile developed by the WAF (in other words, any behavior that it has not previously observed) results in an excessively high level of false positives when detecting threats. Application traffic may be blocked until the situation is manually assessed by specialists.
Benefits of AI-powered machine learning. AI-powered machine learning allows WAFs to view deviations from normal user and application behavior not as a cause for immediate alarm, but as context for a more detailed examination of the anomaly that has occurred. Alerts are not assessed on a “is there a violation” or “is there a violation” basis — they inform an automated probability calculation tool that confirms or does not confirm that the user or application behavior poses a threat that requires a response.