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AI cybersecurity has become an essential tool to help organizations defend against attacks and protect data. It automates, prioritizes, and scales security operations in the face of a talent shortage and data silos. It also helps detect, investigate, and respond to cyberattacks in real time.

When selecting an AI cybersecurity solution, assess the vendor’s ability to integrate seamlessly with your existing security stack, such as firewalls and SIEMs. A lack of integration can lead to visibility gaps, making it hard to get a full picture of an incident and make the right response decisions.

Look for AI solutions that can search and analyze vast volumes of data and network traffic, detecting and understanding new threats in ways traditional point systems cannot. Detection models use deep learning to understand what normal behavior looks like for your business, and flag deviations from this baseline. For example, a detection model may flag a user suddenly pulling ten files from a single S3 bucket at 3 a.m., a behavior that a signature would miss because it isn’t an attacker known pattern.

A strong AI tool should be able to triage alerts at machine speed and minimize noise, allowing it to rapidly detect and prioritize high-priority incidents. It should also allow you to adjust the sensitivity of its detection models, so it can more accurately balance the tradeoffs between false positives and missing real threats. Ask vendors for a proof of concept that you can run in your environment to test performance claims and ensure it’s a good fit for your specific systems, threat landscape, and organizational processes.