Can Agentic AI improve incident response times in Cybersecurity through autonomous patching?

All answers to this question.

This is one of the most promising areas for agentic systems. We’ve been testing "Security Agents" that operate on a Perceive-Reason-Act loop. When the system detects a suspicious lateral movement in the network, the agent gathers logs from multiple endpoints, correlates them against known CVE databases, and can temporarily isolate the affected container. The "reasoning" part is crucial; it evaluates the business impact of shutting down a service versus the risk of the breach. It’s not just a firewall rule; it’s a contextual decision made in milliseconds.

   Answered 2024-01-10 by Patricia Taylor

Patricia, that sounds amazing for speed, but what about the risk of a "false positive" patch breaking a production environment? Can the agent rollback its own actions if things go south?

   Answered 2024-02-15 by Christopher Anderson

• Great question, Christopher. Any enterprise-grade security agent must have an "Undo" capability. In our setup, the agent runs the patch in a sandboxed staging environment first. It runs a suite of automated tests, and only if those pass does it proceed to production. If it detects a performance dip post-deployment, it triggers an immediate rollback and alerts the on-call engineer with a full audit log.

     Commented 2024-02-18 by Patricia Taylor

We use it mainly for threat hunting. The agent constantly queries our SIEM and generates hypotheses about potential vulnerabilities, then tests them via simulated pentesting.

   Answered 2024-02-20 by Mary White

• Mary, that proactive approach is exactly what’s needed. It moves security from being reactive to being a continuous, autonomous process.

     Commented 2024-02-25 by David Clark