AI Agents Automating SOC Chores

This shows where security teams trust agents first, on noisy work with clear feedback loops, not on judgment calls. The first jobs moving to agents are the chores that swamp Tier 1 analysts, sorting duplicate alerts, flagging false positives, reviewing scanner output, and drafting fixes in code workflows. Human approval stays in place where a wrong call can close a real incident, miss a real vulnerability, or trigger the wrong remediation.

• The rollout path is narrow and practical. In the interview, the stack is built on top of Splunk, Jira, GitHub, and OpenAI APIs, with every agent action logged. That means agents are being inserted into existing analyst queues and ticket flows, not replacing the SOC with a new system.
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• Code scanning is a useful comparison because it shows the same control model. GitHub Copilot Autofix can suggest remediations for CodeQL findings, but it generates proposed fixes for review rather than silently changing production code. Security teams are applying the same pattern to SOC and vulnerability work.
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• Vendors are already productizing this wedge. Sublime Security ships autonomous phishing triage and detection engineering agents, while Splunk is adding triage agents and AI authored playbooks into the SOC workflow. The common theme is faster filtering and investigation before fully automated response.
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The next step is selective autonomy on low risk actions like closing duplicate alerts, closing obvious false positives, and generating first pass remediation steps. As audit trails improve and teams build confidence from historical outcomes, security operations will move from human reviewed recommendations to tightly scoped autonomous actions inside existing SOC and developer tools.