Continuous Feedback Loops: Why Training Your AI-SOC Doesn’t Stop at Deployment
Summary: A pre-trained AI-SOC model degrades after deployment because it is built on historical data and lab assumptions, so it flags a traveling CEO's logins as threats and lets real threats slip through. The article argues that continuous feedback loops, in which each analyst correction becomes a labeled data point in supervised retraining, are what keep the model aligned with the environment. It cites false positives in an affected category dropping by 60 to 70% within the first feedback cycle and analyst participation staying near 100% when improvements are visible.
What you should know: A static model forces analysts to suppress the same noise and track the same 'known safe' patterns repeatedly, turning the AI-SOC from an assistant into a bottleneck and extra overhead. If analysts cannot see that their corrections changed the system's behavior, they stop providing context, and trust erodes faster than performance can recover.
Read the original at The Hacker News ↗
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