Hey everyone! I've been deep in the weeds building out some automated triage workflows, and I keep hitting the same big question: how do you *really* know if your AI agent's judgment is sound before you let it loose on live, potentially critical alerts? It's one thing to have it parse data, but another entirely to trust its decisions to escalate, close, or initiate a response.
I'm thinking we need a solid testing "sandbox" phase. Here's what I've been experimenting with:
- **Historical Playback:** Feed it a curated set of past alerts with known outcomes. Compare its proposed actions against what human analysts did. The key is mixing in false positives and subtle threats.
- **Adversarial Simulation:** Craft new test cases that probe edge scenarios—like subtly malicious PowerShell commands buried in normal-looking logs, or tricky phishing links.
- **Judgment Logging:** Before any production connection, have the agent output its reasoning, confidence score, and recommended action into a separate log for every simulated alert. Human review of this log is the final exam.
But I'm curious about the *practical* side. How do you structure this? Do you run it in parallel with your existing SIEM/SOAR for a period, silently comparing outputs? What metrics do you track besides accuracy? (e.g., time saved vs. risk of a missed true positive).
Also, what about testing the "agentic" part—like when it decides to run an enrichment script or isolate a device? That feels like a bigger leap.
Would love to hear how others are tackling this, especially if you've built something in-house or are using a vendor's testing framework. Any war stories or checklists you swear by?
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