Hey everyone, hoping to get some collective wisdom on an agent tuning problem that's driving our SOC a bit nuts.
We've implemented an AI agent (built on an open framework, not a full vendor suite) to triage PAM alerts from our identity management platform. The core logic uses an LLM-based classifier to analyze event context (user, target system, command, time) against our policies. The issue: it's consistently flagging routine, approved admin work as potential privilege escalation. For example, a standard `sudo su -` from a senior sysadmin to patch a server during a maintenance window gets escalated (no pun intended) to a high-severity ticket.
Here’s what we've tried so far:
* **Enhancing context:** We feed it historical data showing the user's normal behavior and role.
* **Rule-based pre-filtering:** Added a allow-list for specific user-group + command combinations during certain hours.
* **Prompt tuning:** We refined the classification instructions to emphasize "authorized routine administrative activity" vs. "atypical, policy-violating privilege elevation."
The results are better, but not great. The false positive rate is still around 30%, which is burning analyst cycles.
My main questions for the group:
1. **Benchmarking:** Has anyone found a reliable way to quantitatively benchmark an agent's classification performance in a PAM scenario? Beyond simple accuracy, what metrics matter most for SOC workflow (like mean time to dismiss false positives)?
2. **Feature Engineering:** What specific event features or enriched data (like adding vulnerability context of the target system) moved the needle for you?
3. **Agentic Workflow:** Should we shift from a single "classify" step to a multi-agent approach? E.g., one agent performs initial triage, and a separate "investigator" agent queries a knowledge base of our change management tickets before making a final call?
I'm a believer in the automation, but the tuning phase is proving that real-world entropy is the ultimate test. Any concrete experiences or architectural ideas would be hugely appreciated.
– Amanda
Show me the accuracy numbers.
That 30% false positive rate is exactly where the fatigue sets in. Been there.
You've covered the major angles. Since you're on an open framework, have you considered weighting the inputs? The LLM might be giving too much weight to the "privilege change" aspect of the event versus the "source is a trusted entity during a trusted window" signal. Sometimes tuning the prompt to de-emphasize the raw command syntax and emphasize trust scores from your other data (historical behavior, role, maintenance schedule) can shift the balance.
One more thing to check: is the agent seeing the *full* command chain? A `sudo su -` looks scary in isolation, but if it's followed by `yum update` on a pre-approved server list, the context is completely different. Making sure it gets that sequence might help.
Stay factual, stay helpful.