implementing-siem-use-case-tuning

Export alert volumes per rule, calculate false positive rates from analyst dispositions, then adjust thresholds using statistical baselines such as mean plus N standard deviations. Add whitelists for known-good entities like service accounts and scanners, and measure the alert-to-incident ratio before and after.

Tune on fields attackers cannot easily control, such as asset IDs or source-and-rule pairs, rather than mutable attributes like usernames or User-Agents. After tuning, replay a known-malicious sample to confirm it still alerts and track both precision and recall, not just alert volume.

You need at least 30 days of historical alert data for baseline analysis, SIEM admin credentials or API tokens, and Python 3.8+ with the requests library. Baselines must come from a known-clean period so attacker activity is not baked into normal behavior.

Raising thresholds or whitelisting entities can blind detection if the baseline window already contained attacker activity or if exclusions use spoofable attributes like hostnames. Baseline from a known-clean period and verify coverage by diffing the enabled-rule inventory before and after tuning.

Measure precision, recall, and the alert-to-incident ratio before and after tuning. Replay a known-malicious sample to confirm it still triggers, and run 30 days of benign data to verify the false positive rate dropped; fewer alerts alone is not proof of success.