Notifications
Clear all
Topic starter
02/09/2026 1:29 pm
TL;DR: Detection programs are often measured by content volume, not operational health, leaving teams with rules that look covered but are silent, noisy, or decayed in production, according to AiStrike. The real problem is proving detections still work against live telemetry, and that is now a governance issue as much as an engineering one.
NHIMG editorial — based on content published by AiStrike: Detection Health
Questions worth separating out
Q: What breaks when a detection rule is in production but no longer has valid data behind it?
A: The rule becomes operationally inert even if it still looks covered on a dashboard.
Q: How do security teams know if intrusion detection is actually reducing risk?
A: Look for earlier alerting, shorter time to response, fewer false positives, and faster containment of suspicious build or runtime activity.
Q: What are the signs that detection content is decaying?
A: Common signs include rules that have not fired for months, detections tied to old schemas or retired platforms, and alerts that generate volume but no confirmed investigative value.
Practitioner guidance
- Inventory last-fire dates for every production detection Export the full rule set and sort by last-fired timestamp.
- Validate data-source health before trusting coverage numbers Check whether each detection’s source is live, whether its fields are still populated, and whether any upstream schema change has broken the query.
- Replay detections against historical telemetry Run high-priority rules over real historical logs from your environment to see what would have matched and what would have created noise.
What's in the full article
AiStrike's full blog covers the operational detail this post intentionally leaves for the source:
- A practical workflow for replaying detections against historical telemetry and measuring true signal quality.
- Detailed guidance on checking data-source health, field population, and schema drift before promoting rule changes.
- A step-by-step approach to tuning noisy detections without suppressing the alert that matters.
- Operational examples of how AI-assisted validation can support, but not replace, human approval for coverage-reducing changes.
👉 Read AiStrike's blog on detection health and continuous validation →
Detection health: are your SOC rules actually working?
Explore further
02/09/2026 1:30 pm
Detection health is a governance problem, not just a tuning problem. Teams have spent years improving how detections are authored, but too little attention goes to whether those detections still function after the environment changes. The result is an operational gap between intended coverage and proven coverage. For programmes that rely on identity telemetry, that gap can hide service account abuse, token misuse, and privileged access anomalies. Practitioners should treat detection validation as a standing control, not an occasional project.
A question worth separating out:
Q: How should teams govern detection changes when automation is involved?
A: Automation should propose validation evidence, not silently rewrite production logic. Human reviewers need to approve any change that reduces coverage or alters semantics, especially for identity and NHI detections where a small logic change can suppress high-value signal. The control point is approval, not generation.
👉 Read our full editorial: Detection health matters more than rule counts in modern SOCs
