Notifications
Clear all
Topic starter
18/08/2026 2:12 pm
TL;DR: Detection engineering has become a core SOC discipline, but Hunters says common failures still undermine alert quality, triage speed, and coverage, from over-tuned rules to silent failures and weak context. The operational lesson is that effective detections need continuous testing, measurable feedback, and enough telemetry richness to stay trustworthy.
NHIMG editorial — based on content published by Hunters: Team Axon Detection Engineering, common failures and practical fixes
By the numbers:
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: How should security teams reduce brittle detection rules in the SOC?
A: Start by removing long exclusion chains and rule logic that only works in one narrow data shape.
Q: Why do detections fail even when the SOC has good tools?
A: They fail because the control depends on telemetry quality, schema stability, and maintained logic, not just the platform.
Q: What do security teams get wrong about noisy detections?
A: They often treat noise as a sign that the idea is bad, when the real issue may be poor scoping or missing context.
Practitioner guidance
- Create health checks for detection pipelines Track whether rules are firing, whether schemas have changed, and whether enrichment sources are still reachable.
- Reduce exclusion-heavy logic Replace long NOT conditions and brittle exception chains with narrower detections, supporting context, or multiple complementary rules.
- Standardise alert enrichment Attach asset tags, identity context, threat scores, and rule-specific rationale before alerts reach analysts.
What's in the full article
Hunters' full guide covers the operational detail this post intentionally leaves for the source:
- Step-by-step detection engineering checklist for building, testing, and maintaining rules across SOC toolchains
- Practical examples of alert enrichment, tuning decisions, and triage-ability improvements for analysts
- Workflow guidance for using threat prevalence and feedback loops to retire weak detections
- Tooling references for Sigma, Splunk, Microsoft Sentinel, and other detection pipelines
👉 Read Hunters' complete guide to common detection engineering failures and practical fixes →
Detection engineering: what SOC teams keep missing in practice?
Explore further
18/08/2026 3:04 pm
Detection engineering is now a control discipline, not a content-writing exercise. The article correctly frames detections as operational logic that must be tested, measured, and maintained. That matters because the control failure is often not an absence of tooling but a loss of trust in the signals that tooling emits. For identity-linked telemetry, this is where alerting, IAM, and SOC operations intersect, and teams should treat detections as governed controls rather than static rules.
A question worth separating out:
Q: How do teams know if detection engineering is actually improving?
A: They should measure whether new detections survive variant testing, whether telemetry is complete enough to support triage, and whether alerts feed back into updated logic without long delays. If the programme only changes during quarterly reviews, it is probably drifting faster than it is improving.
👉 Read our full editorial: Detection engineering failures expose the need for stronger SOC controls
