SIEM & Detection Engineering: Building a Detection Program That Actually Works

Alert fatigue is the leading cause of detection failure. When a SOC analyst receives 1,000 alerts per day and 95% are false positives, the rational response is to stop investigating alerts carefully. Critical detections get buried in noise. Mean time to detect (MTTD) increases not because the detection rule did not fire, but because the analyst did not have bandwidth to investigate it.

The root cause of alert fatigue is not too many threats — it is too many bad rules. Default vendor rules deployed without tuning. Rules triggered by normal business operations that were never baselined. Rules with thresholds set for a generic environment, not your specific one. The solution is not more analysts — it is better rules.

The detection engineering mindset flips the alert volume problem: instead of measuring success by how many alerts your SIEM generates, measure success by signal-to-noise ratio. A detection program producing 50 alerts per day with a 70% true positive rate is infinitely more valuable than one producing 5,000 alerts with a 2% true positive rate.

MITRE ATT&CK is not a checklist — it is a systematic way to map your detection coverage against real adversary behavior. The framework catalogs adversary techniques across the attack lifecycle: initial access, execution, persistence, privilege escalation, defense evasion, credential access, discovery, lateral movement, collection, exfiltration, and impact.

The practical use of ATT&CK for detection engineering is coverage mapping: for each technique relevant to your environment, do you have a detection rule, does the required data source exist, and has the rule been validated? This immediately surfaces two types of gaps — techniques with no detection and techniques with detection rules but missing data sources.

Prioritize ATT&CK coverage by threat relevance, not by framework completeness. You do not need detection for every technique — you need detection for the techniques that adversaries actually use against organizations like yours. Threat intelligence informs which techniques to prioritize. For most mid-market organizations, the highest-priority tactics are initial access, credential access, privilege escalation, and lateral movement.

A detection engineering program starts with three foundations: data source coverage, detection rule development, and detection validation.

Data source coverage means ensuring your SIEM ingests the log sources that contain evidence of adversary behavior. At minimum: cloud audit logs (CloudTrail, Azure Activity, GCP Audit), identity provider logs (Entra ID, Okta), endpoint detection logs (CrowdStrike, Defender, SentinelOne), network flow logs, and email security logs. Gaps in log coverage are gaps in detection capability — you cannot detect what you do not collect.

Detection rule development means systematically building rules for priority ATT&CK techniques. Start with 3 priority tactic areas — typically credential access, privilege escalation, and lateral movement for cloud-focused organizations. Build 15-20 high-quality rules with documented hypotheses, expected false positive rates, and triage procedures. Quality over quantity.

Detection validation means testing that your rules actually fire when the technique occurs. This can range from controlled purple team exercises (simulating adversary techniques in a test environment) to automated detection testing tools. A detection rule that has never been validated is a hypothesis, not a control.

The ongoing operating rhythm includes: weekly detection rule tuning (adjusting thresholds, adding exclusions for validated false positives), monthly coverage review (mapping new threat intelligence to ATT&CK and identifying gaps), and quarterly detection sprint (building new rules for emerging techniques or uncovered tactics).