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Threat Detection Best Practices

Master detection engineering principles, model lifecycle management, performance metrics, and detection-as-code methodologies for scalable AI-powered security.

Detection Engineering Principles

  1. Detection-as-Code

    Version control detection logic (rules and ML models) alongside application code. Use CI/CD pipelines for testing and deployment.

  2. Test-Driven Detection

    Write unit tests for detections using attack simulation tools like Atomic Red Team. Every detection should have a corresponding test.

  3. Coverage-Driven Development

    Prioritize new detections based on ATT&CK coverage gaps and threat intelligence about relevant adversary groups.

  4. Continuous Validation

    Regularly test detections against simulated attacks to ensure they still fire. Alert on detection degradation.

Key Metrics

MetricTargetWhy It Matters
True Positive Rate> 90% for known TTPsMeasures detection effectiveness
False Positive Rate< 5%Determines analyst workload burden
Detection Latency< 5 minutesTime from event to alert generation
ATT&CK Coverage> 80% of priority techniquesBreadth of detection capability
Model FreshnessRetrained within 30 daysEnsures models reflect current environment
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Lifecycle Tip: Implement model monitoring dashboards that track prediction distributions, feature drift, and performance metrics. Set automated alerts when models degrade beyond acceptable thresholds.

Scaling Detection Programs

Model Registry

Maintain a central registry of all detection models with metadata, performance history, data dependencies, and ownership information.

Feature Store

Build a shared feature store for security features so detection models can reuse common enrichments and behavioral signals.

A/B Testing

Run new detection models in parallel with existing ones to compare performance before promoting to production.

Knowledge Sharing

Document detection logic, false positive patterns, and tuning decisions so the team can learn from each other's work.

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Course Complete: You have completed the AI-Powered Threat Detection course. You now have the skills to build and operate AI-driven detection systems using anomaly detection, UEBA, and real-time analytics.