Lilly Tech Systems
Introduction to AI-Powered Threat Detection
Understand how AI transforms threat detection from reactive signature matching to proactive, intelligent identification of sophisticated attacks across the kill chain.
The Detection Challenge
Modern enterprises face a detection gap. Attackers use increasingly sophisticated techniques including living-off-the-land, fileless malware, and slow-and-low approaches that evade traditional rule-based detection.
Detection Approaches Compared
| Approach | Strengths | Limitations |
|---|---|---|
| Signature-Based | Fast, precise, low false positives for known threats | Cannot detect unknown attacks, easily evaded |
| Rule-Based | Flexible logic, human-readable, auditable | Requires manual maintenance, misses novel patterns |
| ML Anomaly Detection | Detects unknown threats, adapts to environment | Higher false positive rate, requires tuning |
| Deep Learning | Complex pattern recognition, minimal feature engineering | Requires large datasets, less interpretable |
| Hybrid AI | Best of all approaches, layered defense | More complex to operate and maintain |
Key Concepts
MITRE ATT&CK Framework
A knowledge base of adversary tactics and techniques that provides a common language for mapping AI detections to real-world attack behaviors.
Kill Chain Mapping
AI can detect threats at multiple stages of the attack lifecycle, from initial reconnaissance through lateral movement to data exfiltration.
Detection Coverage
Measure what percentage of ATT&CK techniques your AI models can detect, and prioritize expanding coverage for the most relevant threats.
Alert Fidelity
The precision of detection models directly impacts SOC efficiency. High-fidelity alerts save analyst time and improve response speed.