Lilly Tech Systems
Intermediate
AI-Powered Log Analysis
Use machine learning to parse, correlate, and analyze security logs at scale, enabling automated threat hunting and rapid incident timeline reconstruction.
ML-Powered Log Parsing
Traditional regex-based parsing breaks when log formats change. ML approaches adapt automatically:
- Template Mining: Algorithms like Drain and LogMine automatically discover log templates from raw text
- Semantic Parsing: NLP models understand log meaning regardless of format, extracting events, entities, and actions
- Format Detection: Classifiers automatically identify log source types and apply appropriate parsing strategies
- Error Handling: ML gracefully handles malformed logs, partial entries, and encoding issues
AI-Assisted Threat Hunting
| Hunting Technique | AI Role | Output |
|---|---|---|
| Hypothesis-Driven | Suggest hunting hypotheses based on threat intelligence and ATT&CK coverage gaps | Prioritized hypothesis queue |
| Data-Driven | Cluster and surface unusual patterns in log data for analyst review | Anomaly clusters with context |
| Entity-Driven | Build comprehensive entity timelines across all log sources | Entity risk profiles |
| TTP-Driven | Map log events to MITRE ATT&CK techniques automatically | ATT&CK heat maps |
Hunting Tip: Use LLMs to translate natural language hunting queries into structured log searches. This enables analysts to ask questions like "show me all PowerShell activity by users who also accessed SharePoint finance folders" without writing complex query syntax.
Timeline Reconstruction
Event Correlation
ML links related events across disparate log sources using entity resolution, temporal proximity, and causal inference.
Attack Chain Assembly
Algorithms reconstruct the full attack sequence by ordering correlated events and identifying the kill chain stages.
Root Cause Analysis
Graph-based models trace back from detected alerts to identify the initial compromise point and attack entry vector.
Impact Assessment
ML estimates the scope of compromise by analyzing all entities and systems touched during the attack timeline.
Looking Ahead: In the next lesson, we will explore real-time detection with stream processing, online learning, and MITRE ATT&CK mapping.