Log Anomaly Detection Framework
Self-supervised log anomaly detection integrating RAG with LLMs.
+17%
Accuracy
-40%
Triage Time
5M+ logs/day
Throughput
Problem
Manual log triage is slow and misses subtle anomalies across millions of daily entries.
Architecture
Modular pipeline: ingestion → Isolation Forest clustering → RAG retrieval → LLM explanation layer.
Challenges
- — Scaling to 5M+ logs/day
- — Reducing false positives
- — Sub-15-min deployability
Engineering Decisions
- — Self-supervised approach to avoid labeled data
- — Modular components for fast deployment
Tradeoffs
- — LLM inference cost vs. explanation quality
Future Work
- — Real-time streaming mode
- — Alerting integrations