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Building an AI Security Monitoring Stack
Enterprise SIEM and monitoring systems were built for IT infrastructure. They have no detection rules for AI-specific threats: prompt injection patterns, model extraction via API probing, training data exfiltration, adversarial input campaigns, model drift indicating data poisoning.
“Your SIEM has no rules for prompt injection. It has no baseline for GPU cluster traffic patterns. It cannot detect model extraction attempts. I built an AI-specific monitoring stack and here’s what it catches that your current system misses.”
Architecture Diagrams
Build Notes
- AI monitoring stack: model serving logs → SIEM with AI-specific detection rules
- Parallel path: model metrics → drift detection engine → alert pipeline
- Prometheus + Grafana monitor inference latency and throughput
- Custom SIEM rules detect prompt injection and model extraction patterns
Lessons Learned
- Building AI-specific SIEM rules requires understanding both security and ML operations
- Start with three detection categories: prompt injection, abnormal API volumes, and model performance anomalies
- Grafana dashboards showing inference metrics give both engineering and security teams shared visibility
- The audit trail is the most valuable output for compliance — build it from day one
Discussion
What’s in your SIEM for AI workloads right now? If the answer is nothing, what would you monitor first — inference API patterns, model performance drift, or GPU cluster traffic?