Agentic Witnessing enables privacy-preserving auditing of semantic properties in private data by running an LLM auditor in a TEE that answers binary queries and produces cryptographic transcripts of its reasoning.
arXiv preprint arXiv:2306.11417 , year=
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TORAI finds fine-grained root causes in microservice failures with blind spots by measuring anomaly severity from multi-source telemetry, clustering services by symptoms, ranking via causal analysis within clusters, and aggregating with hypothesis testing.
citing papers explorer
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Agentic Witnessing: Pragmatic and Scalable TEE-Enabled Privacy-Preserving Auditing
Agentic Witnessing enables privacy-preserving auditing of semantic properties in private data by running an LLM auditor in a TEE that answers binary queries and produces cryptographic transcripts of its reasoning.
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TORAI: Multi-source Root Cause Analysis for Blind Spots in Microservice Service Call Graph
TORAI finds fine-grained root causes in microservice failures with blind spots by measuring anomaly severity from multi-source telemetry, clustering services by symptoms, ranking via causal analysis within clusters, and aggregating with hypothesis testing.
- Anomaly Detection and Root Cause Analysis for Microservice Systems