Tagged:

2 posts

• LLM Security Monitoring in Production: Anomaly Detection, Audit Logging, and Intrusion Detection for AI Systems

  Guardrails block known bad inputs. Incident response handles breaches after they happen. This post covers the gap: building LLM-specific observability that detects attacks while they are occurring — anomalous outputs, prompt injection signatures, unusual tool-call patterns, and model drift from baseline.

  Jul 10, 2026 monitoring anomaly-detection audit-logging intrusion-detection observability langfuse arize owasp nist security llm production

• Zero-Trust Architecture for AI Agent Deployments: Never Trust, Always Verify — Even Your Own Agents

  Zero-trust was designed for human users on networks. AI agents break its assumptions: they act autonomously, spawn child agents, inherit permissions, and run long-lived sessions. Here's how to adapt zero-trust principles specifically for multi-agent and LLM deployment contexts.

  Jul 6, 2026 zero-trust agent-security identity authentication microsegmentation spiffe nist architecture security ai-agents llm