Logging and Telemetry
7 articles
AI Audit Evidence: What Logs, Tests, Policies, and Approvals You Need to Prove Governance Works
AI governance requires evidence artifacts across inventory, risk, data, providers, prompts, evals, red-teaming, approvals, and logs. Evidence should be built into AI workflows, not assembled after a crisis.
Security Monitoring for AI Agents: How to Detect Dangerous Tool Use Before Damage Happens
Security monitoring for AI agents requires tool-call telemetry, action-sequence detection, approval-state tracking, memory monitoring, credential visibility, anomaly detection, and kill-switch response paths. Dangerous tool use should be detected before it becomes data leakage, unauthorized change, financial impact, or customer-facing error.
AI Logging and Telemetry: What to Capture Without Creating a Privacy Disaster
AI systems need logs because you cannot rebuild what happened from vibes. Security teams need to know what prompt was used, what docs were found, what the model said, what tool was called, who approved it, and what happened next.
Vector Database Security: Access Control, Tenant Isolation, Poisoning, and Forensic Logging
Vector database security requires the same seriousness as other production data infrastructure, with additional attention to embeddings, metadata filtering, retrieval authorization, tenant isolation, poisoning resistance, deletion workflows, and forensic logging.
AI Incident Response: Playbooks for Prompt Injection, Model Abuse, Data Leakage, and Rogue Agents
Most incident teams already know how to isolate systems and preserve logs. AI changes the shape of the evidence. The response process must include prompts, retrieval context, tool actions, and model versions.
Detection Engineering for AI Systems
Traditional detections miss AI-specific abuse because the action can start in language and end in a side effect. The control gap is not only alert content. It is missing telemetry.
Securing AI Agents: Identity, Memory, Tools, Permissions, and Kill Switches
Agent projects fail when teams treat autonomy as a product feature instead of a control problem. Once the agent can do work on behalf of a user, the attack surface moves from text to action.