AI Security Monitoring
5 articles
The AI Security Buyer’s Guide: How to Evaluate Vendors for LLM Firewalls, Guardrails, Evals, and Monitoring
AI security buyers should judge vendors by the job to be done: filtering, testing, evals, access, logs, leaks, rules, and proof. Choosing a vendor should start with design and risk, not just labels.
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.
The AI Security Engineering Stack: 50 Tools Across Red Teaming, LLMOps, Governance, and Detection
Teams often buy a tool category before they define the control gap. That creates duplication and gaps at the same time. A stack map helps the buyer see the boundaries first.
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.