Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
A consolidated pre-production AI security checklist covering requirements, design, testing, and release readiness, scaled proportionally to a feature's risk tier.
Automating AI security testing in CI/CD means running unit, integration, and regression corpus tests on every relevant change, not only before a scheduled release.
Security acceptance criteria for an AI feature need to be specific and testable, added to the feature's own definition of done rather than a separate, optional checklist.
An AI security tabletop exercise works best with a specific, realistic scenario and honest facilitation that surfaces real gaps rather than a scripted success story.
A kill switch for an AI system needs to be fast, granular, and pre-tested, since discovering it does not work correctly during an actual incident is the worst time to find out.
A safe AI feature rollout limits exposure through staged release and close monitoring, since AI behavior can shift with scale and audience in ways pre-launch testing misses.
Reviewing an AI SDK or agent framework needs specific questions about default security posture, since many are optimized for capability demos over safe defaults.
AI projects depend on models, weights, datasets, and agent frameworks as supply chain components, each needing the same due diligence applied to any software dependency.
Error handling in AI applications needs to fail safely, avoid leaking internal detail through model-facing messages, and log enough detail without exposing it to users.
Public AI features need abuse prevention layered across identity verification, behavioral monitoring, and cost containment, since exposure invites sustained probing.
Rate limiting AI applications needs limits on requests, tool calls, and agent loop iterations separately, since each protects against a different failure mode.
Schema validation for tool calls needs strict types, bounded values, and explicit rejection of unexpected fields, treating the model as an untrusted API caller.
