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 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.
Validating LLM output before execution needs schema checks, business rule checks, and destination-specific handling, applied before anything the output triggers takes effect.
AI applications need conventional secrets management extended to cover model API keys, agent credentials, and the prompt-template scanning that generic tools may skip.
Testing AI applications safely means building synthetic data that captures real structural edge cases, since production data copied into test environments still carries real risk.
Regression testing prompt injection defenses catches when a model update, prompt change, or refactor silently reopens a previously fixed vulnerability.
A reusable AI security test corpus accumulates injection attempts, edge cases, and past findings over time, making repeat testing faster and more consistent.
