Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
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.
Adversarial testing for AI applications needs deliberate scope, technique variety, and consequence measurement, not just a handful of well-known attack attempts.
Integration testing for AI tool permissions confirms the full authorization path holds end to end, including cases where the model requests something it should not.
Unit testing AI security controls means testing the deterministic code around a model, authorization checks and limits, not the model's own variable behavior.
Testing an AI application before release needs functional, conventional security, and AI-specific adversarial layers combined, since each catches different failures.
A secure design review for an AI application needs specific questions about content trust, tool authority, and failure behavior added to the existing review structure.
Threat modeling before connecting an LLM to an existing system means mapping what new content and action paths that connection opens, before the integration ships.
Security requirements for an AI feature need to be specific and checkable, covering data reach, action authority, and failure behavior, defined before implementation.
