Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
An AI activity audit ledger connects identity, decisions, and outcomes into a single queryable structure, distinct from scattered application logs.
Drawing explicit boundaries between the model, its tools, and the data behind them clarifies where each layer's own security responsibility begins and ends.
A small set of low-impact, low-reversibility-risk AI workflow decisions can reasonably fail open, and naming them explicitly avoids defaulting every dependency to fail-closed unnecessarily.
A fail-closed AI workflow defaults to blocking or holding an action whenever a required check cannot complete, requiring explicit design rather than relying on defaults.
Inline controls can block an action before it executes; out-of-band controls only observe and alert after the fact, and the difference determines what each can actually prevent.
Security policy in an AI stack belongs at the tool-call boundary primarily, with supporting enforcement at content assembly and output validation, not inside the prompt.
A secure AI application architecture places identity, retrieval filtering, tool authorization, and output validation as distinct layers the model cannot bypass.
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.
