Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
Responding to a confirmed prompt injection incident means containing the specific content source, tracing its reach, and closing the containment gap it exploited.
Investigating an AI security incident means reconstructing the specific content, decision, and action sequence, then tracing scope across similar sessions and identities.
Useful AI security metrics measure specific, actionable properties, denial rates, coverage, response time, rather than a general, hard-to-interpret security score.
Detecting suspicious agent behavior means comparing current activity against an established per-agent baseline, since generic anomaly rules miss agent-specific context.
Production AI monitoring needs AI-specific signals layered on top of conventional infrastructure monitoring, since neither alone reveals the full picture.
Tamper-evident logging makes unauthorized modification of AI activity records detectable, which matters most when the log itself becomes evidence in a dispute.
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.
