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 practical, grouped checklist for evaluating whether an AI agent deployment has the identity, authorization, containment, and monitoring controls it actually needs before going to production.
Monitoring an AI agent in production means watching decisions and action patterns, not just uptime, since an agent can be fully available and still be doing something it shouldn't.
Data analysis agents that query and summarize sensitive datasets need row-level authorization and output review, because a technically correct query can still return data the requester was never allowed to see.
Customer service agents handle sensitive personal data and can take account actions directly, which makes authentication, data exposure, and social engineering resistance the core security concerns.
Research agents that gather and synthesize information from many sources need controls against source manipulation and confident misinformation, since their whole output is a judgment call about what to trust.
Coding agents need security controls around what they can execute and commit, because the output of a coding agent is code that will run with real permissions somewhere.
Browser-using AI agents face a distinct risk because every webpage they visit is untrusted, attacker-controllable content that can inject instructions directly into what the agent reads next.
Long-running AI agents need security controls that account for drift over hours or days, not just per-request checks, because permissions, context, and risk all shift over an extended task.
Agent loops and runaway actions turn a single reasoning mistake into repeated tool calls at machine speed, which makes cost and resource controls a security concern, not just a budgeting one.
Safe retry and recovery for AI agents means designing failure handling so that a retried or resumed action can't accidentally duplicate, escalate, or bypass the checks the original attempt was supposed to go through.
Human approval in agentic workflows works only when it's placed at genuinely consequential decision points and designed to resist becoming a rubber stamp over time.
Limiting an AI agent's blast radius means designing so that any single bad decision, however it happened, can only cause bounded, recoverable damage.
