Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
Egress control for AI agents specifically bounds where their tool calls and generated requests can send data, closing the exfiltration path a manipulated model could exploit.
Network controls for AI applications need attention to model provider endpoints, tool destinations, and browsing tools specifically, alongside conventional segmentation.
Sandboxing gives AI-generated actions a contained space to execute safely, particularly valuable for code execution and other actions too dynamic for static limits alone.
A well-designed approval gate surfaces specific context, routes to the right approver quickly, and expires cleanly, avoiding both rubber-stamping and dangerous delay.
Responding to tool abuse means stopping further unauthorized actions immediately, then determining whether the cause was a compromised model decision or a control gap.
Responding to AI data leakage means determining exactly what was exposed, to whom, and through which mechanism, since AI leakage often has an unfamiliar shape.
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.
