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 consolidated production operations checklist for AI systems covering architecture, monitoring, incident readiness, and reliability, scaled to each system's risk tier.
An AI security operations runbook needs specific, step-by-step procedures for recurring scenarios, since a general incident response plan alone leaves execution to improvisation.
Observability for agentic workflows needs to capture the reasoning trace and tool-call sequence, not just request-level metrics, since that is where agent-specific issues live.
Avoiding a security gateway bottleneck means keeping enforcement logic fast, scaling horizontally ahead of demand, and separating cheap checks from expensive ones.
High availability for AI security controls matters because a fail-closed design means an unavailable control blocks legitimate work, making its own reliability a priority.
Service-to-service authentication in AI architectures needs to propagate the original requesting identity through every hop, not collapse into one shared service credential.
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.
