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 decision-focused checklist for product managers shaping AI features, covering scoping, risk-aware requirements, cross-functional alignment, rollout gating, and ongoing ownership.
An oversight-level checklist for security teams governing AI agents they didn't build, covering visibility, threat modeling, detection, audit evidence, incident readiness, and review cadence.
An implementation-level checklist for engineers building AI agents, covering tool design, authorization, output handling, data handling, resilience, and testing before an agent reaches production.
Building AI security infrastructure in-house gives full control but demands ongoing engineering investment to keep pace with new attack techniques; buying gets a working enforcement and audit layer faster but adds integration work and a new dependency. Most teams end up doing both.
Centralized controls sit in a shared gateway every agent's traffic passes through; embedded controls live inside each application's own code. Mature programs typically run both, centralized for what needs to apply everywhere, embedded for logic specific to one app.
Automatic enforcement is fast and consistent but only as good as the rules written in advance; human approval brings judgment to ambiguous cases but doesn't scale and depends on the approver actually having enough context. Most systems need both.
Inline enforcement blocks an action before it happens; post-event detection reviews what already occurred and can only alert or trigger a response. For high-impact actions, relying on detection alone means the first incident already did its damage.
A governance platform tracks whether the organization is following its own AI policy on paper; a security platform enforces and records what actually happens at runtime. Mature programs need both, working off the same risk classifications.
Observability tracks latency, cost, and output quality for reliability; security monitoring watches the same activity for policy denials, injection patterns, and permission escalation.
A model guardrail is a judgment call that a good enough prompt can argue around; runtime policy enforcement checks a fixed permission that doesn't care how the request was phrased.
Guardrails are content-level filters relying on a model's own judgment; AI security controls are deterministic checks like authorization and approval that don't depend on how convincing a prompt sounded.
A WAF blocks requests that match known attack signatures; an AI security gateway blocks prompts, outputs, and actions that mean something dangerous while looking completely ordinary.
