Practical guidance on securing AI agents, tools, and the systems they act on, written by the team building the trust layer for AI agents.
Threat modeling an AI agent means mapping its plans, tools, and data sources step by step, then ranking each possible action by impact and reversibility rather than by sensitivity alone.
An AI agent's attack surface spans its inputs, memory, tool integrations, and inter-agent communication, each offering a different way to redirect what the agent does next.
Autonomous actions change the AI threat model by making tool calls, not just generated text, the thing an attacker is really trying to control.
AI assistants respond with information while AI agents take action, and that action capability is what changes the security requirements from output review to permission control.
AI agents create new security risks because they combine language-driven decision making with the ability to act, turning ordinary manipulation attempts into real-world consequences.
Agentic security is the set of practices and controls that keep autonomous AI agents acting within intended limits as they plan, choose tools, and take real-world actions.
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.
