From MCP server risks to machine identity and runtime guardrails — everything security engineers and CISOs need to govern agentic AI without slowing adoption.
AI agent security is the practice of governing, monitoring, and enforcing policy over autonomous AI systems — agents — that take actions inside business applications without direct human instruction for each step. It differs from traditional application security because agents hold persistent credentials, execute multi-step workflows, and access sensitive SaaS systems at a scale and speed that human oversight cannot track manually.
Prompt security governs what human users submit to language models: filtering sensitive data, detecting injection attempts, blocking harmful outputs. AI agent security governs what autonomous agents do after they receive their instructions — which credentials they use, which SaaS applications they access, which actions they chain.
A toxic combination occurs when multiple medium-severity risk factors exist simultaneously on a single agent, compounding into a critical-priority finding. Example: embedded admin credentials + org-wide public access + disabled creator account = critical blast radius, no owner.