AI agent security is the practice of controlling what autonomous AI systems can do, see, and act on at runtime. Unlike traditional application security, which protects systems from external attackers, AI agent security focuses on constraining the behavior of AI systems from within — enforcing policy on every tool call, data access, and model output before any action completes.
Why AI agents need specialized security
An AI agent uses a large language model (LLM) as its reasoning engine to take a sequence of actions toward a goal. Because agents call external tools, read/write files, and interact with business systems, they create a new attack surface that traditional firewalls and DLP cannot see.
Core components of AI agent security
- Runtime tool call interception: Intercepting every request an agent makes to an external system to verify it against policy.
- Identity-aware access: Ensuring every agent instance has a scoped identity with least-privilege permissions.
- Prompt injection prevention: Detecting and blocking malicious instructions embedded in content the agent processes.
- Audit logging: Capturing a tamper-evident record of every decision, tool call, and model response.
### How is AI agent security different from LLM security?
LLM security focuses on protecting the model itself from attacks like jailbreaking. AI agent security is broader, governing the entire autonomous system, including its tool use, data access, and real-world actions.
### What is a runtime security policy for AI?
A runtime policy is a set of rules enforced at the infrastructure layer (not inside the prompt) that defines what an AI agent is allowed to do. For example, "never send customer data to an external email address."
### Do I need human-in-the-loop (HITL) for AI agents?
Yes, for high-risk actions. AI agent security platforms allow you to define which actions require explicit human approval before the agent can proceed.