Agentic Security
Agentic security is the discipline of securing autonomous AI agents against the new risks they introduce — unsafe tool use, excessive permissions, prompt injection that triggers actions, and unbounded side effects — by governing what agents can do, not just what they can say.
Definition
Agentic security is the discipline of securing autonomous AI agents against the new risks they introduce — unsafe tool use, excessive permissions, prompt injection that triggers actions, and unbounded side effects — by governing what agents can do, not just what they can say.
The new attack surface
Agents expand the security perimeter because they act, not just respond. A compromised or confused agent with broad tool access can exfiltrate data, run destructive commands, or make outbound calls on the operator's behalf. Prompt injection becomes far more dangerous when the model is wired to tools: malicious instructions hidden in fetched content can steer the agent into taking real actions. Over-broad permissions and the absence of an approval checkpoint turn a single bad step into a production incident.
Defending the action layer
Agentic security centers on the trust boundary between the agent and the systems it touches. The core controls are least-privilege scoping (each agent identity only reaches the tools and data it needs), pre-dispatch policy enforcement (every action checked before it runs), human approval for high-risk operations, and a tamper-evident audit trail for detection and forensics. Cordum implements these as an out-of-process control plane so the same protections apply regardless of which framework built the agent.
Frequently asked questions
How is agentic security different from traditional application security?
Traditional app security protects code paths a developer wrote. Agentic security must account for a non-deterministic actor that chooses its own tool calls at runtime, so the focus shifts to scoping permissions, gating actions before they execute, and auditing what the agent did.
Why does prompt injection matter more for agents?
For a chat-only model, injection corrupts text. For a tool-wielding agent, injected instructions can trigger real actions — sending data, running commands, calling APIs. Governing actions before dispatch limits the damage even when the model is successfully manipulated.
Related reading
Govern your AI agents with Cordum
Cordum is the agent control plane: policy-before-dispatch enforcement, human approvals, and a tamper-evident audit trail for autonomous AI agents.