Aeon AI Risk Management
Govern AI agents before they become shadow operators.
Aeon designs governance for AI systems that can use tools, hold credentials, call APIs, take actions, and escalate decisions across workflows.
Questions this page answers
- What is agentic AI governance?
- It is governance for AI systems that can use tools, hold credentials, call APIs, take actions, and escalate decisions across workflows.
- How is agentic governance different from model governance?
- Model governance focuses on model lifecycle and outputs. Agentic governance also covers tools, permissions, actions, audit trail, escalation, and human accountability.
- Where does security testing fit?
- Agentic governance should include a security validation path. CyberGuard tests what agents can access, execute, or leak, then turns findings into governance and audit evidence.
Agents need operating controls
The risk is no longer only what a model says. It is what an agent can do, which tools it can call, and which credentials it holds.
Audit trail must be designed
Agent actions need logs that reconstruct the instruction, tool call, decision path, human checkpoint, and accountable owner.
Security evidence is part of governance
Policy is not enough for agents that can access systems or take action. Governance needs CyberGuard-style evidence for permissions, tool boundaries, logging, and remediation.
Governance needs tested boundaries
For agentic AI, the evidence layer matters as much as the policy layer: who authorized the tool, what it can do, how exceptions escalate, and whether security testing verified the boundary.