AI risk management
AI risk management for agentic systems starts where an agent stops answering and starts acting.
When AI only produces text, its risks are contained. When an agent retrieves data, calls tools, and acts on decisions, the risk surface widens to include disclosure, manipulation, decisions that drift from policy, and actions no one can review after the fact. AI risk management for agentic AI is the practice of naming those risks and putting controls where they fit, inside a governance framework a risk or compliance function can stand behind. Cyaxios brings the controls and the evidence together, built on our work in governance, agent security, and audit trails.
See the governance approachWhy agentic AI changes risk
A system that acts carries risks a system that answers does not.
A generative model that writes a draft carries a bounded risk, because a person reads the output before it matters. An agent that acts through tools can change the state of other systems before a person sees anything, which moves the risk from the quality of an answer to the consequence of an action.
Effective AI risk management follows the action. It asks what an agent can reach, what it can do, what could push it off policy, and what evidence would let a reviewer reconstruct why an action was taken. Those questions turn a general concern about AI into a set of controls a team can implement and a reviewer can check.
The risk categories
The risks that come with letting AI act.
Disclosure
Values that leak
An agent that holds keys, records, or secrets can be worked into disclosing them. The control is to seal those values behind a tool so the agent uses them without revealing them.
Manipulation
Prompt injection
An attacker who influences what a model reads can push it off its instructions. The control is to constrain what the agent can act on and to enforce policy outside the model.
Policy drift and blind spots
Decisions no one can review
A decision at machine speed can drift from policy, and without evidence a reviewer cannot tell why. The control is governance bound to the record and audit evidence for every action.
A framework to manage it
An AI governance framework that connects policy to control to evidence.
Managing AI risk is a matter of connecting three things that often live apart. The policy that states what is allowed, the controls that keep an agent within it, and the evidence that shows it happened. An AI governance framework holds those together, so a stated rule has a control behind it and a record in front of it.
Cyaxios builds that framework from the parts we have described across this cluster. Decision governance in our AI governance work, extraction protection in our AI agent security work, field-level protection in our AI data governance work, and reviewable evidence in our audit trail work. Together they turn a risk register into a system a reviewer can trust.
Mapped to recognized standards
Controls named in the terms your reviewers already use.
The framework maps to the standards that regulators and reviewers reference for AI, including prompt injection and sensitive information disclosure in the OWASP Top-10 for LLM applications, the adversarial machine learning taxonomy and risk guidance in the NIST work on AI, and the obligations set out in the EU AI Act. Naming a control in those terms shortens the distance between a risk claim and an approval, and it gives an auditor a reference they recognize.
Where it begins
Risk management is easier when it starts before the build.
The cheapest time to manage AI risk is before the engineering, when a readiness assessment can surface the data, workflow, and governance gaps that would otherwise become production incidents. Our AI readiness assessment is where the risk work begins, and the governance framework is where it holds as the system moves into daily use.
FAQ
Common questions about AI risk management.
What is AI risk management?
It is the practice of identifying, assessing, and controlling the risks that come with deploying AI, and for agentic AI it extends to the risks of a system that takes actions, including data disclosure, prompt injection, decisions that drift from policy, and loss of auditability.
How does agentic AI change risk?
Because an agent takes actions through tools and data, its risks go beyond a wrong answer to include acting on a manipulated instruction, disclosing a protected value, or making a decision at machine speed that drifts from policy.
What is an AI governance framework?
The set of policies, roles, controls, and evidence that keeps AI use within an organization's risk appetite and regulatory obligations, connecting a stated policy to the controls and audit evidence that show it is being followed.
Which standards does AI risk management map to?
The OWASP Top-10 for LLM applications, the NIST work on AI risk and adversarial machine learning, and the EU AI Act. Naming a control in those terms shortens the distance between a risk claim and an approval.
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