AI Risk Management
Extend Your Risk Framework to Agentic AI Actions
AI agents can refund, transfer, delete, and delegate. Tier those risks, enforce controls before action, and test them with black-box probes.
Your Risk Register
Wasn’t Built for Agentic AI Risks
Most risk registers were built for models that score or predict, with validation before launch and periodic review after. Agents that take actions don’t fit that pattern. Agentic AI risks sit in tool access and handoffs the register never recorded, and the controls, if any exist, live in application code the second line can’t read or test.
When the risk committee asks how an agent is controlled, the answer is spread across teams, tickets, and code reviews.
Lumenova AI ties each agent use case to a risk tier, a control for each risk, and tests that show the control holds. Every decision is recorded, so your second line reviews evidence instead of assurances.
Capabilities
Manage Agentic Risk from Register to Runtime
Tier each use case, test the controls that address its risks, and give your second line the evidence to review them.
Use Case Risk Tiers
Record the owner, approver, and risk tier for each use case in the AI Registry, so higher-risk use cases get stricter policy and closer review.
Tested Controls
Write each risk limit as policy that runs before the action, and test it against sample input before it binds.
Adversarial Testing
Probe running agents with OWASP LLM Top 10, MITRE ATLAS, and OWASP Agentic templates, and replay the attacks after each fix to verify it.
Second-Line Evidence
Export policy decisions, guardrail decisions, findings, and detections as CSV or JSON for your risk committee’s reviews.
Dependency Visibility
See the servers, agents, skills, and prompts each use case depends on before approving it, instead of reconstructing them during an incident.
Validated Evaluators
Measure how closely automated evaluators agree with your own reviewers, so model risk knows how far each score can be relied on.
Treshold Alerts
Monitor for drift and data-integrity issues, and send alerts when a system moves outside the thresholds you set.
Framework Alignment
Map your process to the framework you report against, such as SR 11-7, NIST AI RMF, ISO 42001, or the EU AI Act, from design through production.
Frequently Asked Questions
No. It extends it to agents. Your program still sets the standards and makes the decisions. The platform adds what agents need on top: a control in the path of each risk, tests that show the control holds, and records your second line can review. Each use case can also reference its record in your GRC tool.
You do. Each use case records the risk tier your methodology assigns, and higher tiers can get stricter policy and closer review.
The first line builds agents and writes limits as tested policy. The second line reviews that policy, challenges automated scores against its own reviewers, and pulls decision records without going through engineering. The third line gets an append-only audit log and exports for its own testing.
Test each policy against sample input before it binds, probe running agents with red team templates, and replay successful attacks after each fix. Categories a target can’t exercise are reported as untested, never as passed, so a gap doesn’t look like a clean result.
Both. Controls are tested before release, and in production, live traces can be scored automatically, past sessions hunted for missed attacks, and alerts routed to your team. That fits the ongoing monitoring an agent needs, since it can change after approval.
Exported policy decisions, guardrail decisions, findings, and detections, each tied to the agent, use case, rule, and input behind it.