Make AI oversight part of everyday operations.
Connect your AI inventory to risk classification, execution gates, information controls, and audit evidence. Responsibility stays visible throughout the agentic workflow.
Illustrative product demo · Synthetic institutional data
Read the view
Start with an inventory item that can be reviewed.
The AI Risk view begins with a concrete inventory item: an agent, model, AI-assisted workflow, or governed process. The reviewer sees domain, owner, mode, permitted use, connected data, and current risk tier. This keeps AI oversight close to operational context instead of turning it into a detached policy checklist. Representative views show inventory and risk classification, domain-by-risk tier charts, and whether a workflow is read-only, proposal-only, or allowed to continue only after review.
Inventory
Identify the agent, model, workflow, domain, owner, connected data, and review scope.
Risk tier
Show purpose, exposure, control level, and review status beside the item under governance.
Mode
Distinguish read-only, proposal-only, and gated execution so operational boundaries are visible.
Policy context
Tie each control to the action it governs rather than treating oversight as a detached checklist.
Review status
Keep satisfied, partial, and open requirements visible before the workflow proceeds.
Review workflow
Show which controls must clear before the work moves.
AI risk oversight becomes useful when policy gates are tied to the action they govern. RYNTA brings execution approval, requirement traceability, information-flow and masking controls, model lifecycle context, access control, segregation of duties, and query governance into one reviewable record. A reviewer can see which control is satisfied, which is partial, and which remains open before a workflow proceeds.
Classify
Identify the AI-assisted workflow and risk tier before evaluating operational permissions.
Gate
Check policy, access, masking, and approval requirements before the workflow moves forward.
Trace
Connect requirements, findings, incidents, stops, and audit context to the work under review.
Evidence bundle
Keep audit context close to the AI-assisted output.
AI risk does not end when a workflow completes. Later reviewers may need to understand the request, data boundary, output, masking rule, approval path, and any incident or stop that occurred. RYNTA keeps that history inspectable without presenting it as certification. Inventory context, gate status, validation findings, incidents, alerts, stops, and reviewer action stay available for later review with the right people and source context in view.
Control record
AI workflow inventory, approval gate status, and requirement traceability.
Review context
Information-flow and masking review, validation findings, incidents, alerts, and stop history.
Reviewer handoff
Audit trail and reviewer action attached to the output for later inspection.
Review the workflow with your team.
Policy interpretation, review, and approval remain with authorized people. Production scope, integrations, and controls are agreed per institution.
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