Decision rights
Clarify who can approve, pause, override, retire, and accept residual risk across the AI lifecycle.
Khaos helps institutions turn AI ambition into clear decision rights, accountable governance, mapped dependencies, credible challenge, and evidence that leaders can defend.
AI risk is not confined to model performance. It intersects with data rights, cyber exposure, third-party concentration, workforce behavior, regulatory expectations, customer outcomes, reputation, and the speed at which automated decisions propagate. Governance has to connect those systems rather than sit beside them.
Reference: NIST AI Risk Management FrameworkClarify who can approve, pause, override, retire, and accept residual risk across the AI lifecycle.
Map training and operational data, vendor dependencies, access boundaries, retention, and concentration risk.
Examine over-reliance, workarounds, deskilling, incentives, escalation pathways, and where human judgment must remain decisive.
Connect policy, testing, monitoring, incident response, and board reporting into a defensible control record.
Where is AI already influencing institutional decisions, including through unsanctioned use?
Which uses create the greatest asymmetry between potential value and potential consequence?
Can leaders explain the control environment in operational rather than aspirational terms?
What would cause the institution to pause, restrict, or retire a capability?
Establish the real AI footprint: use cases, models, agents, vendors, data, owners, and decision impact.
Connect each capability to institutional obligations, dependencies, and failure pathways.
Test governance, control evidence, human oversight, and escalation under realistic pressure.
Set decision rights, control cadence, reporting, and triggers that evolve with capability and exposure.