EvidenceDemonstrated real incidentv1.22.1
In plain English
This page shows what kind of support exists for each claim: real systems, experiments, early evidence, architectural reasoning, open questions, or speculative scenarios.
- Why this matters: AI risk can come from the whole arrangement, not one obvious model.
- What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
- Technical version below: the expert terminology remains available and is linked through the glossary.
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
Evidence card
- Claim
- Public AI risk management emphasizes governance, measurement, mapping, and management functions.
- Evidence level
- Demonstrated
- Source
- https://www.nist.gov/itl/ai-risk-management-framework
- Publication date
- 2023-01-26
- Authors or institution
- National Institute of Standards and Technology
- System tested
- Cross-sector voluntary risk-management framework, not a model-evaluation experiment.
- Limitations
- Framework does not prescribe one technical implementation for adaptive model populations.
- What the evidence does show
- Public AI risk management emphasizes governance, measurement, mapping, and management functions.
- What the evidence does not show
- That framework adoption alone creates ecology-level assurance.
- Date last reviewed in UTC
- 2026-06-26T00:00:00Z
Site use
This source supports Cognivirus.com pages related to AI risk management, governance, measurement, trustworthiness. Its role is bounded by the limitations listed above.