Concern 897 · Working draft · editorial review in progress
AI-specific enterprise-risk registers
An AI risk register is useful only if it connects specific problems to responsible people, evidence and action rather than becoming a static list of concerns.
Understanding the concern
Organizations may use AI in many places without a complete view of where it affects people or operations. A register can help teams see the use, the possible harm, current controls and unresolved questions together. A long list does little if nobody owns the next decision.
NIST's framework supports inventories and defined responsibilities. Consider a building's maintenance log: recording a broken handrail is useful only if someone repairs it and checks the repair. An AI register should be updated when a model, data source or use changes. It should distinguish a possible risk from an incident that actually occurred and avoid unsupported numerical precision.
An example
A board receives an AI risk list every quarter, but the same unresolved issue appears repeatedly without an owner or action date.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
Evidence and its limits
The framework supports risk-management structure. It does not validate a particular scoring system or show that maintaining a register by itself prevents harm.
NIST · Voluntary framework
Artificial Intelligence Risk Management Framework, AI RMF 1.0 ↗ (opens in a new tab)Governance, roles, inventory and risk management principles. The framework is being revised; adopting its language does not establish effective controls.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- NIST AI RMF (opens in a new tab)
framework
- ISO/IEC 42001 (opens in a new tab)
standard
A question to discuss
For each important risk, who will act, what evidence is needed and what would trigger escalation?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Trustworthiness
Trustworthiness means giving people good reasons to rely on you. It grows when promises, actions, and evidence line up. With AI, a friendly voice or a famous brand is not enough: people need to know what was checked and what could still go wrong.
In practice, keep promises small enough to test, record mistakes, and tell people when the tool's limits affect them. If a service promises human review, make sure a person really has time and authority to review it.
Ask together: What would give the people affected a sound reason to trust this use?
Stewardship
Stewardship means caring for resources and systems that others depend on, including people who will use them later. It looks beyond the benefit of a quick launch.
Plan for maintenance, energy use, data protection, and eventual shutdown. Ask who will look after the tool when the original team moves on and how future users can avoid inheriting hidden costs.
Ask together: What are we responsible for caring for over time?
Prudence
Prudence means using careful judgment before and during action. It balances worthwhile opportunities with the need to avoid harm that could have been anticipated.
Start with a limited trial, set stopping points, and prefer choices that can be undone when uncertainty is high. Review what happens rather than assuming the original plan remains sound.
Ask together: What is a careful next step, and when should we stop or change course?
Editorial ratings
These are editorial judgments from the register, not measured probabilities or predictions about your situation.
About the register and scoring →More ways to explore
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