Concern 916 · Working draft · editorial review in progress
AI use policies for employees
Employee AI-use policies fail when people cannot tell which tools, data and tasks are allowed or how to get help with an uncertain case.
Understanding the concern
A rule saying use AI responsibly leaves much of the decision to each employee. Staff need practical examples of approved accounts, confidential material, review requirements and reporting routes. A policy can also be so restrictive that people work around it to complete ordinary tasks.
Ethics and security guidance point toward clear responsibilities and usable processes. Think of workplace rules for handling customer records: people need to know what to do with a real document, not just agree that privacy matters. Policies should be tested with staff, updated as tools change and supported by suitable alternatives. A person who reports an honest mistake should help the organization learn.
An example
Two employees follow different assumptions about whether customer notes can be entered into an AI assistant because the policy never addresses it.
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.
Evidence and its limits
These sources provide governance and security principles. They do not establish one policy that fits every employer, data type or legal setting.
UK Government Digital Service · Government guidance
Data and AI Ethics Framework ↗ (opens in a new tab)December 2025 framework connects principles to practical governance, responsibility and stakeholder feedback. Normative guidance, not evaluation proving committees work.
UK National Cyber Security Centre · Security guidance
ChatGPT and large language models: what's the risk? ↗ (opens in a new tab)2023 guidance explains risks in sharing prompts with a provider. Current training, storage and access terms differ by product; do not reuse historical defaults as current facts.
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
Could a new employee apply this policy to a real task without guessing, and where would they ask for help?
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.
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