AI Acceptable Use Policy for Organisations: An Internal Governance Template in 10 Clauses

Why does an organisation need a written acceptable use policy?

Staff are already using generative AI tools to draft messages, summarise reports and analyse data, whether or not a policy exists. The question is not whether AI is being used, but whether it is being used under known rules.

The generative AI profile that NIST published in 2024 identifies twelve risks that are unique to or made worse by this technology, including confabulation (confidently presenting invented information), data privacy, information security, intellectual property, harmful bias, and reliance on third-party components and suppliers. ^[2] Inside an organisation, each of these becomes a practical question: what may staff enter? Who checks the output? Who is accountable?

NIST explicitly recommends that organisations define acceptable use policies for generative AI interfaces and address illegal uses in them. ^[2]

What should the policy be built on?

The NIST AI Risk Management Framework (AI RMF 1.0) was released in January 2023 for voluntary use and is built on four functions: Govern, Map, Measure and Manage. The Govern function sets policies and accountability and cuts across the other three, so an acceptable use policy sits at its heart. ^[1]

ISO/IEC 42001:2023 was published in December 2023 and sets requirements for establishing, implementing, maintaining and continually improving an AI management system. It applies to any organisation that provides or uses AI-based products or services, whatever its size. ^[3] It includes a clause on AI policy (5.2) within the leadership requirements, a clause on AI system impact assessment (6.1.4), and an annex of reference controls. ^[4]

SDAIA’s AI Ethics Principles (Version 1.0, September 2023) apply to everyone who designs, develops, deploys, uses or is affected by AI systems in the Kingdom, and set out seven principles: fairness; privacy and security; humanity; social and environmental benefits; reliability and safety; transparency and explainability; and accountability and responsibility. ^[5]

The ten clauses of the policy

1. Purpose and scope

State who the policy covers: employees, contractors and trainees. State what it covers: public generative AI tools, AI features built into office software and systems, and models the organisation develops or buys.

2. Roles and responsibilities

Name the policy owner, the body that approves new tools, and who assesses high-risk uses. The NIST framework calls for documented roles and for staff to be trained in AI risk management. ^[1] SDAIA’s principles set out specific roles for adopting entities, including a Responsible AI Officer and an AI System Assessor. ^[5]

3. Approved tools and a register of uses

Maintain a list of permitted tools and the conditions for each, and keep a register of the AI systems in use. The NIST framework requires mechanisms to inventory AI systems, ^[1] and the generative AI profile suggests that the inventory record data provenance, known issues, human oversight roles and the underlying foundation model. ^[2]

4. Data classification: what may be entered?

In practice this is the most important clause. Link it directly to the organisation’s approved data classification policy: public data is permitted in approved tools; internal data only in tools the organisation has contracted; and personal or confidential data only in an environment approved for that purpose, in line with the personal data protection law in your country.

5. Permitted, conditional and prohibited uses

Classify uses by risk instead of writing a long list of prohibitions. SDAIA’s principles place systems in four categories: little or no risk; limited risk; high risk, which requires conformity assessments before and after deployment; and unacceptable risk, which is not allowed, with examples including social profiling of individuals and the exploitation of children. ^[5]

6. Human review and verification of output

Staff are responsible for any output they use as if they had written it themselves. Require numbers, references and names to be checked before use, because confabulation is one of the main risks of generative models. ^[2] Any decision affecting a person’s rights, such as hiring, appraisal or eligibility, remains a documented human decision.

7. Disclosure and transparency

Specify when AI use must be disclosed: in content published to the public, in automated replies to customers, and in reports that rely on automated analysis. Transparency and explainability is one of SDAIA’s seven principles. ^[5]

8. Intellectual property and confidentiality

Prohibit entering third-party protected documents or contractual secrets into public tools, and specify who owns the output and how it may be used in published material. Intellectual property is one of the twelve risks in the NIST profile. ^[2]

9. Cyber security and suppliers

Require cyber security approval before any tool is connected to the organisation’s systems or data. The NIST profile suggests red-teaming generative systems to test their resilience against attacks such as prompt injection, and including clauses on content ownership and security in contracts and service level agreements. ^[2] The core framework calls for policies and procedures that address AI risks associated with third parties, including intellectual property risks. ^[1]

10. Incidents, training and review

Define an AI incident, such as a data leak through a tool, publication of incorrect content or a biased decision, and set a reporting channel. The NIST framework calls for incidents and errors to be communicated to relevant parties, and tracked, responded to and recovered from under documented processes. ^[1] Tie the policy to short mandatory training, and set a review date.

Hypothetical example: a use classification table

Hypothetical example: a mid-sized service organisation attached this table to its policy:

The advantage of the table is that staff find the answer in seconds, and any new use is added to the right category rather than rewriting the whole policy.

How do you launch the policy so it does not stay on paper?

1. Map current practice first: a short survey of the tools actually in use and what they are used for.
2. Draft it with a joint team: legal, IT, cyber security, data protection, HR and a representative of the user departments.
3. Have it approved by top management: ISO/IEC 42001 places the AI policy within its leadership requirements. ^[4]
4. Provide the approved alternative before banning: at least one enterprise tool.
5. Train, then measure: short training with examples from the organisation’s own work, followed by periodic review of the register and incidents.

What differs across the Gulf?

Reference points differ from one Gulf country to another, so do not apply one country’s rules to the others. In Saudi Arabia, SDAIA’s principles are a key reference for principles, risk categories and roles, ^[5] and SDAIA has since issued further tools, including a national AI risk management framework, so check its latest publications before adopting the policy. In the UAE, the official government portal lists a generative AI guide covering uses and data privacy, and notes that the government has created the role of Chief Executive Officer for Artificial Intelligence in ministries and federal entities, with duties that include strategic planning and setting governance frameworks for AI adoption. ^[6] In other countries, check with the national data and AI authority and your sector regulator before adopting the policy.

Conclusion

A good AI acceptable use policy is short enough for staff to read and specific enough for them to find an answer to their everyday situation. Build it on the ten clauses, link it to data classification, classify uses by risk, and review it whenever the tools or requirements change.