AI Governance in Saudi Arabia: A Practical Framework for AI Audits and Responsible AI

AI Governance in Saudi Arabia: A Practical Framework for AI Audits and Responsible AI

AI governance in Saudi Arabia has moved from a topic for technology teams to a board-level question. Staff already use chat assistants to draft emails, summarise contracts and analyse spreadsheets, vendors are adding AI features to CRM, ERP and helpdesk tools, and customers increasingly meet chatbots before they meet a person. Most companies have adopted AI faster than they have decided who owns it, what data it may touch and how its output is checked.

This guide is for executives and compliance leads who want a workable answer. It explains what the Saudi Data and AI Authority (SDAIA) has published and who each document is aimed at, what an internal AI governance framework contains, what an AI audit actually checks, how the Personal Data Protection Law (PDPL) applies when AI processes personal data, and a 90-day plan to get started. If you are still choosing use cases, our AI implementation guide for Saudi businesses covers that earlier step.

Table of Contents

• What SDAIA has published on AI, and who each document applies to
• The SDAIA AI ethics principles and the four risk levels
• AI governance in Saudi Arabia: what an internal framework should contain
• What an AI audit in KSA checks: a practical checklist
• PDPL and AI: where personal data rules apply
• A 90-day plan and a simple AI governance maturity table
• Common AI governance mistakes Saudi companies make

What SDAIA has published on AI, and who each document applies to

SDAIA is the national authority for data and AI and the competent authority for the PDPL. Its AI documents differ in audience, so read each one for what it says about scope before treating it as a rule for your company.

Two points matter for private companies. First, the government guidelines, such as the rule that government employees should limit generative AI tools to data classified as public, are written for government entities. Private firms can borrow the logic, but should not describe it as a legal obligation on themselves. Second, the AI Ethics Principles offer an optional registration route with SDAIA and motivational badges reflecting the level of adoption. Sector regulators and specific laws, including the PDPL, may add binding duties for your activity, so confirm the current rules with the relevant authority or a qualified adviser before relying on any summary, including this one.

The SDAIA AI ethics principles and the four risk levels

SDAIA lists seven AI ethics principles. A practical reading for a company is:

• Fairness. Avoid bias against individuals or groups, and define how fairness is measured where AI affects people.
• Privacy and Security. Protect the data used and produced by AI systems, in line with data protection rules.
• Humanity. Design AI that supports human choice rather than manipulating or deceiving people.
• Social and Environmental Benefits. Consider the wider effects of a system, not only its business value.
• Reliability and Safety. Test that the system works as intended and does not cause harm when conditions change.
• Transparency and Explainability. Be able to explain what the system does and tell people when they are dealing with AI.
• Accountability and Responsibility. Named people remain answerable for outcomes, supported by human oversight and documentation.

The principles also group AI systems into four risk levels, which give a ready-made basis for internal AI risk assessment:

• Little or no risk (SDAIA's example is a spam filter): no restrictions, though ethical compliance is recommended.
• Limited risk: subject to the ethics principles.
• High risk to basic rights: conformity assessments before and after deployment, plus any relevant statutory requirements.
• Unacceptable risk to safety, livelihood or rights, such as social profiling or exploitation of children: not allowed.

The document divides the AI lifecycle into four stages (plan and design, prepare input data, build and validate, deploy and monitor) and attaches checklist questions to each, so controls can be built into projects rather than added at the end.

AI governance in Saudi Arabia: what an internal framework should contain

A responsible AI policy does not need to be long, but it needs to answer eight questions clearly.

1. Roles. Who approves AI use, who owns each system and who checks it. SDAIA's principles describe an entity head or Chief Data Officer, a compliance officer, a Responsible AI Officer and an AI System Assessor. A mid-sized company can combine these roles, as long as the person who builds or buys a system is not the only one reviewing it.
2. AI use-case inventory. Every AI tool and feature in use, its owner, purpose, users, data and vendor, including AI built into software you already license.
3. Risk tiers. Each use case mapped to the four SDAIA risk levels, with stronger controls for higher tiers.
4. Data rules. Which data classes may go into which tools. A simple rule: no personal, client-confidential or commercially sensitive data in public generative AI tools without an approved enterprise agreement and a documented assessment.
5. Human oversight. Which outputs must be reviewed by a person before use, especially anything sent to customers, regulators or affecting employment, credit or pricing decisions.
6. Vendor review. Questions every AI supplier must answer on data storage location, use of your data for model training, security, subprocessors and exit.
7. Incident handling. How staff report a wrong, biased or harmful output or a data leak, and how that links to your security incident and PDPL breach process.
8. Training. Short, role-based training on acceptable use, data rules and checking outputs, refreshed when tools change.

Our AI consulting service for strategy and governance helps companies draft this policy and decide which roles to combine.

What an AI audit in KSA checks: a practical checklist

An AI audit tests whether the framework works in practice. Expect a reviewer to look at:

• Inventory completeness. Compare the register with software licences, browser extensions, expense claims and network logs to find unlisted tools.
• Data sources and consent. What data each system uses, the lawful basis for personal data, and whether the privacy policy describes the AI processing.
• Model and vendor risk. Contract terms, data retention, training on customer data, hosting and transfers outside the Kingdom.
• Accuracy and bias testing. A test set drawn from real cases, including Arabic and mixed-language inputs, with error rates checked across relevant groups.
• Security. Access control, prompt injection and data leakage risks, API keys and integration permissions. Our cybersecurity risk assessment guide explains how this fits a wider security review.
• Documentation. Purpose, design decisions, versions, test results and approvals recorded for each system.
• Human in the loop. Evidence that required reviews actually happen, not only that a policy says they should.
• Logging and monitoring. Records of inputs, outputs and overrides kept long enough to investigate a complaint, with periodic performance checks.

The output should be a findings report rated by risk, with owners and dates. For an independent review, see our AI audit service for governance and compliance in Saudi Arabia.

PDPL and AI: where personal data rules apply

The Personal Data Protection Law does not contain AI-specific articles, but it applies whenever an AI system processes personal data. The provisions that matter most are:

• Purpose and minimisation (Article 11). Collect only what the purpose requires. Reusing customer data to train or prompt a model for a new purpose needs its own assessment.
• Privacy policy (Article 12). Your policy should cover processing carried out with AI tools.
• Processors (Article 8). An AI vendor processing personal data for you must provide adequate guarantees, and you must monitor its compliance.
• Security and breach notification (Articles 19 and 20). Personal data pasted into an uncontrolled tool can become a reportable incident.
• Impact assessment (Article 22). Assess processing for products and services according to the nature of the activity; higher-risk AI use cases are natural candidates.
• Transfers (Article 29). Many AI services are hosted abroad, so transfer conditions apply.

For consent, notices, data subject rights and breach timelines in more detail, read our PDPL compliance checklist for websites and marketing teams.

A 90-day plan and a simple AI governance maturity table

Days 1-30: find and freeze

• Appoint an executive owner and a working group from IT, legal or compliance, data protection and one business unit.
• Run a short staff survey and licence review to build the first AI inventory.
• Issue an interim acceptable-use note: approved tools, and no confidential or personal data in public tools.

Days 31-60: assess and decide

• Assign each use case a risk level using the SDAIA categories.
• Review contracts and data terms for the main AI vendors.
• Draft the AI policy, data rules and human review requirements, and get management approval.

Days 61-90: control and prove

• Test the highest-risk use case for accuracy, bias and security, and document the result.
• Train staff on the approved policy and set up an incident reporting route.
• Agree a review cycle and plan the first internal or external AI audit.

When a pilot proves value and needs to scale under these controls, our guide on moving generative AI from pilot to production covers the next stage.

Common AI governance mistakes Saudi companies make

• Shadow AI. Banning tools without offering an approved alternative pushes use onto personal accounts, where you have no visibility or contract.
• Pasting confidential data into public tools. SDAIA's public guidelines warn that prompts can be used to develop models and that leaked information is hard to remove, and they recommend organisational usage policies and staff training.
• No owner. When AI belongs to everyone, nobody checks output quality, renews vendor reviews or answers a customer complaint.
• Policy without evidence. A signed policy with no logs, test results or review records will not withstand an audit or a regulator's question.
• Ignoring embedded AI. AI features switched on inside existing software are easy to miss in the inventory.
• No disclosure. Customers dealing with a chatbot or AI-generated content should be told, and SDAIA's public guidelines suggest offering a non-automated channel for people who prefer one.

Frequently Asked Questions

Need a clear AI policy, inventory and audit plan for your company in Saudi Arabia? Contact Sokrab Saudi Arabia today.