Saudi Arabia - Generative AI Guidelines

Overview

The "Generative AI Guidelines for Government" were published by the Saudi Data & AI Authority (SDAIA) in January 2024 to provide practical, risk‑based guidance to government entities and public servants on the responsible use of generative artificial intelligence. The Guidelines complement SDAIA's AI ethics principles and aim to balance innovation with protection of sensitive government data, personal privacy, national security, and fundamental rights. The publication is available from SDAIA's official publications repository (see SDAIA: Generative Artificial Intelligence Guidelines (Public & Government)) and is widely cited by legal analyses and sector trackers. The document frames GenAI as an opportunity to enhance public services while requiring clear governance, vendor due diligence, data classification, human oversight, and ongoing evaluation to prevent harms such as misinformation, data leakage, discriminatory outcomes, or threats to critical infrastructure.

Definitions

The Guidelines define key terms to ensure a common operational vocabulary across government bodies: "Generative AI" (models/systems that produce novel content such as text, images, audio, video or code); "data classification" (a regime labelling data as public, internal, sensitive or classified); "human‑in‑the‑loop" (human oversight for high‑risk outputs); "model provider" (third parties offering GenAI services); "prompt" (user input to a generative system); and "output verification" (processes to validate or fact‑check generated content). The definitions are practical and oriented to public‑sector workflows, emphasising provenance, traceability and the distinctive risks of generative outputs (hallucination, bias, and synthetic media). The Guidelines also map risks to categories used in SDAIA's AI Ethics Principles to enable consistent governance across instruments.

Governance and Institutional Framework

SDAIA places responsibility for compliance on each government entity, while positioning itself as the national oversight and guidance body. The Guidelines instruct entities to establish or empower internal Data Management Offices (DMOs) or AI governance units, to appoint Data Protection Officers (DPOs) where required by the Personal Data Protection Law, and to create roles for GenAI Champions, Risk Assessors, and Procurement Review Boards. They recommend inter‑agency coordination mechanisms and escalation pathways to SDAIA for high‑impact cases. The document also prescribes vendor risk assessments, contractual clauses for model security and data handling, and clear lines of accountability for outputs affecting third parties. SDAIA provides templates and checklists to operationalise these roles; see the SDAIA publications portal for the referenced templates (SDAIA Publications).

Key Focus Areas

The Guidelines highlight several priority domains where GenAI use demands special care: (1) Data protection and privacy — rigorous handling of personal and sensitive data and conformance with the Kingdom’s Personal Data Protection Law; (2) Information integrity — safeguards against misinformation, fabricated evidence, and deepfakes (including watermarking and provenance metadata where practicable); (3) National security and classified information — prohibitions or strict controls on any use of classified or defence‑related datasets; (4) Critical public services — human oversight and higher assurance levels for GenAI used in healthcare, justice, emergency response, and other high‑impact domains; (5) Vendor governance — due diligence, security testing, and contractual obligations on third‑party providers; and (6) Transparency and accountability — logging of inputs/outputs, impact assessments, and public documentation for services that affect citizens. The Guidelines operationalise these focus areas with concrete checklists, example scenarios, and recommended controls to mitigate hallucination, bias, and data exfiltration risks.

Implementation Framework

SDAIA recommends a phased implementation approach: initial risk‑screening for prospective use cases; pilot deployment under controlled conditions; formal approval and integration for approved uses; and continuous monitoring and retraining. The Guidelines require documented risk assessments that classify each GenAI application by impact level (low, medium, high) and list required mitigations. Technical recommendations include access controls, encryption, secure prompt handling, prompt engineering standards, output watermarking or labeling, model provenance logging, and regular vulnerability testing. For procurement, entities must require providers to disclose model training data provenance where feasible, security certifications, and incident response commitments. The Guidelines also urge capacity building (training programmes for users and decision makers) and the creation of cross‑agency AI incident response playbooks.

Monitoring and Evaluation

Monitoring is framed as continuous and multi‑layered: technical monitoring of model behaviour and outputs (drift, bias metrics, safety incidents), operational monitoring (user training uptake, incident response times), and periodic policy reviews. SDAIA recommends deploying logging and audit mechanisms to record prompts, inputs, outputs, and decisions made with GenAI assistance, subject to data minimisation and privacy constraints. Evaluation metrics include accuracy, hallucination rate, bias indicators, false positive/negative rates for critical tasks, and user satisfaction. The Guidelines encourage third‑party audits for high‑risk systems and require reporting of significant incidents to SDAIA within defined timelines. They also specify that monitoring data should be retained according to classification rules and be made available for oversight and accountability reviews.

Penalties, Liability, and Appeals

Although the Guidelines themselves are non‑binding, they make explicit that misuse of GenAI may lead to administrative or legal consequences under existing Saudi laws (e.g., Personal Data Protection Law, cybersecurity regulations and sectoral statutes). The document outlines internal disciplinary measures for staff who contravene the Guidelines and recommends contractual liability clauses for external providers. It explains appeal mechanisms for decisions taken under the Guidelines' processes (for example, where a use case is denied) and encourages entities to maintain transparent records of decision rationale. SDAIA signals that repeated or serious non‑compliance may prompt escalated oversight, audits, or referrals to competent authorities under relevant statutes.

Relationship to Other Instruments

The Guidelines explicitly align with SDAIA’s AI Ethics Principles (September 2023) and the Kingdom’s Personal Data Protection Law. They are positioned as operational guidance that integrates ethical principles into public‑sector practice and as preparatory instruments pending future statutory AI regulation. The document cross‑references existing cybersecurity standards, procurement rules, and sectoral compliance regimes (health, finance, justice) so that government entities can reconcile GenAI controls with other legal obligations. SDAIA also points to the AI Adoption Framework (published subsequently) as a companion guide for broader AI maturity and integration.

International Alignment

SDAIA designed the Guidelines to be consistent with international best practices and multilateral instruments, encouraging alignment with OECD recommendations, UNESCO’s AI ethics guidance, and peer public‑sector GenAI guidance issued by other national authorities. The Guidelines recommend that procurement and contractual templates reflect international standards for model safety, security testing, and transparency while preserving national security and cultural norms. SDAIA also highlights the need to monitor evolving international regulation (e.g., EU AI Act developments and global standards) and to update national guidance accordingly.

Implementation Timeline

Phase	Action	Suggested Timing
Phase 1	Immediate risk screening; adopt checklist for pilot uses	0–3 months from publication
Phase 2	Pilot deployments under licensed environments; staff training	3–6 months
Phase 3	Approval and controlled roll‑out of low/medium risk use cases	6–12 months
Phase 4	Full integration for approved services; periodic audits	12+ months

Compliance Checklist

Requirement	Yes/No
Data classification completed for the use case
Risk assessment (impact & mitigation) documented
Human‑in‑the‑loop controls for high‑risk outputs
Vendor due diligence and contractual clauses in place
Logging and provenance mechanisms implemented
User training completed and recorded

Sources and References

Source	Type
Saudi Data & AI Authority — Generative Artificial Intelligence Guidelines (Government/Public) (Jan 2024)	Primary Source
White & Case — AI Watch: Global regulatory tracker (Saudi Arabia)	Secondary Analysis
Baker McKenzie — Global Data & Cyber Handbook: Saudi Arabia	Secondary Analysis