Saudi Data & Artificial Intelligence Authority
Turn SDAIA responsible AI references into practical AI inventories, risk assessments, oversight, evaluation and monitoring for Saudi organizations.
SDAIA's AI Ethics Principles address responsible development and use across the AI lifecycle. They cover fairness, privacy and security, humanity, social and environmental benefits, reliability and safety, transparency and explainability, and accountability and responsibility. These principles should influence decisions about the use case, data, model, deployment and operation rather than appearing only in a policy statement issued after procurement.
SDAIA also publishes the National AI Risk Management Framework. Its official publication listing and Saudi Press Agency launch announcement confirm a national reference for government and private-sector entities to identify, assess, treat and monitor AI risks. IRIS Technology can help translate these references into an operational governance program that connects accountable decisions with data controls, security architecture, model evaluation and ongoing review. The implementation should reflect the system's purpose and potential consequences.
Establish which AI systems the organization develops, procures, integrates or uses, including externally hosted models and AI features embedded in existing software. Review relevant SDAIA references, personal data requirements, cybersecurity controls, sector directions and contracts. Do not treat every principle-based publication as an identical legal obligation or assume that one generic AI policy resolves all requirements for every use case.
Define the assessment boundary across inputs, retrieval sources, models, prompts, tools, outputs and human decisions. Risks can change when a model, dataset, integration or business process changes. The national risk framework supports context and scope definition, identification and assessment, treatment, and continuing monitoring and review. Establish review triggers and evidence proportionate to the actual exposure, including third-party dependencies and the consequences of incorrect or inappropriate outputs.
| Focus area | Implementation examples | Evidence to maintain |
|---|---|---|
| AI inventory and accountability | Identify use cases, system owners, suppliers and decision authority. | AI register, ownership assignments and approved use boundaries. |
| Risk and impact assessment | Assess intended use, affected people, misuse and failure consequences. | Documented assessments, treatment decisions and approval conditions. |
| Data and supplier governance | Review input rights, privacy, retention and external processing. | Data flows, supplier reviews and documented processing arrangements. |
| Evaluation and safeguards | Test reliability, security, appropriate behavior and human intervention. | Evaluation results, escalation procedures and recorded release decisions. |
| Monitoring and change | Track incidents, drift, service changes and effectiveness of safeguards. | Monitoring records, review triggers and corrective action evidence. |
Confirm which repositories the assistant can search, how user permissions are enforced and whether prompts or retrieved content reach external providers. Test inappropriate disclosure and unsupported answers before release.
Identify who remains accountable for the outcome, how users can challenge errors and when a human must review the recommendation. Evaluate performance with representative cases and record limitations.
Agree the scope, responsible owners and acceptance criteria before work begins. The approach is tailored to your systems, suppliers, operating constraints and existing evidence.
Review applicability, current controls and evidence. Identify gaps and dependencies, distinguish design from operating effectiveness, and prioritize actions with accountable owners.
Translate approved requirements into architecture, policies, processes and technical changes. Reuse effective controls while keeping national and sector mappings distinct.
Configure agreed safeguards, document procedures and validate representative workflows within authorized access and change windows. Record exceptions and residual risks.
Organize evidence, transfer operating knowledge and define a review cadence. Track corrective actions as systems, services, suppliers and requirements change.
Deliverables, depth, access and timing depend on the statement of work. This is readiness and implementation support; it does not include a promise of regulator acceptance or formal certification.
SDAIA lists the National AI Risk Management Framework in its publications and the official Saudi Press Agency reports its launch in July 2026. Confirm the applicable document and directions when establishing the assessment baseline.
No. Procurement, deployment and use introduce governance decisions too. Include embedded AI, external APIs, assistants and supplier-operated services in the inventory.
It sets expectations, but evidence must show assessments, owners, safeguards, evaluations and review in operation. Connect the policy to actual product and service decisions.
Map applicable PDPL obligations and cybersecurity controls to the AI data flows and architecture. Responsible AI governance coordinates these disciplines while addressing additional model and decision risks.
No. IRIS can support assessment and implementation within an agreed scope. Acceptance, regulatory judgments and any official recognition remain with the relevant authority or scheme.
Read the linked authority publications for the binding scope, wording and applicable updates. The implementation examples on this page are practical recommendations, not a reproduction of every regulatory requirement.
General readiness and technology information, not a legal opinion, regulator endorsement or guarantee of compliance. Confirm applicability, current versions and sector directions with the competent authority and qualified advisers.
Bring your services, systems, supplier dependencies and existing findings. We will help turn them into a practical assessment and implementation scope.
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