What Saudi Arabia's AI Risk Management Framework Means For Organisations

In April 2026, the Saudi Data and AI Authority (SDAIA) published the National Artificial Intelligence Risk Management Framework (الإطار الوطني لإدارة مخاطر الذكاء الاصطناعي).

This is the first national-level guide that sets out a single approach for finding, assessing, treating and monitoring artificial intelligence (AI) risks. It was issued under Cabinet Resolution No. 292 dated 27/4/1441H, which made SDAIA the country's main authority for all data and AI matters—covering regulation, development, oversight and coordination across sectors. The framework turns that broad role into a hands-on governance tool.

As organisations increasingly adopt artificial intelligence across their operations, regulators are placing greater emphasis on governance, oversight and accountability. AI is now central to how both government and private entities deliver services, enabling powerful analysis, automated decisions and large-scale use of data. However, these capabilities also create new challenges relating to privacy, ethics, fairness, regulatory compliance and cybersecurity that traditional IT governance was never designed to handle.

SDAIA views effective AI risk management as essential to supporting trustworthy AI adoption, protecting individual's rights and maintaining public trust. The framework supports Saudi Vision 2030 and the National Data and AI Strategy, based on the idea that good governance is what allows large-scale AI investment to move forward safely.

The framework gives entities a step-by-step method for making smart technology decisions and keeping systems safe as they grow. It is aimed at three groups: (i) system developers, who should build risk management into how they design, train and test models; (ii) system operators, who need clear rules for running and watching over AI in daily use; and (iii) policymakers, who must check whether current regulations are enough and create balanced, risk-based policies.

Which organisations and AI systems does the framework apply to?

The framework is comprehensive in scope while maintaining a flexible approach. It applies to all government and private organisations in the Kingdom, no matter their sector or how far along they are in adopting digital tools. It is flexible enough to work across very different levels of AI use. It focuses on high-impact systems, including, predictive models, natural language processing, image and video analysis, smart automation, and general-purpose models like large language models, because these are most likely to cause real harm if something goes wrong.

Importantly, the framework is intended as a governance and risk management tool rather than a technical standard. It helps leaders make decisions about governance, regulation and operations, but it does not set performance benchmarks or model-testing rules. Instead, it provides principles, a common vocabulary, a repeatable process and tools that entities can use to build their own internal policies based on their specific systems and risk tolerance. This keeps the framework useful even as the technology keeps changing.

How was the framework developed?

SDAIA built the framework using a four-step process designed to balance the Kingdom's local needs with global best practice. First, it reviewed how widely AI is being used in Saudi Arabia, the risks that already exist and the national rules that are already in place. Second, it looked at leading international AI risk management frameworks to find strengths, gaps and approaches that could work locally in the Kingdom. Third, it carried out expert consultations and workshops with specialists and relevant entities. Fourth, it conducted a user needs study to understand who would use the framework, what their roles are and how much detail it should include. The final framework brings together general principles, a governance structure and a working methodology for identifying, assessing and managing AI risk in one document.

How does the framework define AI risk?

The framework defines risk as the combination of three things: (i) how likely it is that a system causes unwanted harm, (ii) how bad that harm could be, and (iii) the uncertainty around how the model behaves when it interacts with data and its working environment. That last element—uncertainty—is what makes AI risk different from normal operational risk. Even a well-tested system can act in unexpected ways when it meets conditions it was not trained on. Risk can show up as the system failing to do what it was supposed to, or as direct harm to people, entities, society, the environment or business operations.

Most AI risk comes from five sources: (i) poor data quality, (ii) flaws in how the model was designed, (iii) problems when connecting with other systems, (iv) unpredictable user behaviour and (v) a lack of proper organisational controls. The framework looks at harm on three levels: (i) individual (people's rights, safety and economic chances), (ii) organisational (disrupted operations, money lost, damaged reputation and regulatory trouble) and (iii) societal/environmental (effects on supply chains, natural resources and the broader ecosystem).

What makes AI risk different from traditional technology risk?

Four key challenges make AI risk harder to manage than other traditional technology risks.

• First, third-party risk is common. Most systems use outside data, pre-built models or external software parts. Differences in how risks are measured plus limited openness between builders and users can make effective assessment tough.
• Second, there is a lack of good, standard measurements. Existing indicators often show only part of the picture and may miss how different groups are affected differently.
• Third, risks change over time. As the system moves from development to deployment to daily use, with hidden risks appearing or getting worse as usage grows.
• Fourth, there is a gap between testing and the real world. What works in a controlled setting rarely captures the full range of real-life situations, which is why ongoing monitoring after launch is essential.

AI risk is also different from traditional software risk in its nature. Data dependency means the training data may not match real-world use, may be incomplete or biased and often lacks a reliable reference point to compare against, quietly making outputs less accurate, reliable or fair. Performance drifts over time because retraining, updates or changing real-world data can make once-good datasets outdated. Complexity is on another level, with huge numbers of decision points making it hard to predict or explain behaviour, especially when large models show unexpected new abilities. Finally, testing can be very difficult because there is no single standard that covers every possible situation, and reproducing the same results is hard when models and conditions keep changing.

The framework's governance principles

The framework's general principles are based on Islamic values, including justice, transparency, protecting rights, human dignity and doing no harm. These principles are intended to ensure that AI systems are designed and used in ways that put responsibility and fairness first, rather than treating ethics as an afterthought.

Building on these foundations, the framework draws seven AI Ethics Principles:

• Integrity and fairness - reducing bias and discrimination from data or algorithms at every stage.
• Privacy and security - protecting personal data and blocking unauthorised access throughout the system's life.
• Humanity - building AI ethically based on human rights and cultural values, avoiding deception and keeping humans in control of choices.
• Social and environmental benefits - making a positive contribution to sustainability without causing harm.
• Reliability and safety - making sure systems work as expected and keeping unintended harm to a minimum.
• Transparency and explainability - making it possible to understand how the system makes decisions, especially when the stakes are high.
• Accountability and responsibility - clear roles, human oversight throughout, and measures to protect people's rights.

Three more pillars round out the structure: AI Regulations (the laws and policies that govern how AI is built and run), Data Regulations (rules for collecting, processing, storing, and using data, with a focus on personal and sensitive information), and Sector-Specific Regulations (recognising that risks look different in healthcare, finance, education, and other fields depending on data sensitivity, who is affected, and what decisions are being made). Together, these create a compliance structure that works both across sectors and within them.

The five-phase risk management process

At its core, the framework follows a five-phase process that repeats continuously, not something you do once and forget about.

Phase 1 - Context and Scope Definition

Entities describe the system and how it will be used (its type, purpose, features, allowed vs. not-allowed uses and who will use it). They list all inputs and outputs, sorted by type, sensitivity and quality, and map out how data flows and who is involved. They define how much the system does on its own versus how much a person is involved, including where human checks are required. Finally, they set out a plan for the system's lifecycle and how changes will be managed—covering the current stage, how often updates happen, what triggers a new risk check and how users will be told about changes.

Phase 2 - Risk Identification

Risks are listed and sorted into seven categories: (i) Bias, Discrimination and Harm (unfair treatment, hate speech, unequal performance across groups); (ii) Privacy and Security (data leaks, identity theft, system weaknesses); (iii) Misinformation (wrong or misleading outputs, information bubbles); (iv) Harmful Use (intentional misuse for disinformation, spying, fraud, or cyberattacks); (v) Human-Machine Interaction (over-reliance on the system, too much trust, loss of human control); (vi) Socioeconomic and Environmental Impacts (growing inequality, job losses, carbon footprint); and (vii) Safety and Limitations (the system doing things it was not meant to, poor reliability, risks from multiple systems working together). Each risk is then described by its source (system or person), whether it was intentional, when it might appear (before or after launch) and its weak points.

Phase 3 - Risk Assessment

Likelihood is rated on four levels: Rare (less than 1% chance per year), Unlikely (1–10%), Likely (10–50%), or Near-Certain (50% or more). This is based on how widely the system is used, how strong the safeguards are, how easy it is for things to go wrong, and whether similar problems have happened before. Impact is also rated on four levels: Low, Medium, High, or Catastrophic. These are judged by how severe the harm is, how many people or systems are affected, how sensitive the data or decisions are and how quickly things can be fixed. Catastrophic is saved for the worst cases—threats to public safety, loss of basic rights or large leaks of sensitive data. The two scores are multiplied to get the Risk Level = Likelihood × Impact, giving a number from 1 to 16 shown on a color-coded chart (Low 1–2 green, Medium 3–6 yellow, High 8–12 orange, Catastrophic 16 red). This gives everyone, managers and regulators alike, a shared way to compare and prioritise risks.

Phase 4 - Risk Treatment

Entities pick from four options: Avoidance (change the design, limit use or stop the system), Mitigation (put in place technical, organisational or operational safeguards), Transfer (shift some risk through contracts, insurance or supplier agreements—though this does not remove the organisation's own governance duties), and Acceptance (live with the remaining risk if it is within agreed limits, with a plan to review it regularly). Each safeguard gets an owner, a timeline and success measures. After that, the remaining risk is checked again and a formal decision is made to approve, launch with conditions, limit, or stop the system.

Phase 5 - Monitoring and Review

This last phase keeps three things running: ongoing monitoring of performance, data drift, error rates, and fairness against set warning levels; scheduled reviews that update the risk register when something changes (new data, model updates, wider use, or new regulations); and a system for spotting and responding to incidents, including reporting, escalation, finding the root cause and recording lessons learned. Together, these turn the framework from a one-time checklist into a living governance practice.

The framework in practice: a government LLM use case

The appendix shows how the framework can be applied in practice through the example of a government agency using a large language model (LLM) to help staff in preparing internal reports, including periodic updates, memos, meeting summaries and first drafts. The example assumes a low level of automation. All outputs are reviewed by a person before use, the system operates on internal or approved government infrastructure, and only non-sensitive, non-classified information can be used. Even in this relatively low-risk scenario, the framework finds eight separate risks that are tracked through all five phases. All eight end up at a medium risk level once likelihood and impact are multiplied together. This shows how a careful process can turn what seems like a low-stakes writing tool into a fully documented, well-justified risk position. The real value of the appendix is showing, step by step, how the abstract method works against an everyday use case.

What does the framework mean for organisations operating in Saudi Arabia?

The framework is built around what makes AI special: it changes fast, behaves in hard-to-predict ways, and keeps evolving. It draws on scientific best practices and global governance standards while adapting them for the Kingdom. It is designed to work across all sectors and system types, no matter how complex or mature they are. Rather than being a rigid technical rulebook, it serves as a flexible, practical guide. This puts the Kingdom among a growing group of countries moving from high-level AI principles toward hands-on risk methods—while standing out by grounding its ethical foundation in Islamic values alongside internationally recognised best practice.

For legal and compliance professionals, the framework matters beyond just its immediate guidance. It sets a clear expectation that AI risk governance will be checked in a structured way across Saudi entities. It offers a practical method that can be used in compliance programs, contract risk-sharing and technology due diligence. It also lays out accountability principles that will likely shape future enforcement and liability decisions as the Kingdom's AI ecosystem grows—helping organisations keep up with technological change while strengthening the country's overall readiness.

Organisations developing, deploying or procuring AI systems in the Kingdom should consider how the framework aligns with their existing governance arrangements and risk management practices. If you would like to discuss the implications of the framework for your organisation, please contact Christine Khoury or Renad AlHarbi.

Read the original article on GowlingWLG.com