KSA’s New AI Adoption Framework: What You Need to Know

5 min 45 sec April 7, 2026 (Edited)

Written by

Christine Khoury

Of Counsel

c.khoury@tamimi.com

View LinkedIn Profile

Artificial intelligence is moving faster than the policies that govern it. For businesses operating in Saudi Arabia, that gap is steadily closing. In alignment with Vision 2030, the Saudi Data & AI Authority (“SDAIA”) has issued the first KSA AI Adoption Framework 2025 (“Framework”). The Framework offers an actionable roadmap for government and businesses seeking to integrate AI into their operations in a responsible manner. The message is clear: AI in Saudi Arabia must be secure, measurable, and built for the long term. This article explains what the Framework requires, why it was issued, and what businesses must do now to comply.

Why This Framework?

To understand what the Framework changes, it is necessary to recognize the regulations that existed before it. Saudi Arabia’s regulatory approach to AI has moved through distinct phases. In 2023, SDAIA issued the final version of its AI Ethics Principles (Version 1.0), which represented the first AI legal framework in Saudi Arabia and defined AI ethics as a set of values. Alongside the AI Ethics Principles, the Personal Data Protection Law (“PDPL”) was issued to apply to any processing of personal data by any means that takes place within the Kingdom.

Though both the PDPL and AI Ethics Principles addressed the use of data, they left a structural gap. There was no unified, operational framework that guided businesses on how to build, govern, and scale AI in a measurable and accountable way. Although non-binding but the Framework fills that gap. It is not a replacement for the PDPL or the AI Ethics Principles. Both remain in force and are referenced throughout the Framework as compliance anchors. The Framework is not just an implementation tool; it is an intelligent system that aims to build flexible, data-driven governance capable of harnessing AI capabilities efficiently yet responsibly.

The Three Core Pillars

To better understand the Framework core structure, it organizes AI adoption into three interconnected pillars: Directions, Enablers, and Outcomes. Together, they define not just what to implement, but how to implement and measure progress.

Directions – Strategy, Governance, and Budget

The first pillar covers strategic vision, initiative design and governance effectiveness. Businesses must document an AI strategy with clear metrics tied to the objectives they seek to achieve from an AI adoption. This also measures the quality of technology initiative design, and the effectiveness of governance frameworks.

To ensure this, the Framework sets a phased roadmap across short (1 to 2 years), medium (3 to 5 years), and long term (5+ years) horizons. Therefore, businesses are recommended to have a dedicated AI budget with periodic reporting to senior management. Governance must move from informal to institutional: businesses should establish a dedicated AI governance unit, draft internal policies that define accountability for AI outputs and conduct legal gap assessment to identify areas not covered by existing laws. This approach allows businesses to actively mitigate any potential compliance risks.

Enablers – Data, Infrastructure, and People

The second pillar addresses important factors including staffing and talent retention, reliability of data, integration of AI infrastructure, growth of the human workforce, and continuous development.

On data, businesses must implement role-based access controls such as a Data Governance Office, audit data sources for provenance and licensing, and treat PDPL compliance as an embedded AI obligation not a separate data team function.

On infrastructure, businesses should design scalable systems aligned with national platforms such as G-Cloud and apply the Framework’s availability benchmark of less than 30 minutes of downtime per year, monitored through monthly readiness reporting.

On people, businesses must map AI roles to the National Occupational Standards Framework for Data and AI (“NOSF”), link training to SDAIA-accredited certifications, and formalize academic partnerships to build a graduate pipeline. This approach ensures the continuous development of a skilled and competent workforce.

Outcomes – Applications, Performance, and Impact

This pillar is about results. It examines how efficiently AI models are built and deployed. The key questions every organization should be asking are: Is the AI improving the service quality? Has it lowered the costs? Is the business performing better overall?

Accordingly, businesses must document the full AI model lifecycle, embed privacy and security by design into every stage of development, link AI output directly to business KPIs. The Framework sets an 8% operational efficiency improvement as key indicator of the success of AI initiatives. This means it reinforces AI as a driver of organization performance to validate that the AI initiative is delivering tangible business value.

Latest Updates

On 3 April 2026, SDAIA published a draft Responsible AI Policy for public consultation via the Istitlaa Platform (the official Saudi Arabian platform for collecting public opinion on regulatory matters).

The policy introduces a comprehensive governance framework for AI, signalling a shift from adoption to regulation. It emphasizes lifecycle governance, centralized oversight, and national data sovereignty.

Key strengths include a structured lifecycle-based model, integration of ethical and safety considerations, and a four-tier risk classification system. While aligned with global standards, it places stronger focus on sovereign control over data and infrastructure.

The framework clearly assigns responsibilities to developers, deployers, and operators, though effective implementation will require careful alignment with actual levels of control. It also grants regulators authority to request audits from foreign AI providers, raising important cross-border regulatory considerations. An AI ethics labeling system is introduced, but its effectiveness will depend on consistent application.

If this Policy is issued by SDAIA and deemed as binding policy, it will impact the Framework across all sectors in KSA.

Overall, the policy is ambitious and well-designed, but its success will depend on practical, scalable implementation mechanisms. Public feedback is open until 3 May 2026.

Key Takeaways for Businesses

Businesses operating in the Kingdom should consider the following actions:

(a) Governance and Legal: Review existing AI initiatives against the Framework’s governance and compliance requirements. Establish a designated AI governance unit, draft internal policies defining accountability for AI outputs, and develop or update internal AI policies addressing risk assessment.

(b) Data Foundations: Assess data governance practices to ensure alignment with PDPL and the Framework’s data management standards.

(c) Infrastructure: Elevate infrastructure readiness and identify gaps in scalability and resilience with a focus on integration with national platforms such as G-Cloud.

(d) Talent: Map roles to National Occupational Standards Framework for Data and AI; plan annual workforce growth; align professional development with SDAIA-accredited certifications; and build academic partnerships and graduate pipelines.

(e) Strategy and Phasing: Ensure alignment with National Strategy for Data and AI and Vision 2030 through phased roadmaps and measurable KPIs across short, medium and long-term value.

The Framework raises the bar, providing businesses with a structured, and authoritative reference point for every stage of AI adoption. The SDAIA is actively building the national maturity index against which organizations will be assessed. Businesses that treat this as a governance and legal priority, rather than a technology project, will be better placed for regulatory engagement, government partnership, and long-term AI driven performance. The question is no longer whether to comply — it is how quickly businesses can adapt.