Saudi Arabia’s AI Leadership: Securing Public Sector Innovation

Every other country in this index is trying to get its public servants to use AI. Saudi Arabia has a different problem: making sure the AI use that’s already happening at massive scale is secure, governed, and sustainable.

Key Takeaways

1. Saudi Arabia Ranks First in the World for Public Sector AI Adoption. Saudi Arabia scored 66 out of 100 on the Public Sector AI Adoption Index — 8 points ahead of second-place Singapore and India (both at 58). KSA placed first on every single dimension in the index: enthusiasm, empowerment, enablement, embedding, and education. No other country came close to this level of across-the-board performance.
2. 98% of Saudi Public Servants Have Used AI at Work — Two-Thirds Use It Every Day. AI use in the Saudi public sector isn’t experimental. It’s routine. Two-thirds of public servants report using AI tools at work every day — the highest daily usage rate in the index. Nearly half have been using AI for more than a year. This is not a pilot programme. This is scaled, system-wide adoption.
3. KSA’s Top-Down Approach Delivered What Other Countries Are Still Debating. Through SDAIA and the National Strategy for Data and AI, Saudi Arabia combined a strong political mandate with central investment, enterprise-wide tool rollout, and clear leadership permission. 77% say their institution has invested in AI. 84% received employer-provided training. 95% are optimistic about AI in the public sector. The lesson: Clear direction from the top works.
4. But Half of Saudi Public Servants Say Their AI Training Is About Ticking Boxes. 50% report that most AI training provided by their organisation is about compliance rather than building real capabilities. 52% say training comes too late. 51% rarely have time or space to focus on AI training. As AI use moves from basic tasks to advanced enterprise use cases — and as agentic AI systems introduce autonomous decision-making — this quality gap becomes a governance risk, not just a skills risk.
5. The Next Challenge Isn’t Adoption — It’s Securing What’s Already Been Built. Saudi Arabia has solved the adoption problem that paralyses most governments. The question now is whether the data governance infrastructure can keep pace with the volume, velocity, and sensitivity of AI-data interactions happening across the public sector every day. When two-thirds of a workforce uses AI daily, the attack surface, compliance exposure, and data protection requirements are fundamentally different from countries where adoption is still emerging.

The Public Sector AI Adoption Index 2026, released recently by Public First for the Center for Data Innovation with sponsorship from Google, surveyed 3,335 public servants across 10 countries — including 324 in Saudi Arabia. KSA scored 66 out of 100, placing first in the index by a significant margin. Singapore and India tied for second at 58. South Africa came fourth at 55. The United States, for context, scored 45 and ranked seventh.

Saudi Arabia didn’t just win. It won on every dimension — enthusiasm (79), education (68), empowerment (69), enablement (55), and embedding (60). No other country placed first across all five. And the headline statistics are extraordinary: 98% have used AI at work, two-thirds use it every day, 95% are optimistic, and 89% describe AI as empowering.

This is what public sector AI adoption looks like when a government decides to make it happen. The question is what comes next — because the challenges of leading are fundamentally different from the challenges of catching up.

What KSA Got Right That Other Governments Haven’t

The index data makes clear why Saudi Arabia is so far ahead. It isn’t technology. It isn’t spending. It’s the combination of five things delivered simultaneously — the same five things that every other country in this study is struggling to provide individually, let alone together.

First, an unambiguous political mandate. AI was positioned as a core enabler of Vision 2030, not as an experiment or a risk to be managed. The Saudi Data and AI Authority (SDAIA) and the National Strategy for Data and AI created the institutional architecture. The message from leadership was clear: AI is expected, supported, and central to modernising the state.

Second, enterprise-wide tool rollout. 77% of Saudi public servants say their institution has invested in AI. KSA consistently ranks at the top for access to enterprise-level tools, in-house or adapted AI systems, and approved public tools. This is the critical difference between KSA and countries like Brazil (where enthusiasm is high but enablement is the lowest in the index) or the U.K. (where tools exist but access is uneven).

Third, clear rules and strong permission structures. Saudi Arabia’s empowerment score (69/100) is the highest in the index by a wide margin. Large shares of public servants report that their organisation has a formal policy promoting AI use. Rules are perceived as balanced, and leadership is seen as modelling effective AI use. Compare this to the U.S., where more than one in three public servants don’t know whether their organisation has a formal AI policy.

Fourth, extensive training provision. 84% say their employer provided AI training. More than 1 in 3 report that their organisation played a primary role in teaching or supporting their AI use. Saudi Arabia ranks at the top internationally for manager- and IT-led initiation into AI use.

Fifth, cultural momentum. 79% want AI to dramatically change their day-to-day work — the highest appetite for AI-driven transformation in the index. 95% are optimistic. Colleagues are excited. AI isn’t perceived as a threat — it’s perceived as progress.

The result is a public sector where AI is normalised. Not piloted. Not debated. Not confined to specialist teams. Normalised. That’s an achievement no other country in this index has matched.

The Risks That Come With Leading

But leading creates its own risks — and the index data points to three that Saudi Arabia’s government and security leaders need to address as AI use deepens.

The first is training quality. The index reveals a significant gap between training coverage and training depth. 84% have received training — but 50% say most of it is about ticking boxes rather than building real capabilities. 52% say training opportunities come too late, after changes have already happened. 51% rarely have time or space to focus on AI training. 45% say training feels like an afterthought.

When a workforce is using AI every day for basic tasks, surface-level training is manageable. When that workforce moves into advanced analytics, system integration, automation, and AI-enabled services — as Saudi Arabia’s national strategy envisions — the gap between compliance-driven training and genuine capability becomes a governance risk. Public servants making consequential decisions with AI tools they haven’t been trained to evaluate critically creates exposure that goes beyond productivity into accuracy, bias, and accountability.

The second risk is data governance at scale. When 98% of a workforce is using AI and two-thirds are doing so daily, the volume of AI-data interactions is orders of magnitude larger than in any other country in this study. Every interaction is a potential data touchpoint — citizen information being ingested, processed, summarised, or analysed by AI systems. The question is whether Saudi Arabia’s data governance infrastructure has scaled at the same pace as its AI adoption.

This challenge is intensifying as AI shifts from passive tools to active agents. Agentic AI systems don’t wait for prompts — they execute multi-step processes, access databases, and interact with external APIs with substantial independence. At KSA’s scale of adoption, the proliferation of AI agents across departments would create thousands of non-human identities requiring API access, machine-to-machine authentication, and real-time policy enforcement that traditional identity management systems were not designed to handle. Data-layer security with zero-trust governance and unified visibility across every interaction — whether initiated by a human or an AI agent — is essential infrastructure for a government operating at this volume.

This is where the challenge shifts from “how do we get people to use AI” to “how do we maintain visibility, control, and compliance across millions of daily AI-data interactions.” Most countries in this index don’t have this problem because adoption is too low to create meaningful data exposure. Saudi Arabia does.

The third risk is the transition from individual productivity to enterprise-grade use. The index shows that Saudi Arabia’s AI adoption is still predominantly centred on individual tasks — drafting, analysis, summarisation. The national strategy calls for a shift toward system integration, advanced analytics, automation, and AI-enabled public services. That transition introduces fundamentally different data governance requirements: AI systems accessing structured databases, processing cross-departmental datasets, and making or supporting decisions that affect citizens at scale. The data protection, auditability, and accountability requirements for enterprise AI are categorically different from those for an individual public servant using ChatGPT to draft a memo.

The Data Governance Infrastructure KSA Needs Now

Saudi Arabia built the adoption infrastructure. Now it needs the data governance infrastructure to match.

Most of the countries in this index need to solve for adoption first. Saudi Arabia is past that. Its challenge is ensuring that the AI interactions happening at scale — every day, across every department — are visible, governed, logged, and compliant.

This requires a fundamentally different approach than what most governments are building. Not policies that encourage adoption (KSA has those). Not training programmes that build awareness (KSA has those too). What KSA needs is operational data governance infrastructure — the systems that sit between AI tools and sensitive data, enforcing policies in real time, logging every interaction, and providing the forensic capability to respond when something goes wrong.

Data security posture management (DSPM) capabilities can discover and classify sensitive data across repositories, including data being ingested into AI systems. Automated policy enforcement can block privileged or confidential data from AI ingestion based on classification labels — critical when the volume of AI interactions is as high as KSA’s. Comprehensive audit logs can track all AI-data interactions with user ID, timestamp, data accessed, and the AI system used. And incident response capabilities specific to AI data exposure scenarios provide the forensic infrastructure that every government needs but few have built.

The capabilities needed are clear: integration of DSPM with automated policy enforcement and immutable audit logs, with AI-powered anomaly detection that flags suspicious activity — like an agent suddenly requesting large volumes of data it doesn’t normally access. Kiteworks’ Private Data Network and Secure MCP Server deliver this approach, keeping sensitive data within the private network while enabling AI productivity with tools like Claude, ChatGPT, and Copilot. Existing governance frameworks (RBAC/ABAC) extend to all AI interactions — including those initiated by autonomous agents — every AI operation is logged for compliance and forensics, and sensitive content never leaves the trusted environment. For Saudi Arabia, this kind of infrastructure addresses the unique challenge of governing AI not at the point of adoption — which KSA has already achieved — but at the point of scale.

The alternative is hoping that policies, training, and cultural enthusiasm alone will prevent data exposure across millions of daily AI interactions. For a country that has led the world in executing a top-down AI strategy, relying on hope would be an uncharacteristic departure from the discipline that got KSA to number one.

What the Rest of the World Should Learn From KSA

The other nine countries in this index are all struggling with some version of the same problem: how to get public servants to use AI. Saudi Arabia has shown that the answer is not complicated — even if it’s hard to execute.

Clear mandate from the top. Enterprise tools, not personal accounts. Formal policies that promote use, not ambiguity that suppresses it. Training delivered before people need it, not after. Cultural messaging that frames AI as progress, not risk.

Every country that ranks below KSA in this index — from Singapore at 58 to France at 42 — is failing on at least two of these five conditions. The U.S. (45) has tools but no clarity. The U.K. (47) has ambition but uneven execution. Germany (44) has confidence but no permission. France (42) has strategy but no relevance. Brazil (49) has enthusiasm but no infrastructure.

Saudi Arabia delivered all five simultaneously. That’s why it’s first.

But KSA’s own data also reveals the limits of the top-down model. Training that feels like box-ticking. Capability gaps as AI moves beyond basic use. And the data governance challenge of securing AI interactions at a scale no other government has achieved.

The lesson for other countries isn’t just what KSA built. It’s what KSA needs to build next — because the countries that solve data governance for AI at scale will be the ones that sustain their lead, not just the ones that won the adoption race.

Three Priorities for Sustaining KSA’s Lead

The index points to three actions that would help Saudi Arabia maintain and extend its position as AI use deepens across the public sector.

First, shift from introductory training to role-specific capability building — with data governance at the core. AI training in KSA is widespread but risks becoming a ceiling rather than a floor. The priority now is training that shows how AI reshapes particular functions, workflows, and decisions — not just how to use the tools. This should include data governance training: how to handle sensitive data when using AI, what constitutes appropriate versus inappropriate AI-data interactions, and how to escalate when something goes wrong. As AI tools evolve toward agentic capabilities, training should also address how to work alongside autonomous systems safely, including understanding what data AI agents can access and what guardrails are in place. Partnering with external providers and technology partners — as KSA public servants have themselves suggested — would help access a wider range of up-to-date, role-specific courses that go beyond internal compliance materials.

Second, deploy operational data governance infrastructure that matches the scale of adoption. Saudi Arabia’s AI usage volumes are unmatched in this index. The data governance infrastructure needs to match. This means DSPM capabilities to classify and protect sensitive data in real time, automated policy enforcement across all AI-data interactions, immutable audit logs that capture every touchpoint, and incident response capabilities specific to AI data exposure. This is especially critical as agentic AI systems enter government workflows, since autonomous agents require the same zero-trust governance as human users — with the added need for machine-to-machine authentication, sandboxed execution, and real-time anomaly detection. Platforms like Kiteworks’ Secure MCP Server demonstrate how to deliver this at scale — maintaining governance controls, compliance logging, and data protection while enabling the AI productivity that KSA’s public servants have already embraced. For KSA, this isn’t about enabling adoption. It’s about securing what’s already been built.

Third, create clear pathways from individual productivity to enterprise-grade AI use. 79% of Saudi public servants want AI to dramatically change their day-to-day work. Leadership can harness this momentum by focusing on enterprise use cases — system integration, advanced analytics, automation, and AI-enabled services — that go beyond individual tasks. Clear pathways from experimentation to scaled deployment, governed sandboxes for testing advanced use cases, and mechanisms to share learning across departments will help ensure that KSA’s early lead translates into sustained, high-impact AI capability. The data governance infrastructure described above is the prerequisite: Enterprise AI cannot scale safely without it.

The Stakes for the World’s Leading AI Government

Saudi Arabia’s first-place ranking in this index is a genuine achievement — the result of coordinated strategy, political will, institutional execution, and cultural momentum that no other country has matched. But rankings measure a moment. Sustainability measures what happens next.

Every day that two-thirds of KSA’s public sector uses AI creates both value and exposure. Every enterprise use case that moves from pilot to production introduces new data flows, new classification requirements, and new compliance obligations. Every public servant who moves from basic tasks to advanced analytics needs training that goes beyond ticking boxes. And as AI agents become more autonomous and more prevalent, the attack surface grows in tandem.

Saudi Arabia solved the adoption problem. The next problem — governing AI at scale while maintaining the speed and ambition that got KSA to number one — is harder. But for a government that turned a national AI strategy into the world’s leading public sector adoption in less than a decade, it’s also the kind of challenge that plays to its strengths.

The 324 Saudi public servants surveyed in this index are already using AI. They’re enthusiastic, confident, and productive. What they need now isn’t permission or tools. It’s the data governance infrastructure, the role-specific training, and the enterprise-grade security that ensures their government’s extraordinary AI momentum is protected — permanently.