The Regulatory Landscape for AI in Saudi Arabia and the GCC

The Saudi National Cybersecurity Authority (NCA) and the Saudi Arabian Monetary Authority (SAMA) have embedded AI governance expectations into their latest frameworks. The NCA Essential Cybersecurity Controls (ECC) and SAMA Cybersecurity Framework (CSF) now explicitly address the security of systems that incorporate artificial intelligence, machine learning, and large language models. Financial institutions and critical infrastructure operators must treat AI-driven systems with the same rigor as traditional IT assets—including threat modeling, access control, audit logging, and incident response.

The Saudi Personal Data Protection Law (PDPL) and its implementing regulations further constrain how enterprises may use AI for data processing and decision-making. Automated profiling, algorithmic decision-making, and cross-border data flows involving AI all trigger compliance obligations. Organizations must document the lawful basis for AI processing, implement transparency measures, and ensure individuals can exercise their rights—particularly the right to explanation when AI affects them.

Key Security Risks in AI Deployment

Security leaders must address risks that are unique to AI systems:

  • Model Poisoning and Adversarial Attacks: Training data can be manipulated to degrade model accuracy or inject hidden behaviors. Adversarial inputs—subtly modified data—can fool even well-trained models into wrong decisions, with real consequences in fraud detection, access control, or critical infrastructure automation.
  • Data Leakage and Privacy Violations: Large language models and generative AI systems can memorize and reproduce sensitive training data, including personal information, trade secrets, or confidential communications. Prompt injection attacks may extract proprietary model weights or cached sensitive data.
  • Supply Chain Compromise: Pre-trained models, fine-tuning datasets, and third-party AI services introduce dependencies that are difficult to audit. A compromised model provider or poisoned dataset can affect hundreds of downstream users.
  • Lack of Transparency and Auditability: Many AI models operate as "black boxes." Regulators and auditors increasingly demand explainability, yet many deep learning systems cannot reliably explain their decisions. This creates compliance gaps in regulated sectors.
  • Inadequate Access and Change Control: AI systems often run in development, staging, and production environments with weak separation. Model versioning, retraining pipelines, and API keys are frequently left uncontrolled, enabling insider threats and lateral movement.

Aligning AI Governance with SAMA CSF and NCA ECC

Effective AI governance requires integration with existing cybersecurity frameworks. The SAMA CSF and NCA ECC both demand:

  • Clear ownership and accountability for AI systems, including a designated data steward and security owner.
  • Risk assessment and threat modeling specific to each AI use case, documented in the organization's risk register.
  • Continuous monitoring and logging of model performance, data inputs, and access patterns, with alerts for anomalies.
  • Regular security testing, including adversarial testing and red-team exercises targeting AI components.
  • Incident response procedures tailored to AI failures—such as model drift, data poisoning, or inference-time attacks.
  • Third-party assessment and vendor management, ensuring AI service providers meet the same security and compliance standards as internal systems.

Practical Steps for Security Leaders

Organizations should establish an AI Security Steering Committee that brings together Chief Information Security Officer (CISO), Chief Data Officer (CDO), Chief Risk Officer (CRO), and business stakeholders. This committee should:

  • Inventory all AI and machine learning systems in use, including shadow AI and third-party services.
  • Classify AI systems by risk level and regulatory impact, using a framework aligned with SAMA and NCA expectations.
  • Implement data governance controls that enforce PDPL compliance and prevent unauthorized use of personal data in training.
  • Establish secure development practices for AI, including model versioning, reproducibility, and automated security testing in CI/CD pipelines.
  • Conduct regular AI security assessments and penetration testing, documenting findings and remediation in audit trails.
  • Build internal capability in AI security and threat detection, or partner with specialized vendors, to ensure ongoing vigilance.

AI governance is not a one-time project. As models evolve, threats emerge, and regulations mature, security leaders must maintain a continuous improvement mindset. Organizations that embed AI security into their SAMA CSF and NCA ECC compliance programs—rather than treating it as a separate initiative—will be best positioned to innovate responsibly and meet regulatory expectations in 2026 and beyond.