AI & LLM Security in Saudi Arabia

AI & LLM security in Saudi Arabia protects AI and Large Language Model applications from prompt injection, data leakage, model poisoning and excessive agency. Saudi Arabia's SDAIA issued AI Ethics Principles and Generative AI guidelines, launched the HUMAIN initiative and the Arabic ALLaM large language model, and treats AI as central to Vision 2030. CyberSigma delivers LLM red-teaming and AI governance mapped to SDAIA and the National Cybersecurity Authority (NCA) and the global frameworks (OWASP LLM Top 10, NIST AI RMF, ISO/IEC 42001, MITRE ATLAS). We are CERT-In empanelled and PCI QSA (CEMEA) authorised.

Secure AI adoption for Saudi Arabia organisations

Saudi Arabia's SDAIA issued AI Ethics Principles and Generative AI guidelines, launched the HUMAIN initiative and the Arabic ALLaM large language model, and treats AI as central to Vision 2030. That momentum means Saudi Arabia organisations must now show their AI is secure, governed and compliant — not just functional.

AI introduces failure modes traditional testing misses: chatbots manipulated into leaking data, AI agents coaxed into unauthorised actions, and poisoned models or datasets from public hubs. CyberSigma secures the full AI lifecycle — model, data, application, prompts, plugins and agents — and maps every finding to SDAIA and the National Cybersecurity Authority (NCA) and recognised global frameworks.

• LLM & GenAI application penetration testing and red-teaming (OWASP LLM Top 10).
• AI/ML model, pipeline and MLOps security assessment (MITRE ATLAS, Google SAIF).
• AI governance — ISO/IEC 42001 AI Management System and NIST AI RMF.
• Local alignment with SDAIA and the National Cybersecurity Authority (NCA).
• Secure AI adoption — GenAI usage policy, shadow-AI and data-leak controls.

Vision 2030, Arabic LLMs and the NCA

As Saudi entities deploy Arabic generative AI across government, energy and banking, SDAIA's Generative AI guidelines and the NCA's Essential Cybersecurity Controls set the bar — and the Personal Data Protection Law (PDPL) governs how training data and outputs handle personal data.

What we test (OWASP Top 10 for LLMs + MITRE ATLAS)

We adversarially test your LLM and GenAI applications the way a real attacker targeting a Saudi Arabia organisation would:

• Prompt injection — direct and indirect (documents, web pages, tools).
• Sensitive information disclosure — PII, secrets and system-prompt leakage.
• Insecure output handling — XSS, SSRF and code execution from model output.
• Excessive agency — agents/plugins taking unauthorised or destructive actions.
• Training-data poisoning and model/data supply-chain risks.
• Jailbreaks, guardrail bypass, model extraction and denial-of-wallet.

AI governance & compliance in Saudi Arabia

We turn the applicable frameworks into a prioritised, evidenced programme:

• Saudi Personal Data Protection Law (PDPL) for AI and training data.
• SDAIA AI Ethics Principles and Generative AI guidelines.
• NCA Essential Cybersecurity Controls for the underlying systems.
• ISO/IEC 42001 + NIST AI RMF.

What are SDAIA's Generative AI guidelines?

SDAIA has published guidelines for the responsible adoption of generative AI by both public and private bodies, covering acceptable use, risk and human oversight. We align your GenAI deployment to them.

Does the Saudi PDPL apply to AI training data?

Yes. Personal data used to train or operate AI falls under the PDPL, including lawful basis, transfer rules and security. We assess and document compliance.

How does AI red-teaming differ from normal penetration testing?

Traditional pen testing targets code and infrastructure; AI red-teaming additionally targets the model's behaviour via prompts, poisoned context and connected tools to make it leak data or act without authorisation. Mature programmes use both — we provide each and can combine them.