AI & LLM Security in Egypt

AI & LLM security in Egypt protects AI and Large Language Model applications from prompt injection, data leakage, model poisoning and excessive agency. Egypt published an Egyptian Charter for Responsible AI and a National AI Strategy (now in its second edition), with a strong focus on Arabic natural-language processing and regional AI leadership. CyberSigma delivers LLM red-teaming and AI governance mapped to the Data Protection Center and NTRA 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 Egypt organisations

Egypt published an Egyptian Charter for Responsible AI and a National AI Strategy (now in its second edition), with a strong focus on Arabic natural-language processing and regional AI leadership. That momentum means Egypt 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 the Data Protection Center and NTRA 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 the Data Protection Center and NTRA.
• Secure AI adoption — GenAI usage policy, shadow-AI and data-leak controls.

The Charter for Responsible AI and Arabic NLP

As Egyptian organisations build Arabic-language AI and chatbots for government and financial services, the Charter for Responsible AI sets ethical expectations while Egypt's Personal Data Protection Law (Law 151 of 2020) governs personal data in training and outputs.

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 Egypt 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 Egypt

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

• Egypt Personal Data Protection Law (Law 151/2020) for AI and training data.
• Egyptian Charter for Responsible AI and National AI Strategy alignment.
• NTRA and National Cybersecurity Strategy expectations.
• ISO/IEC 42001 + NIST AI RMF.

What is Egypt's Charter for Responsible AI?

It is a national framework setting ethical principles for AI development and use. We align your AI deployment to it alongside the Personal Data Protection Law.

Is Egypt's data protection law in force for AI?

Egypt's PDPL (Law 151/2020) governs personal data, including data used by AI. We assess lawful basis, security and transfer rules for your AI systems.

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.