We Use AI to Secure AI

Who We Are

infosec.qa is the global AI security intelligence practice of the NomadX consulting family - and one of the first firms to build a purpose-built AI security intelligence methodology rather than adapting traditional penetration testing approaches to AI systems.

We operate from Dubai, UAE, and serve clients worldwide. Our work spans the full lifecycle of AI security intelligence: from initial AI attack surface assessment to ongoing AI threat intelligence, from regulatory risk frameworks to AI security training programs for engineering and security teams.

We were founded on a single observation: the AI threat landscape is evolving faster than security practices designed for traditional software. Prompt injection, model extraction, data poisoning, supply chain attacks on AI models - these are not theoretical threats. They are being actively researched, demonstrated, and deployed. The organizations best equipped to address them are the ones that have built security intelligence practices specifically for AI systems, rather than trying to extend general security practices to cover AI edge cases.

Our Methodology

AI security intelligence at infosec.qa follows a structured approach across four domains:

Threat Characterization - We map the specific threat actors, techniques, and attack patterns relevant to your AI deployment context: your industry, your regulatory environment, your AI architecture, and your adversary profile. This is not generic AI security guidance - it is a threat model built for your specific situation.

Attack Surface Assessment - We systematically assess your AI attack surface: every exposed LLM endpoint, every AI agent tool connection, every training data pipeline, every model update mechanism. We apply the OWASP LLM Top 10 framework and extend it with emerging attack classes not yet captured in published frameworks.

Risk Quantification - We translate technical AI vulnerabilities into business risk terms: the potential impact of a successful model extraction, the regulatory consequence of a HIPAA-relevant training data breach, the enterprise sales impact of a publicly disclosed AI security incident. Risk quantification drives remediation prioritization.

Intelligence Operations - Ongoing AI threat intelligence means tracking the adversarial research community, identifying emerging attack techniques before they appear in production attacks, and alerting clients to threats relevant to their AI architecture before they become incidents.

The NomadX Ecosystem

infosec.qa is part of the NomadX consulting family - a group of specialized practices sharing a common commitment to building AI infrastructure that is secure, reliable, and accountable:

• infosec.qa - AI Security Intelligence (this practice)
• secops.qa - AI Security Operations - continuous monitoring and response for production AI
• pentest.qa - AI Security Testing - penetration testing and shift-left security QA
• nomadx.ae - AI Agents Consulting - building and deploying AI agent systems
• devsecops.ae - DevSecOps Consulting - security in the development lifecycle
• kubernetes.ae - Kubernetes & AI/ML Infrastructure - the compute layer for AI systems

The family integration is our structural advantage. infosec.qa identifies threats and quantifies risk. secops.qa monitors for those threats in production. pentest.qa tests for exploitability. devsecops.ae and kubernetes.ae build and maintain the infrastructure securely. No standalone AI security intelligence firm can offer this end-to-end intelligence-to-operations coverage.

Why AI Security Intelligence Requires Specialization

Traditional information security is well served by general security practices extended to cover AI systems. AI security intelligence requires something different - and infosec.qa was built to provide it.

The reasons are structural:

AI attack surfaces are different. Prompt injection has no analogue in traditional software security. Model extraction attacks exploit statistical properties of machine learning systems, not implementation bugs. Data poisoning operates against the training process rather than the deployed system. These attack classes require specialized research and tooling to assess and address.

AI threat actors are different. The adversarial ML research community is extraordinarily active - publishing new attack techniques, sharing proof-of-concept implementations, and demonstrating practical attacks against production AI systems at a pace that outstrips the security community’s ability to respond with general security guidance.

AI regulatory requirements are different. The EU AI Act, FDA AI/ML SaMD guidance, NIST AI RMF, OCC model risk management requirements, and emerging AI provisions in sector-specific regulations all impose AI-specific obligations. Mapping AI security controls to these frameworks requires specialized regulatory intelligence.

infosec.qa was built to meet these requirements - with AI-specialized researchers, AI-specific assessment methodology, and regulatory intelligence that tracks the AI governance landscape as it evolves.