Security Testing for UAE Healthcare AI Applications

AI diagnostic tools, telemedicine platforms, and health data systems require specialized security testing under DHA, HAAD, and ADHICS requirements.

What We See in This Space

AI diagnostic tools making clinical recommendations have never been adversarially tested for manipulation
Telemedicine API endpoints expose patient health data and consultation records
ADHICS compliance requires documented security assessment of all systems handling health data
Third-party health AI integrations (diagnostic imaging, clinical notes, medication management) not independently vetted
Patient data exposed via LLM context windows in clinical AI tools

Healthcare AI applications handle the most sensitive data category — protected health information — while making recommendations that directly affect patient outcomes. The security requirements for these systems are correspondingly high.

UAE Healthcare Regulatory Context

DHA (Dubai Health Authority) and HAAD (Health Authority Abu Dhabi) require healthcare providers and digital health platforms operating in UAE to maintain documented information security programs. ADHICS — the Abu Dhabi Healthcare Information and Cyber Security Standard — specifies security controls for health information systems.

PDPL (UAE Personal Data Protection Law) classifies health data as sensitive personal data requiring elevated protection. AI systems that process, analyze, or make decisions based on health data must meet PDPL requirements for data minimization, purpose limitation, and security.

AI-Specific Risks in Healthcare

Clinical AI tools — diagnostic imaging AI, clinical decision support systems, medication management AI — represent a new attack surface that traditional healthcare security programs don’t address:

Prompt injection in clinical AI — an adversary who can manipulate the instructions given to a clinical AI assistant can potentially cause it to recommend incorrect medications, misinterpret test results, or provide false clinical guidance. The consequence is not a data breach — it’s a patient safety issue.

LLM context window exposure — many clinical AI tools include patient records in the LLM context window for personalization. If not properly isolated, an adversarial user can craft inputs designed to extract other patients’ data from the model’s context.

AI diagnostic tool manipulation — AI systems trained to identify pathology in imaging can be fooled by adversarial inputs. While physical adversarial attacks on medical imaging require physical access, AI diagnostic tools exposed via API can be tested for input manipulation vulnerabilities.

ADHICS-Aligned Assessment

Our AI Security Assessment for healthtech clients is structured to map findings against ADHICS control requirements and PDPL obligations — giving your compliance team the documented evidence they need for DHA and HAAD regulatory submissions.

Frameworks We Cover

DHA Cyber Security FrameworkHAAD Information Security StandardsADHICS (Abu Dhabi Healthcare Information and Cyber Security Standard)PDPL (UAE Personal Data Protection Law)HL7 FHIR API Security Guidelines

How We Help

AI Security Assessment

LLM Penetration Testing

API Security Testing

Web Application Pentest

Guardian Security Retainer

Find It Before They Do

Book a free 30-minute security discovery call with our AI Security experts in Dubai, UAE. We identify your highest-risk AI attack vectors — actionable findings in days.

Talk to an Expert