AI & LLM SECURITY
AI & LLM Security for
Regulated Medical Devices
Bridge the gap between probabilistic AI innovation and deterministic MDR & EU AI Act compliance. We navigate the emerging landscape of AI security, tracking evolving guidance from OWASP and SAIF, to deliver the precise technical evidence needed for IEC 81001-5-1 and ISO 14971 certification.
The Core Conflict: Probabilistic AI vs. Deterministic Regulations
Integrating generative AI into a medical device introduces non-deterministic attack surfaces that standard cybersecurity misses. A generic penetration test won't catch subtle prompt injections leading to clinical misdiagnosis, nor will it satisfy a Notified Body asking how you verify probabilistic outputs under IEC 81001-5-1.
Our Approach:
We validate your AI's defenses against prompt injection attacks and inference-time exploits, ensuring your innovation survives the regulatory audit.
Navigating an Emerging Regulatory Field
There is no single harmonized standard for medical AI security yet. We synthesize guidance from the bleeding edge to build robust, defensible compliance strategies.
OWASP LLM Top 10
Definitive list of critical GenAI vulnerabilities.
Google SAIF
Secure AI Framework for conceptual defense.
OWASP AI Exchange
Emerging technical testing guide and taxonomy.
EU AI Act & MDCG
Regulatory guidance for High-Risk medical AI.
Our Medical AI Security Methodology
A rigorous fusion of adversarial tradecraft and regulatory governance.
LLM Red Teaming & Stress Testing
We adopt adversarial mindsets to break your model's alignment and safety guardrails. We test not just for technical flaws, but for dangerous behavioral outcomes in a clinical context.
- •Prompt Injection & Jailbreaking: Testing robustness against adversarial inputs (OWASP LLM01)
- •Data Leakage Probing: Attempts to extract training data or PII via inference attacks
- •Safety Guardrail Evasion: Bypassing content filters to trigger harmful or unauthorized medical advice
AI Application Security (SAIF Aligned)
An AI model is only as secure as the application wrapping it. We assess the entire infrastructure surrounding your model, aligning with Google's Secure AI Framework (SAIF) principles.
- •Insecure Output Handling: Ensuring LLM output is sanitized before affecting downstream medical systems (OWASP LLM02)
- •Supply Chain & Model Theft: Securing weights, embeddings, and third-party dependencies
- •Inference-Time Monitoring: Evaluating mechanisms for real-time detection of adversarial usage
MDR & EU AI Act Alignment
We translate technical AI risks into the language of auditors. We help map probabilistic findings to deterministic risk management requirements.
- •EU AI Act Readiness: Security validation for high-risk AI medical systems
- •ISO 14971 Integration: Security evidence for AI-specific threats
- •IEC 81001-5-1 Evidence: Security testing evidence for secure lifecycle management of AI components
Medical AI/LLM Security Readiness Checklist
Key validation requirements for integrating probabilistic models into regulated medical devices (MDR Class IIa/IIb & EU AI Act).
Governance & Risk (ISO 14971)
- AI failure modes (hallucinations, bias) mapped in Risk Management File.
- Defined clinical workflow for Human-in-the-Loop oversight.
- Data governance policy for provenance, consent, and bias minimization.
- Strict model version control triggering re-validation protocols.
Technical Defense (OWASP/SAIF)
- Input validation layers to detect prompt injection attacks (OWASP LLM01).
- Strict sanitization of LLM output before passing to clinical systems.
- Measures to prevent data poisoning during training/fine-tuning (SAIF).
- Supply chain vetting for third-party models and libraries.
Regulatory Alignment
- Security evidence mapped to MDR Annex I (GSPR) requirements.
- Adherence to IEC 81001-5-1 secure development lifecycle.
- Proactive Post-Market Surveillance (PMS) for model drift.
- Audit-ready test results for EU AI Act "High-Risk" systems.
Resources for Building Secure Medical AI
Practical guides for development teams navigating the new regulatory landscape.
OWASP Top 10 for LLM Applications (2025)
The definitive standard for identifying critical vulnerabilities in Generative AI applications. We test directly against this updated list.
Read the StandardGoogle's Secure AI Framework (SAIF)
A conceptual framework for securing AI systems. We use SAIF principles to ensure your defenses scale and adapt to new threats.
Explore SAIFIs Your Medical AI Ready for the Notified Body?
Don't let AI security risks derail your MDR or AI Act certification. Get the technical evidence you need to prove your system is secure by design.
Schedule an AI Risk Assessment