AI & LLM Security for Healthcare & Life Sciences

AI & LLM security for healthcare protects the AI systems behind your products and operations from prompt injection, data leakage, model poisoning and excessive agency. CyberSigma red-teams these healthcare AI flows and maps governance to HIPAA, India's DPDP Act, GDPR and medical-device safety expectations, plus OWASP LLM Top 10, NIST AI RMF, ISO/IEC 42001 and MITRE ATLAS. We are CERT-In empanelled and PCI QSA (CEMEA) authorised.

A real healthcare AI risk: the hallucinated dosage

A clinical-summary LLM confidently suggests a medication dose that is subtly wrong, or a patient chatbot is manipulated into revealing another patient's record. In healthcare these failures are safety and privacy incidents, not just bugs. We red-team for PHI leakage, prompt injection through patient inputs, hallucination in clinical content, and weak access control in retrieval over medical records.

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

We adversarially test your healthcare LLM and GenAI applications the way a real attacker 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 for Healthcare & Life Sciences

We map AI controls to your sector's obligations and the global AI frameworks:

• HIPAA safeguards and de-identification for AI over PHI.
• DPDP / GDPR for patient personal data in AI.
• FDA / medical-device expectations for AI as software (where applicable).
• ISO/IEC 42001 + NIST AI RMF.

How do you protect PHI in AI systems?

We test for PHI leakage in model outputs and retrieval, assess de-identification and access control, and verify safeguards aligned to HIPAA and applicable privacy law.

Can LLM hallucinations be a compliance risk in healthcare?

Yes — confident but wrong clinical output is a patient-safety and liability risk. We test for it and assess grounding, human-oversight and disclaimer controls.

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.