AI Security for Healthcare

AI security for healthcare means securing and governing the AI and LLM features healthcare and health-tech organisations ship — clinical documentation, triage and diagnostic support, and patient-facing assistants — against the OWASP Top 10 for LLM Applications and the AI governance standards (EU AI Act, NIST AI RMF, ISO/IEC 42001). CyberSigma threat-models your AI stack, tests guardrails and data isolation, reviews governance, and hands you a prioritised, board-ready report. We are CERT-In empanelled and PCI QSA authorised (CEMEA, Asia Pacific and the USA).

The AI security risks that matter most in healthcare

In healthcare, AI touches the most sensitive data there is and can influence clinical decisions, so safety, hallucination and privacy sit alongside the usual model-security risks. An assessment is only useful if it targets the risks your AI use cases actually create:

• Patient-data exposure through prompts, logs, fine-tuning sets or model responses.
• Hallucination and unsafe output in clinical or triage contexts without human oversight.
• Prompt injection via patient-supplied or document content in RAG pipelines.
• Bias in diagnostic or triage models across patient populations.
• Regulatory exposure where AI features approach medical-device or high-risk classification.

Which AI standards should healthcare and health-tech organisations map to?

Beyond the sector-specific risks above, these are the cross-cutting AI standards we assess against:

• EU AI Act — the risk-tiered AI regulation that applies extraterritorially to anyone placing AI systems on the EU market or whose AI output is used in the EU; high-risk and general-purpose AI carry specific obligations.
• NIST AI Risk Management Framework (AI RMF 1.0) — the leading voluntary framework for governing, mapping, measuring and managing AI risk.
• ISO/IEC 42001:2023 — the certifiable AI Management System standard, with ISO/IEC 23894 for AI risk management.
• OWASP Top 10 for LLM Applications — the de-facto checklist for securing LLM and generative-AI features.
• Health-data privacy law — AI in healthcare processes some of the most sensitive personal data, so the applicable health-privacy regime (and HIPAA where US data is involved) is central.

What a CyberSigma AI security review covers for healthcare

We assess how your AI systems behave under attack and how they handle data, not just your model card. In a typical engagement we:

• Threat-model the AI/LLM stack against the OWASP Top 10 for LLM Applications — prompt injection, insecure output handling, sensitive-information disclosure, excessive agency and the rest.
• Test guardrails for real: jailbreak and prompt-injection resistance, input/output filtering, and what the model does with untrusted content from tools, RAG sources and users.
• Review data governance for AI: what personal or sensitive data feeds training, fine-tuning and prompts, the lawful basis for it, and whether customer data leaks across tenants or into a foundation model.
• Secure the model pipeline: access to model registries, secrets and MLOps tooling, supply-chain risk in third-party and foundation models, and protection against model and data exfiltration.
• Assess governance and oversight against ISO/IEC 42001 and the NIST AI RMF: accountability, human oversight, documentation, and evaluation of high-risk use cases.
• Check monitoring and incident response for AI-specific failure modes — abuse, drift, harmful output — not just classic infrastructure alerts.

Representative engagement: a health-tech triage assistant

A useful way to picture the work: a health-tech company’s triage assistant summarised patient messages for clinicians. We red-teamed it against the OWASP LLM Top 10, tested for unsafe output and data leakage, reviewed human-oversight design, and delivered a risk-ordered remediation plan. This example is representative of how we structure these reviews; named client references are available under NDA on request.

How long does an AI security review take, and what does it cost?

Most AI security reviews run a few weeks, depending on how many models, applications and data flows are in scope. Cost follows that scope rather than a fixed list price, so we run a short, free discovery call, agree the scope in writing, and give you a fixed quote before any work starts. If you are working to a launch or customer deadline, tell us the date and we will tell you honestly whether it is achievable.

How do you protect patient data in AI systems?

We trace where patient data enters training, prompts and logs, test for leakage in responses, and assess the controls against the applicable health-privacy regime (and HIPAA where US data is involved).

How do you handle hallucination and safety in clinical contexts?

We test for unsafe and fabricated output, assess where human oversight sits, and check that high-risk uses have the documentation and guardrails the NIST AI RMF and EU AI Act expect.

Could our AI feature be treated as a medical device or high-risk AI?

Some clinical-support use cases can approach medical-device or EU AI Act high-risk classification. We help you understand where your use cases land and the obligations that follow.

How often should we review our AI systems?

At least annually, and again whenever a clinical-facing model changes or a new patient-facing assistant is deployed.