Information Governance, Security & Cryptography for Medical Systems

Protecting clinical data, and proving you did

Clinical systems carry some of the most sensitive data there is, under some of the strictest expectations. Doing this well is more than a policy binder: it is the right governance, the right architecture, and cryptography that lets you prove integrity and protect identities by design. We help health organisations and medtech teams build systems that are trustworthy, and demonstrably so, combining information-governance experience with hands-on applied cryptography.

Information governance and security

• Information governance for clinical data: aligning to NHS expectations (Information Governance, the Data Security and Protection Toolkit) and UK GDPR, with data flows, lawful bases, and records that survive scrutiny.
• ISO 27001 information security management: a working ISMS for clinical systems, sized to the organisation and audited against the standard.
• Risk and clinical safety: security risk assessment that connects to the clinical safety case rather than sitting beside it.

Cryptography that protects identity and integrity

• Zero-knowledge proofs: proving eligibility, consent, or vaccination status without disclosing the underlying identity or data, so systems can verify what they need and learn nothing more.
• Immutable audit ledgers: tamper-evident, append-only logs that make clinical access and decisions provable after the fact.
• Secure enclaves and trusted execution: running sensitive processing and ledger integrity inside trusted execution environments (e.g. AWS Nitro Enclaves), with remote attestation so you can prove what code handled the data.
• Encryption and key management: protecting data in transit and at rest with key handling that holds up to audit, with an eye on post-quantum readiness.

Clinical governance when AI is in the loop

Putting AI into clinical workflows changes who, or what, is making, shaping, or ranking decisions, and clinical governance has to keep up:

• Accountability and the clinical safety case: making explicit where a model assists, where it decides, and where a clinician remains responsible.
• Assurance and explainability: the evidence, monitoring, and human oversight that let a clinical safety officer sign off AI-assisted decisions.
• Auditability: capturing model version, inputs, and rationale in the immutable record, so an AI-influenced decision can be reconstructed and reviewed.
• Privacy-preserving AI: keeping identity and raw data protected even as models are trained and run, using the cryptographic tools above.

How it works

We start from your data, your obligations, and your real threats, then design the governance and the cryptography together so the controls reinforce rather than fight each other. The result is a system you can defend to a regulator, an auditor, and a patient, including when AI is part of the decision.

Talk to us about securing your medical systems.