AI Security
Assess and secure machine-learning models, LLM applications and AI agents against adversarial and data-driven attacks.
Clinical AI Governance
Govern AI used in clinical and operational decisions with validation, bias testing, human oversight and audit trails.
The problem
AI is entering triage, imaging, documentation and revenue-cycle workflows. Where it influences care or coverage, organisations must show it was validated on relevant populations, monitored for drift and bias, kept under clinician oversight and documented well enough to explain any individual decision.
Our approach
Catalogue in-house and vendor AI and classify it by risk.
Define approval, oversight and acceptable-use policies.
Test performance, bias and safety on local data before go-live.
Track drift, overrides and incidents throughout operation.
Capabilities
Register of AI systems with intended use and risk level.
Performance and subgroup testing on representative data.
Analysis of disparate performance across patient groups.
Clinician review points, override capture and escalation.
Guardrails against patient-data leakage and unsafe output.
Model cards, decision logs and audit trails.
Engagement
Standards & technology
FAQ
Our focus is governance, validation and secure integration. Clinical judgement and any regulated medical-device approvals remain with the clinical organisation and manufacturer.
Let’s discuss it. Tell us what you are working on and an engineer — not a sales script — will respond.