EHR Access Assurance. Continuous, AI-powered compliance monitoring for electronic patient record access — built for healthcare organisations that take patient privacy seriously.
Healthcare organisations around the world face a growing and increasingly demanding set of obligations around how staff access patient data. In the UK, the NHS Data Security and Protection Toolkit and the National Data Guardian framework set clear expectations for monitoring and investigation. In Quebec, Canada, Loi 25 mandates breach notification within 72 hours and requires organisations to demonstrate they have taken active steps to detect and prevent inappropriate access. Across jurisdictions, regulators are moving from guidance to enforcement.
The problem is not a lack of awareness. It is a lack of capacity. A large hospital network may generate hundreds of thousands of staff access events every day. Even a well-resourced Information Governance team can realistically review only a small fraction of these — and they are doing so retrospectively, looking for problems that may have occurred weeks earlier.
verif.ai was built to close that gap entirely.
Multi-layer AI detection — built for the complexity of clinical environments.
Most access monitoring tools rely on simple rules: flag any access outside working hours, or any access to a VIP patient record. These approaches generate noise, miss sophisticated patterns, and quickly lose the confidence of the teams meant to act on them.
verif.ai uses two AI models working in combination. The first builds a statistical baseline of normal behaviour for every staff role and department — identifying access patterns that deviate significantly from the norm. The second analyses the clinical logic of how records are accessed in sequence, detecting patterns that may appear statistically normal but are clinically inappropriate. Together, they surface the anomalies that matter — and filter out the ones that do not.
AI that understands clinical context — not just raw data.
A ward nurse accessing ten patient records in an hour is entirely normal — if those patients are on her ward. The same nurse accessing ten records from five different wards she has never worked on is a very different situation.
verif.ai understands this distinction. Our AI models clinical behaviour in context — learning the care relationships, departmental norms, and role-specific patterns that define legitimate access in your organisation. This means fewer false positives, faster investigations, and alerts that your IG team can actually trust.
Investigations that write themselves.
When verif.ai flags an anomaly, your IG team does not receive a raw data dump. They receive a structured, plain-English investigation summary — generated automatically by our AI — that explains what happened, why it was flagged, what the normal baseline looks like for that staff member's role, and what the likely next steps are.
What would previously take an analyst two hours to piece together manually is delivered in seconds. Your team can focus on decision-making, not data-gathering.
A complete compliance workflow — from alert to resolution.
verif.ai is not just a detection engine. It is a complete compliance workflow tool. Every flagged anomaly becomes a structured case record — with a full audit trail of every action taken, every decision made, and every communication sent. Cases can be escalated, assigned, and closed within the platform, and the complete record is available for regulatory review at any time.
Whether you are responding to a Subject Access Request, preparing for an inspection, or managing a live breach investigation, verif.ai gives you the evidence base and the documentation trail you need.

Full integration with EHR audit event streams, including Epic E1M audit log data.

Built on HL7 FHIR standards — the international standard for healthcare data interoperability.

Deployable on premise with local models, in Azure and in AWS, keeping your data within your environment and jurisdiction at all times, or as a SaaS.