Trust & Compliance
AI you can govern, oversee and trust
The boundaries, oversight and monitoring that keep clinical AI safe after it goes live — not just at launch.
Governance building blocks
What we help you put in place
Intended-use boundaries
What the AI is — and is not — for.
Human oversight
Who reviews outputs, and the conditions for safe use.
Change control
How model updates are assessed and approved.
Monitoring
Ongoing performance, drift and incident tracking.
Escalation
What happens when the model is uncertain or wrong.
Documentation
An oversight policy your board and buyers can review.
Boundary. Our website and tools do not diagnose, triage or provide emergency advice. Governance guidance supports your accountable decisions; it does not replace regulatory or legal advice.
Why governance is a lifecycle, not a launch
Safety that holds after go-live
Most clinical AI failures are not present at launch — they emerge afterwards. A model that performed well on its validation data can degrade as patient populations shift, as upstream data sources change format, or as clinicians use it in ways its designers never anticipated. This is why we treat AI governance as a continuous discipline rather than a launch-day document. The goal is a system you can keep proving is safe, month after month, to your board, your clinical leadership and your buyers.
That starts with an unambiguous statement of intended use: the clinical question the AI helps answer, the population it applies to, and the decisions it must never make on its own. Around that boundary we build a layer of human oversight calibrated to the risk — light-touch confirmation for low-stakes information, structured clinician review for anything that could influence a diagnosis or treatment. The design deliberately resists automation bias, so confident-but-wrong outputs are questioned rather than waved through.
Accountability. Governance gives your accountable decision-makers something concrete to own: a written oversight policy, monitoring evidence, and an escalation route — not a vague assurance that "a human is in the loop".
Putting it in place
From principles to an operating policy
- Define intended use and limits. Write down what the model is for, the population it covers, and the decisions reserved for humans.
- Design the oversight model. Specify who reviews which outputs, on what evidence, and under what conditions reliance on the AI is acceptable.
- Set change control. Establish how model updates, retraining and configuration changes are assessed and approved before they reach clinical use.
- Instrument monitoring. Agree performance metrics, drift indicators and alert thresholds, and decide who acts when they trip.
- Document and escalate. Produce a board-readable oversight policy and a clear route for when the model is uncertain, wrong or causing harm.
Governance is the connective tissue across our trust framework. It sets the rules that AI validation and clinical safety tests against, depends on lawful data handling described in data protection and security, and feeds the evidence buyers expect under NHS buyer readiness. For the regulatory question of whether your AI is a medical device, our DPIA for AI in healthcare guide is a useful starting point.
Answers
Frequently asked questions
What does good AI governance look like?
Clear intended-use boundaries, defined human oversight (who reviews what, and when), change control for model updates, ongoing performance monitoring, and an escalation route when the model is uncertain or wrong.
Is our AI a medical device?
If software has a medical purpose it may be regulated as a medical device by the MHRA. We help you assess intended use and prepare, but the regulatory determination is yours to make with your advisers.
How does this relate to your validation service?
Governance sets the rules; validation tests the product against them. See AI Validation & Clinical Safety.
What does meaningful human oversight actually require?
It is more than a clinician technically being able to override the AI. Meaningful oversight means the reviewer has the information, time and competence to question an output, that the workflow does not pressure them into rubber-stamping it, and that the conditions for relying on the AI are written down. We help define who reviews what, when, and on what evidence — so oversight is real, not nominal.
How do you guard against automation bias and model drift?
Automation bias — over-trusting a confident machine — is designed against through interface cues, confidence signalling and audit of override rates. Drift, where performance degrades as real-world data shifts, is caught through ongoing performance monitoring against agreed metrics and alert thresholds, with re-validation triggered when they are breached.
How does governance connect to clinical safety and data protection?
They are layers of the same assurance. Governance sets intended-use boundaries and oversight rules; clinical safety (DCB0129/DCB0160) evidences that residual risk is acceptable; data protection ensures the underlying data is processed lawfully via a DPIA. We align all three so they reinforce rather than contradict each other.
Stand up AI governance
We'll help you put oversight and monitoring in place.