Security & Responsible AI
Trust infrastructure must itself be trustworthy.
This page outlines the prototype's security posture and responsible-AI stance. Certification, regulatory compliance and independent validation are future milestones.
Note: BioTrust AI is in prototype development. Security, privacy and regulatory claims require independent assessment before production use.
Security architecture
TLS 1.2+ in transit; encrypted storage for prototype artefacts; segregated workspaces; audit events on analysis and policy operations. Independent assessment is a prerequisite before production use.
Privacy-by-design
Data minimisation is designed into the pipeline. Analyses are structured so that only what is required for a decision is retained; retention windows are planned as customer-configurable controls.
Responsible AI principles
Every decision is explainable, versioned and paired with a confidence signal. We do not intend BioTrust AI to make consequential decisions autonomously; human oversight is part of the operating model.
Human oversight
Review queues and configurable human-in-the-loop workflows are first-class. Reviewer actions emit audit events.
Fairness & performance evaluation
Fairness evaluation across demographic and capture conditions is a planned control, subject to independent validation.
Data lifecycle
Inputs, derived signals and audit events have documented lifecycles. Storage encryption, deletion and provenance are documented for customer review.
Deployment options
Cloud, private-cloud and future edge deployment options are planned. Requirements for regulated environments can be discussed as part of a pilot.
Controls in the prototype
Data minimisation
Designed to process only what is necessary for a trust decision.
Configurable retention
Retention windows planned as customer-configurable controls.
Encryption in transit & at rest
TLS 1.2+ in transit; encrypted storage for prototype artefacts.
Role-based access
Workspace and role scoping planned for the enterprise dashboard.
Audit logs
Analysis and policy changes emit structured audit events.
Human oversight
Review queues and configurable human-in-the-loop workflows.
Model & version traceability
Every response references the model version that produced it.
Fairness evaluation
Planned bias evaluation across demographic and capture conditions.
Uncertainty communication
Scores are paired with confidence bands, not binary outcomes.
Privacy-conscious deployment
Cloud, private-cloud and future edge deployment options.
Data provenance documentation
Documentation of training data provenance and evaluation.
Incident-response readiness
Prototype incident-response playbooks and disclosure contact.
Frequently asked questions
BioTrust AI is currently a prototype. Results shown here are illustrative, are not identity-verification decisions and should not be used to make consequential decisions.