Assessment governance
Validation theory → Confidence
Validation Theory
Confidence
Evaluation Runs
Assurance
Assessment Governance
Trust & Governance
Regulatory alignment.
Interface is designed to support compliance, governance and assurance activities across a range of regulatory and professional frameworks. Compliance remains a shared responsibility between Interface and its customers; the platform provides governance capabilities and audit artefacts that help organisations demonstrate transparency, accountability and control.
Compliance as architecture.
Rather than treating compliance as a separate activity, Interface is designed so that governance records are generated as part of normal platform operation. The aim is to connect design principles, platform capabilities and regulatory expectations in a single traceable structure.
FoundationResearch
PrincipleDesign
ProductCapability
EvidenceGovernance Record
OutcomeAssurance
AlignmentRegulation
Data protection.
Relevant frameworks include UK GDPR, the Data Protection Act 2018 and ICO data protection guidance. Interface capabilities should support data control, transparency, lifecycle management and security of processing.
| Requirement | Why it matters | Design principle | Platform capability | Governance outcome |
|---|---|---|---|---|
| Right of access | Individuals may need to understand what information is held about them. | Ownership | Export workflows and structured artefact records. | Transparency and user control. |
| Right to erasure | Individuals may request deletion of personal information. | Ownership | Retention controls and deletion workflows. | Data lifecycle management. |
| Data portability | Information should be movable between systems where applicable. | Portability | Structured exports for source artefacts, outputs, metadata and records. | Reduced lock-in and improved control. |
| Data minimisation | Only necessary information should be processed for a defined purpose. | Cognitive Ergonomics | Purpose-bound workflows and controlled data collection patterns. | Reduced risk exposure. |
| Security of processing | Personal information must be protected against unauthorised access or misuse. | Sovereignty | Access controls, encryption, workspace isolation and audit logging. | Information security and accountability. |
AI governance.
Relevant frameworks include ICO AI guidance, the EU AI Act and emerging AI assurance practices. Interface is designed to make AI-assisted workflows traceable, reviewable and evaluable.
| Requirement | Why it matters | Design principle | Platform capability | Governance outcome |
|---|---|---|---|---|
| Transparency | Organisations should understand how AI-derived outputs were produced. | Transparency | Inference provenance across source, model, prompt, framework and output. | Auditability. |
| Explainability | Users and reviewers should be able to investigate the basis of AI outputs. | Transparency | Model, prompt and framework version tracking. | Explainability and investigability. |
| Record keeping | AI-assisted processes require evidence and traceability over time. | Confidence | Evaluation records, audit trails and run histories. | Accountability. |
| Human oversight | Humans remain responsible for consequential interpretation and use. | Augmentation | Review workflows, approval states and escalation paths. | Human accountability. |
| Continuous monitoring | AI systems and workflows should be evaluated over time, not only at launch. | Confidence | Evaluation runs, benchmark comparisons and performance tracking. | Ongoing assurance. |
Assessment & decision support.
Relevant frameworks include the Equality Act 2010, educational assessment guidance and professional psychometric standards. This area becomes especially important where Interface supports assessment, selection, promotion, learning or other consequential evaluation workflows.
| Requirement | Why it matters | Design principle | Platform capability | Governance outcome |
|---|---|---|---|---|
| Validity | Outputs should support the interpretations and uses being made from them. | Confidence | Validation workflows and evaluation records. | Evidence-based use. |
| Reliability | Outputs should be consistent, reproducible and appropriately stable. | Confidence | Benchmarking, repeated evaluation runs and model comparisons. | Measurement quality. |
| Fairness | Systems should minimise unintended bias and support responsible deployment. | Confidence | Monitoring workflows, subgroup analyses and adverse impact checks. | Fairness evidence and risk mitigation. |
| Transparency | Assessment processes and outputs should be interpretable and defensible. | Transparency | Framework versioning, inference provenance and audit trails. | Explainability. |
| Human review | Expert judgement remains important for consequential assessment use. | Augmentation | Reviewer workflows, comments, approvals and escalation paths. | Appropriate oversight. |
Security & information governance.
Relevant frameworks include ISO 27001, SOC 2 and enterprise procurement expectations. Interface should support security review through clear access control, logging, segregation and resilience practices.
| Requirement | Why it matters | Design principle | Platform capability | Governance outcome |
|---|---|---|---|---|
| Access control | Only authorised users should access information and perform sensitive actions. | Sovereignty | Role-based permissions, workspace membership and RLS policies. | Least-privilege access. |
| Audit logging | Security-relevant actions should be traceable and investigable. | Transparency | Audit records and event logs. | Investigability. |
| Data segregation | Customer information should remain isolated in multi-tenant environments. | Ownership | Workspace isolation and permission-scoped records. | Multi-tenant security. |
| Backup & recovery | Information and service continuity should be recoverable after failure. | Sovereignty | Backup and recovery procedures. | Operational resilience. |
| Incident management | Security incidents should be handled consistently and transparently. | Confidence | Incident response workflows and internal governance records. | Risk management. |
From principles to compliance.
The regulatory alignment page acts as a crosswalk between research foundations, design principles, platform capabilities and compliance expectations.
AI governance
Extended cognition → Oversight
Extended Cognition
Augmentation
Review Workflows
Human Oversight
AI Governance
Data protection
Data sovereignty → Ownership
Data Sovereignty
Ownership
Workspace Isolation
Data Control
GDPR Alignment
Positioning note
This page is not legal advice and does not certify compliance. It describes how Interface is being designed to support compliance activities, governance reviews and assurance workflows. Detailed implementation evidence, policies and technical controls should live in authenticated documentation.