Govern every AI system, and everything it touches.
Regurai gives complex and regulated organisations a governed register of AI models, agents and AI-enabled applications — connected to the data they consume, the processes they affect, the risks they create, the controls that constrain them and the obligations they must satisfy.
AI is governed in fragments.
AI is being adopted faster than governance can follow. Model registers, risk registers, data catalogues, control libraries and architecture repositories each hold part of the answer, and none of them hold the relationships between them.
One accountable view of AI in the enterprise: what exists, who owns it, what it depends on, what it affects, and what evidence supports the decision to run it.
Six stages, from registration to evidence.
- 01
Register
What AI exists here?
Record models, agents and AI-enabled applications with owners, purpose and intended use.
- 02
Connect
What does it depend on and affect?
Link each AI system to its data, applications, processes, controls and obligations.
- 03
Assess
What risk does it carry?
Score risk from real dependencies and control coverage rather than a standalone questionnaire.
- 04
Approve
Should it run, and under what conditions?
Decide with separation of duties, recorded conditions and named accountability.
- 05
Monitor
Has anything changed?
Detect changes in data, dependencies or control state that invalidate an earlier decision.
- 06
Prove
Can we evidence it?
Assemble the audit trail behind any AI decision without re-collecting evidence by hand.
Eight capabilities over one connected model.
Each capability reads from the same enterprise model, so risk, control coverage, data lineage and approval history describe the same AI system.
A governed register of models with ownership and lifecycle state.
Every model is recorded with its purpose, owner, intended use, limitations, validation state and approval history, so a model can be traced from proposal through to retirement.
- Model inventory
- Intended use and limitations
- Validation records
- Lifecycle and retirement
Constrain what autonomous agents may do, and against which systems.
Agents are registered alongside the tools and data they may invoke. Permitted actions, approval requirements and escalation paths are declared explicitly rather than assumed.
- Agent registry
- Permitted tools and actions
- Human approval points
- Escalation and stop conditions
Risk assessed against dependencies, not in a standalone register.
Risk scores are derived from the model's exposure: the sensitivity of its data, the criticality of the processes it supports and the strength of the controls around it. When a dependency changes, the score is reconsidered.
- Impact and likelihood assessment
- Data sensitivity exposure
- Process criticality
- Continuous re-scoring
Controls connected to the AI systems they actually constrain.
Controls are mapped to the models, data flows and processes they cover, making coverage gaps visible rather than inferred from spreadsheets.
- Control library
- Coverage mapping
- Ownership and testing state
- Gap and waiver visibility
Obligations mapped to policies, controls, systems and evidence.
Regulatory and standards requirements are mapped to the parts of the enterprise expected to satisfy them. Coverage is stated as coverage — never as a claim of compliance or certification.
- Obligation mapping
- Framework coverage
- Policy alignment
- Transparent methodology
The data behind each AI system, with lineage and sensitivity.
Training and inference data is traced to its source, with quality and sensitivity classification attached, so data issues surface as AI issues before they reach production.
- Lineage
- Quality signals
- Sensitivity classification
- Ownership and stewardship
Structured assessment, review and approval with separation of duties.
Proposals move through assessment, review and approval with defined roles. Approvers see the connected risk, control and data picture at the point of decision.
- Assessment workflow
- Separation of duties
- Approval records
- Change re-review triggers
Prove what happened, who approved it and on what basis.
Decisions, approvals and changes are recorded as immutable evidence, assembled on request instead of reconstructed manually from disconnected systems.
- Immutable audit trail
- Evidence assembly
- Traceability to decisions
- Export for review
What Regurai does — and does not — claim.
- What does Regurai govern?
- AI models, AI agents and AI-enabled applications, together with the data, processes, technology, risks, controls and regulatory obligations connected to them.
- Does Regurai replace our existing model risk or GRC tooling?
- No. Regurai connects the information held across governance, risk, data and architecture platforms so decisions can be made against one connected view. Existing systems can stay in place.
- Does Regurai use AI to make governance decisions?
- No. Governance decisions, approvals, risk acceptance and regulatory interpretation remain with accountable people. Assistive features help users navigate information; they do not decide.
- Does Regurai make our organisation compliant?
- No product can. Regurai is designed to support governance and is mapped to recognised frameworks and standards. Coverage is reported as coverage, with the methodology published, and never as certification or a compliance guarantee.