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.
Regurai in five questions.
Written for executives, risk, architecture and audit leaders who need the position quickly.
- What is Regurai?
- Regurai is the governance and operational intelligence layer connecting AI, data, architecture, risk, decisions and evidence in one enterprise model.
- Why does an organisation need it?
- Because the information needed to govern AI already exists — in model registers, risk registers, data catalogues, control libraries and architecture repositories — and none of them hold the relationships between the others.
- How does the Regurai model work?
- Thirteen connected perspectives, from strategy through AI, data and controls to outcomes, are held as one structure. A change in one is visible in the others, so consequence is traceable rather than assumed.
- What capabilities does it provide?
- AI governance, enterprise architecture, data governance, risk and control, decision intelligence and evidence — delivered as connected modules over the same model rather than separate products.
- What governance outcomes does it produce?
- An accountable register of AI in use, mapped controls with visible gaps, decisions recorded with assumptions and approvals, and evidence that can be reconstructed as at a point in time.
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 the organisation is left holding.
Governance is judged on what can be shown afterwards. These are the outputs Regurai is built to produce.
- Accountable AI register
- Every AI model, agent and AI-enabled application in use has a named owner, a purpose and a lifecycle state.
- Connected risk position
- Risk is scored from the data, processes and controls a system actually depends on, not a standalone questionnaire.
- Mapped control coverage
- Requirements are mapped to controls and evidence, so weak or unevidenced coverage is visible rather than assumed.
- Recorded decisions
- Approvals carry conditions, assumptions and named accountability, with the economic consequence attached.
- Retrievable evidence
- A governance position can be reconstructed as at a point in time, without reassembling it by hand.
- Board-ready reporting
- Executive, risk, audit and regulator-facing reporting is produced from the same model the business runs on.
Where AI governance connects.
AI cannot be governed apart from the architecture, data, risk and evidence around it.
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.