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System of Control

Turn AI policy into institutional decisions and enforceable control.

Route material AI activity through intake, authorisation, policy, regulatory obligations, review and exception handling before risk becomes operational dependency.

Discuss AI Governance & Control See the full platform
Why institutions need it

The problem is already operating inside the organisation.

Cybatar is designed for material AI activity: systems, decisions, spend and dependencies that can create institutional consequence if they remain fragmented or ungoverned.

01

AI policy exists on paper but is not embedded in operating workflows.

02

Approval decisions are slow, inconsistent or difficult to reconstruct later.

03

Different teams interpret materiality, risk and regulatory obligations differently.

04

Exceptions become permanent because there is no institutional decision queue or expiry discipline.

What Cybatar provides

Institutional capability, not another isolated dashboard.

01

Universal AI Intake

Create one front door for material AI proposals, purchases, deployments and changes.

02

Authorisation Workflow

Route requirements to accountable authorities and preserve the decision record.

03

Enterprise Policy Engine

Translate policy into structured rules, conditions, review requirements and control decisions.

04

Regulatory Control Graph

Connect frameworks, obligations, applicability decisions, controls and evidence requirements.

Institutional outcomes

What changes when ai control becomes part of the operating model.

01Faster governed approvals
02Consistent policy execution
03Defensible decision history
04Visible exceptions and obligations
Connected by design

The control plane should sit across the AI estate—not require the estate to move into one vendor.

Cybatar is designed to connect institutional records, providers and workflows while preserving a common governance and evidence model.

AI Registry and Passport Risk and compliance workflows Human review queues Evidence and Audit Room
The wider platform

Every operating layer works against the same AI estate.

View platform architecture →
System of RecordAI Estate

Create a canonical, continuously maintained inventory of models, agents, applications, suppliers and material AI use across the institution.

 System of EvidenceAI Evidence

Preserve interaction, decision, source, calculation, tool, moderation and review evidence so material AI facts can be reconstructed and independently inspected.

 Continuous AssuranceAI Assurance

Combine model governance, review queues, moderation, incidents, controls and ongoing assurance so risk management does not stop at initial approval.

 AI EconomicsAI Economics

Connect provider usage, token spend, infrastructure, suppliers, programmes and validated business outcomes into one economic view of the AI estate.

 System of CommerceAI Commerce

Bring supplier assurance, procurement, contracts, compute, capacity and commercial terms into the same operating model as AI governance and economics.

 Executive Control TowerExecutive Control Tower

Bring portfolio, risk, investment, evidence, economics and decision queues together so executive teams can govern AI as an institutional estate rather than a collection of projects.
Next step

Discuss AI Governance & Control

Bring the current operating context. Cybatar will structure the estate, decision problem, evidence requirement and next practical step.

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