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Continuous Assurance

Know whether AI remains within the conditions under which it was approved.

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

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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

Initial assessments go stale while models, prompts, suppliers and workflows change.

02

Human review queues are fragmented across business and technical teams.

03

Moderation and safety rules are difficult to govern as institutional policy.

04

Incidents are investigated without a complete history of controls and prior decisions.

What Cybatar provides

Institutional capability, not another isolated dashboard.

01

Model Governance Matrix

Track model status, review requirements, ownership and approved institutional use.

02

Human Review Queue

Route material findings and decisions to authorised reviewers with evidence context.

03

Moderation & Policy Rules

Manage rules, actions and review outcomes as governed institutional controls.

04

Incident & Assurance Records

Connect findings, incidents, evidence, remediation and assurance activity over time.

Institutional outcomes

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

01Continuous visibility
02Faster review resolution
03Clear model accountability
04Evidence-backed assurance
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 Control AI Evidence Enterprise Security Regulatory Control Graph
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 ControlAI Control

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

 System of EvidenceAI Evidence

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

 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 Continuous AI Assurance

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

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