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System of Evidence

Prove what AI did, who decided, and what evidence existed at the time.

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

Request an Evidence Readiness Briefing 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

Governance teams can describe policy but cannot always prove what happened in production.

02

Evidence is assembled after incidents, audits or regulatory requests rather than preserved continuously.

03

Sources, calculations, tool calls and human reviews are separated across systems.

04

Organisations struggle to demonstrate chain of custody and whether evidence changed after the event.

What Cybatar provides

Institutional capability, not another isolated dashboard.

01

Evidence Packets

Bind material interaction context, sources, claims, calculations, tools and reviews into inspectable records.

02

Cryptographic Integrity

Use canonical manifests, hashes, signatures and immutable ledger controls to make tampering detectable.

03

Evidence Assertions

Attach later verification or assurance findings without rewriting historical evidence.

04

Audit Room

Provide authorised reviewers with scoped access to defensible evidence and chain-of-custody records.

Institutional outcomes

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

01Audit-ready evidence
02Faster investigations
03Stronger provenance
04Reduced evidence reconstruction burden
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.

Runtime interaction capture Policy and moderation decisions Human review Regulatory evidence requirements
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.

 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

Request an Evidence Readiness Briefing

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

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