AI Evidence Plane
Discuss one workflow
For risk, security and compliance

Connected evidence for the teams accountable when AI misbehaves.

Fragmented evidence, dynamic tool access and unclear accountability make agentic AI hard to assure. AI Evidence Plane keeps evidence local, boundaries explicit and accountability named.

  1. profile.resolved · customer-onboarding/verify-identity
  2. policy.evaluated · pii-boundary v5 → allow
  3. model.invoked · primary-gpt v3 prompt: sha256 ref
  4. tool.called · document-store.read (scoped)
  5. judge.primary · grounding-judge v4 → pass
  6. evidence.recorded · local store · ref ev-58c2…

For any execution you can see which profile, policies and judges actually applied — step by step.

A workflow-level record of the governance decisions applied during one execution, kept in the local execution evidence store.

  • Metadata only: keys, hashes, IDs and timings — no raw prompts or payloads.
  • Each entry links into the Connected Evidence Graph.

The assurance problem

Evidence you cannot connect is evidence you cannot use

Approvals, execution logs, evaluations and remediation live in different systems. When a reviewer asks a joined-up question, someone rebuilds the answer by hand.

The Connected Evidence Graph approach links governance intent, controls, evaluations, approvals, operational decisions, supporting evidence, remediation and business outcomes into a traceable evidence chain — so assurance questions become traversals, not projects.

What you get

Boundaries you can review, evidence you can follow

Local evidence custody

Execution evidence stays in your local evidence store inside your boundary — metadata only, never raw prompts or customer payloads.

Data and tool boundaries

Which tools, tool servers and memory sources an agent may touch is approved configuration, enforced locally.

Human accountability

Approvals, human decision boundaries and remediation actions name accountable roles.

Policy enforcement

Approved policy sets are evaluated on the execution path and leave evidence.

Audit support

Approved-versus-executed comparisons and governance traces support audit and regulatory review.

Governed remediation

Findings resolve through controlled, evidenced actions — not untracked hotfixes.

Optional SecureAI

Deeper inspection, still inside your boundary

Optional SecureAI inspects AI traffic locally — sensitive-data, policy and anomalous-behaviour findings flow into the local evidence store. It never sends traffic outside your boundary, and it remains optional.

Without SecureAI, local runtime assurance still governs the application and records local execution evidence.

Review the Data Boundary

Honest limits

What this does not claim

The Connected Evidence Graph approach can strengthen traceability, accountability and operational assurance. It does not itself guarantee that an AI system is safe, compliant or trustworthy.

Get started

Review the boundary with your security team

We walk the data boundary, evidence custody model and enforcement path against your architecture.