Verifiable AI Governance
01 / 09

Verifiable AI Governance.
A common layer of evidence for AI.

V-PROOF connects governance, agents, code, models, and human oversight with cryptographic evidence that a third party can verify. It does not replace your stack: it transforms what happens within it into something that can be proven later.

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Verifiable AI Governance

Govern AI. Prove the decision.

In just over a minute, see how systems and owners are registered, available controls are evaluated, and evidence that can be sealed is linked.

The Thesis
02 / 09

Governance says
what should have happened.
V-PROOF helps show what actually happened.

Governance does not end when a policy is written. It begins when a decision, an autonomous action, a code change, or a model version can be linked to its context, its controls, those responsible, and its supporting evidence.

01

Governance

Risks, policies, controls, responsible parties, and approvals.

→
02

Execution

People's work, agents, code, models and connected systems.

→
03

Evidence

Context, identity, action, supervision, and outcome.

→
04

Verification

Integrity and traceability that can be verified by a third party.

The platform
03 / 09

Different processes.
The same body of evidence.

Each module works on a different object, but they all converge on the same layer of context, responsibility, evidence, and verification.

AI Governance

Systems, Risks, and Decisions.

Inventory of systems, agents, models, and services. Risks, controls, assessments, approvals, evidence, and governance history.

See AI Governance → AI Agent Governance

Govern the agent. Prove the action.

Link identity or authority, policy, action, resource, outcome, and human oversight to verifiable evidence of implementation.

See Verifiable AI Agent Governance → Code Governance

The code is also accountable.

Analyze connected repositories, document changes, dependencies, and declared use of AI, and record the results in the same governance registry.

See Code Governance →
Model Governance · MLOps

Models with context, controls, and approval.

Connect MLflow in read-only mode, review versions and results, run defined checks, and record evidence and approvals without modifying the source system.

MLflow · controls · approval · evidence
ISO Management

Controlled documentation. Identified approvals.

Link the reviews, responsible parties, and current status of your management system (ISO 9001, 14001, 45001, 27001, or other) to the preparation of your audits.

View ISO Management → Evidence · V-Seal® · Verify

What matters can be verified.

Associate a cryptographic fingerprint, context, and provenance with documents and manifests, and enable subsequent verification of their integrity and record.

See how it's verified →
AI Agent Governance
04 / 09

Every agent has boundaries.
Every governed action can leave evidence.

Agents are changing the nature of governance: They no longer just generate content. They can use tools, call APIs, operate systems, access information, and trigger processes.

Identity

Which agent took action?

Identity, source system, or delegated authority associated with the execution.

Authority

What was it allowed to do?

Permissions, policies and limits in force at the time of the action.

Action

What did it do?

Tool, API, data, resource, or process on which it operated.

Supervision

Who intervened?

Approval, review, or human intervention when the process requires it.

Result

What did it cause?

Outcome and context related to the same evidence of implementation.

Verification

Can it be proven later?

Verifiable cryptographic evidence for auditing, governance, and third-party review.

See Verifiable AI Agent Governance →
How it fits
05 / 09

It doesn't replace your stack.
It connects it with evidence.

Security, identity, GRC, MLOps, and runtime tools continue to do their job. V-PROOF adds a common layer for reconstructing and subsequently demonstrating the governance context of an action.

Layer 01

Identity & Access

Who can take action, and under what authority?

↓
Layer 02

Policy / Runtime / Security

What is authorized, restricted, blocked, or monitored.

↓
Layer 03

Tools · APIs · Models · Data

Where the actual execution takes place.

↓
V-PROOF

Verifiable Evidence Layer

Action + context + responsible party + policy + result + oversight.

↓
Result

Audit · Governance · Assurance

Evidence prepared for internal or third-party review.

In the real world
06 / 09

Documents.
Code.
Models.
Agents.

Documents

Word · Portal · Desktop

Drafting, revisions, approvals, and supporting documentation related to the asset.

Code

GitHub · GitLab

Technical history, analysis, checks, and evidence associated with commits and versions.

Models

MLflow

Versions, controls, results, approval, and evidence of the model.

Agents

Actions · Tools · APIs

Implementation context, authority, policy, oversight, and results.

Audits
07 / 09

Three audits.
A single governance framework.

Code, models, and agents are audited where they already reside, in read-only mode. Each audit records its systems, controls, evidence, alerts, and approvals in AI Governance. They can be contracted separately, and any one can be the first.

Code audit · GitHubThe code, in context.
  • Glossary and technical history of each branch.
  • Proportion of human and AI-assisted work.
  • Code checks with automatic verification.
  • V-Seal of the commit, including its glossary and history.
MLOps audit · MLflowThe models the company trains.
  • Reading from MLflow without writing to it.
  • Six deterministic controls, without AI.
  • Approval before moving to production.
  • V-Seal of artifacts, data and results.
Agent audit · six platformsThe agents run by the company.
  • Vertex AI, Copilot Studio, Azure AI Foundry, Amazon Bedrock, Agentforce, and ServiceNow.
  • Eight deterministic controls, without AI.
  • One approval per published configuration.
  • V-Seal of the agent configuration.
What They Have in Common
A recordA model, an agent, and an assistant are entries in the same AI inventory.
A fileEvidence, controls, alerts, and approvals all under a single audit trail.
A sealA document, a commit, a model, and an agent are all sealed the same way.
A verificationThe same public page to check them, without an account.
Integration
08 / 09

Go where the work is already happening.

The integration is designed around the systems, events, and data capture points that the organization chooses to manage. V-PROOF offers APIs, SDKs, webhooks, and specific integrations depending on the deployment.

Interfaces

Portal · Word · Desktop

Spaces for people and teams who need to work with governance built into the process.

Software

GitHub · GitLab

Connected repositories for analysis, technical history, and code evidence.

Models

MLflow

Reading models, versions, controls, and results.

Enterprise

API · SDK · Webhooks

Integration with corporate processes and systems through defined data capture points.

Current Scope

Synchronization, MLOps controls, and certain evaluations run on demand. Current safeguards operate within V-PROOF flows. Gateway, MCP, and extended discovery capabilities are part of the product’s evolution.

V-PROOF
09 / 09

Govern the system.
Govern the agent.
Prove the action.

Connect systems, agents, people, code, and models with a single layer of governance and verifiable evidence.

Talk to the team See AI Agent Governance