AI Governance Platform

Reliable Data.
Responsible AI.

Regulations are tightening. Data leakage through AI is rising. TrustedGovernance maps your data and AI landscape, tests it before attackers do, fixes what it finds, and turns the evidence into audit-ready compliance — continuously, not once a year.

See how it works

app.trustedgovernance.ai/dashboard

Trusted Governance

Governance workspace

100%

Understand

50%

Simulate

30%

Mitigate

60%

Audit

◎Risk mapped across the org

◈Guardrails validated & enforced

▤Findings mapped to regulation

✓Evidence ready for audit

↳This is your workspace — the view that opens right after you log in.

The Governance Lifecycle

From blind spot to compliance in four steps

Every stage feeds the next — what you learn in Understand targets what you test in Simulate; what breaks in Simulate drives what gets fixed in Mitigate; every fix becomes evidence in Audit.

01 / 04

◎

Understand

Know your landscape

Maps your tech stack, data assets, ML/AI systems, stakeholders and policies — the foundation every later check relies on.

Data landscapeML/AI systemsStakeholders & policies

02 / 04

◈

Simulate

Test before attackers do

Runs automated red-teaming and risk checks, targeted to your specific AI architecture, based on what Understand mapped.

Prompt injectionBias & fairnessHallucination checks

03 / 04

◫

Mitigate

Fix, not just flag

Pairs every finding with a concrete, prioritised remediation plan, then re-tests to confirm the fix actually closes the gap.

Remediation plansGuardrail configRe-test validation

04 / 04

▤

Audit

Prove compliance on demand

Maps every finding and fix to the applicable regulation and converts it into structured, audit-ready evidence.

Clause-level assessmentEvidence scoringAuto-generated reports

Ownership is a label, not a responsibility

Data gets assigned in a catalog or a policy document — and that's where accountability ends.

Shadow AI is the norm

Models and agents get deployed with no registry, no owner, and no risk classification.

Evidence gets assembled after the fact

When a regulator asks what a model did, teams reconstruct evidence from logs, emails and screenshots.

Governance doesn't fail because organisations don't care. It fails because visibility, testing, remediation and evidence live in different places — and none of them talk to each other.

Regulators have moved from issuing guidance to conducting audits, imposing fines, and enforcing operating restrictions. Long before a model is in question, they look at the data behind it — how it was collected, secured and controlled.

TrustedGovernance closes the loop: one continuous system that carries what it learns in Understand through Simulate, Mitigate and Audit — so when a regulator, auditor, or board asks for proof, it already exists.

Compliance Frameworks

Mapped to the standards that matter

Every finding, fix and audit report can be mapped to the regulations and frameworks your organisation is actually measured against.

EU AI Act

The world's first comprehensive legal framework for AI — a risk-based approach with obligations for high-risk systems.

ISO 42001

The AI Management Systems standard — guides organisations to establish, implement and improve AI governance.

ISO 27001

The Information Security Management standard — protects information assets and ensures security by design.

NIST AI RMF

The AI Risk Management Framework — helps manage AI risk across the entire AI lifecycle.

Extensible to additional regulations, standards, and industry-specific frameworks as your compliance surface grows.

Explore TrustedGovernance

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Existing dashboard, Understand workflow & APIs