AI Assurance and AI Governance
AI assurance is independent, evidence-based evaluation of whether an AI system is safe, lawful, reliable, and fit for its stated purpose in its real deployment context. AI governance is the operating system around the AI that makes those claims controllable over time through clear accountabilities, documented decisions, risk controls, and continuous monitoring.
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About This ServiceAI Assurance and AI Governance
Many AI products ship with performance numbers but weak evidence about validity, bias, misuse risk, or downstream harm. Assurance closes the evidence gap. Governance keeps the system inside its intended boundaries after launch. The outcome is decision-grade clarity on what the system can claim, a documented risk position tied to concrete controls and owners, practical remediation actions sequenced by severity, and monitoring design so safety and compliance do not depend on best intentions.
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The AGILE Assurance Model
Six components we evaluate to produce a decision-grade assurance package.
Asserted claims and intended use
We audit the system’s stated purpose, decision impacts, and prohibited uses so evaluation targets the real risk.
Governance and accountability
We map decision rights, escalation paths, documentation duties, and change control so responsibility is unambiguous.
Integrity of data and measurement
We test whether inputs and labels are defensible for their use, including construct alignment where human attributes are measured or inferred.
Lifecycle risk controls
We assess how risks are identified, measured, mitigated, and re-checked across build, deployment, and updates.
Legibility and contestability
We verify that outputs are explainable at the level required by users and regulators, and that people can challenge or correct outcomes.
Evidence and monitoring
We define the minimum evidence standard for release, then specify drift, bias, and incident monitoring with trigger thresholds.
What You Receive
A decision-grade assurance package with clear evidence, risk scores, and actionable remediation.
Our Approach
A structured, step-by-step methodology tailored to every engagement.
Scope
Confirm system claims, deployment context, affected populations, and decision criticality. Define evidence thresholds and the assurance boundary.
Map the System
Test and Evidence
Design Governance
Deliver Package
| Step | Title | Description |
|---|---|---|
| 01 | Scope | Confirm system claims, deployment context, affected populations, and decision criticality. Define evidence thresholds and the assurance boundary. |
| 02 | Map the System | Document the model, data pipeline, human-in-the-loop points, and operational dependencies. Identify foreseeable misuse and failure modes. |
| 03 | Test and Evidence | Evaluate performance and reliability in context. Where human outcomes are involved, test validity, subgroup stability, and potential bias mechanisms. |
| 04 | Design Governance | Implement risk register, control set, documentation pack, and accountability assignments. Specify thresholds, escalation, and incident handling. |
| 05 | Deliver Package | Provide a clear evidence map, risk-scored findings, and remediation plan. Set up monitoring and periodic re-assurance cadence. |
Who This Is For
This service is designed for organisations and teams navigating the intersection of AI, people, and accountability.
Executive Leaders
Leaders deploying AI in hiring, performance, wellbeing, education, or customer decisions who need decision-grade clarity.
Product and Engineering Teams
Teams shipping AI features into regulated or high-trust contexts who need independent validation.
Risk and Compliance Teams
Risk, compliance, and internal audit professionals needing defensible AI oversight and documentation.
AI Vendors
Vendors needing third-party assurance before enterprise procurement or regulatory review.
Frameworks & Standards
Every engagement is anchored to recognised standards and frameworks for accountability and rigour.
Engagement model: Typical engagement runs 4 to 8 weeks depending on system complexity and data access. Work can be structured as a one-off audit, or as an ongoing assurance function with quarterly re-assurance.
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Let\u2019s discuss how this service can support your needs.