AIGR^™ Launches Independent Artificial Intelligence Governance Ratings

TORONTO, CANADA — February 22, 2026 — AIGR^™ — Artificial Intelligence Governance Ratings today announced the launch of its independent AI governance ratings framework, introducing a structured approach for assessing the governance readiness of artificial intelligence systems used by enterprises and institutions.

AIGR^™ is designed to provide organizations, boards, risk leaders and other stakeholders with a clear governance opinion based on documented evidence, defined controls, reviewer assessment and critical-gate analysis.

The framework evaluates how an AI system is governed rather than simply evaluating the technical performance of the underlying model.

Each assessment examines the governance environment surrounding a defined AI system, including accountability, risk management, evidence quality, data governance, human oversight, cybersecurity, monitoring and operational controls.

The objective is to transform fragmented AI governance information into a structured and interpretable rating that organizations can use in governance, oversight and decision-making processes.

Establishing an independent signal for AI governance

Artificial intelligence is increasingly embedded within business processes, operational systems, customer interactions and institutional decision-making.

As deployment increases, organizations face a growing challenge: determining whether an AI system has the governance controls, evidence and accountability structures required for responsible operation.

Policies alone do not demonstrate that controls are operating.

Technical documentation alone does not demonstrate organizational accountability.

A model inventory alone does not demonstrate whether critical governance obligations have been met.

AIGR^™ has been developed around a different premise:

AI governance should be evidenced, assessed and capable of producing a clear decision signal.

The AIGR^™ framework converts governance evidence into a structured assessment record consisting of defined controls, evidence states, governance scoring, critical gates, reviewer conclusions and an AIGR^™ rating designation.

The resulting rating is intended to provide a concise representation of governance readiness while preserving the underlying evidence and assessment record required to understand how that conclusion was reached.

From governance documentation to governed evidence

An AIGR^™ assessment begins with a clearly defined AI system and assessment scope.

The system is evaluated as an operating environment rather than as an isolated algorithm.

Assessment scope may include:

• the AI system or application being evaluated;
• its intended business use;
• accountable system and business owners;
• underlying models and technology dependencies;
• data sources and data governance practices;
• third-party technology providers;
• deployment environment;
• applicable jurisdictions;
• users and affected stakeholders;
• lifecycle stage;
• risk classification;
• and the evidence period covered by the assessment.

Governance requirements are then mapped to specific controls and evidence expectations.

Evidence may include policies, system records, approval documentation, validation results, testing records, monitoring reports, risk assessments, control attestations and other artifacts demonstrating that a governance requirement is operating in practice.

AIGR^™ evaluates whether submitted evidence is relevant, attributable, current and sufficient for the control being assessed.

Evidence therefore becomes part of a governed assessment record rather than simply a collection of uploaded documents.

A two-layer ratings model

The AIGR^™ methodology separates analytical measurement from the published rating opinion.

The Governance Score^™ provides a quantitative analytical measure on a 0–100 scale.

The AIGR^™ rating designation translates the assessment into a governed rating signal designed for executive and institutional interpretation.

This distinction is important.

A high numerical score does not automatically produce a high rating.

Certain governance failures may represent risks significant enough to restrict a rating regardless of the overall average score.

AIGR^™ therefore incorporates critical-gate logic into its methodology.

Where a required critical control remains unresolved, the resulting rating may be constrained until sufficient evidence demonstrates that the issue has been addressed.

This prevents strong performance in lower-risk areas from masking a material governance deficiency.

Critical gates

Critical gates are designed to identify governance conditions that may require resolution before a system can receive a stronger rating outcome.

Depending on the system and sector, critical gates may address areas including:

• accountability and ownership;
• prohibited or unauthorized use;
• human oversight;
• privacy;
• cybersecurity;
• data provenance;
• validation;
• discrimination or fairness risks;
• retention and deletion requirements;
• incident management;
• regulatory obligations;
• or other controls considered fundamental to the governance of the assessed system.

An open critical gate is recorded directly within the assessment.

The associated evidence deficiency, responsible control and rating impact remain visible within the governed assessment record.

This structure is intended to make the relationship between evidence and rating outcome understandable to decision-makers.

The AIGR^™ assessment lifecycle

The AIGR^™ methodology organizes an assessment around a controlled lifecycle.

1. Scope

The AI system, intended use, ownership, deployment context and applicable assessment boundaries are established.

2. Evidence

Governance controls are mapped to defined evidence requirements.

3. Assessment

Evidence is evaluated against the applicable AIGR^™ control framework and assessment criteria.

4. Review

Findings, evidence states, scoring and critical gates are reviewed before a rating recommendation is finalized.

5. Rating

The Governance Score^™, critical-gate status and reviewer conclusions are translated into an AIGR^™ rating designation.

6. Ongoing Review

Material changes, incidents, new evidence or changes in operating conditions may require reassessment or other rating action.

This lifecycle is designed to ensure that a rating reflects a defined system and evidence period rather than functioning as a permanent designation.

Independence of the rating process

AIGR^™ is positioned as an independent artificial intelligence governance ratings organization.

The ratings methodology is designed to separate commercial engagement, assessment workflow and remediation activity from final rating determination and reviewer approval.

The purpose of this separation is to protect the integrity of the resulting governance opinion.

Organizations may receive information concerning assessment requirements, evidence deficiencies and remediation priorities. However, commercial considerations are not intended to determine the rating assigned to an AI system.

Rating decisions are expected to follow documented methodology, evidence requirements, critical-gate rules and reviewer processes.

AIGR^™ also maintains a defined approach to conflicts of interest, methodology governance, rating review and challenges to assessment findings.

A rating is an opinion—not a certification

An AIGR^™ rating represents an opinion regarding the governance posture of a defined AI system within the scope and evidence period assessed.

It is not intended to represent:

• government approval;
• regulatory authorization;
• legal advice;
• compliance certification;
• cybersecurity certification;
• assurance that an AI system cannot fail;
• a guarantee of technical performance;
• a credit rating;
• or an endorsement of a company, product or investment.

The rating provides a standardized governance signal based on the evidence available at the time of assessment.

Organizations remain responsible for their own legal, regulatory, technical, security and operational obligations.

Sector-specific governance frameworks

The initial AIGR^™ framework architecture is organized around four sector environments:

Each sector framework operates through the same core evidence model while incorporating controls relevant to its operating environment.

Designed for enterprise decision-making

AIGR^™ ratings are designed to provide governance information that can be interpreted beyond specialist AI teams.

The framework may support decision-making by:

• Boards and executives seeking visibility into whether material AI systems are operating within defined governance boundaries.
• Chief Risk Officers and risk teams requiring structured evidence of control effectiveness and outstanding governance gaps.
• Chief Information Security Officers assessing the relationship between AI governance, security controls, system access and third-party risk.
• Legal and compliance teams evaluating evidence associated with organizational governance requirements.
• Technology and AI leaders seeking a structured operating model for moving AI systems from experimentation into governed production environments.
• Procurement and third-party risk teams evaluating AI systems supplied or operated by external technology providers.

The rating is intended to complement—not replace—the detailed assessment record underlying it.

Evidence before opinion

A central principle of AIGR^™ is that a rating should be traceable to evidence.

Governance controls are therefore associated with identifiable evidence records wherever possible.

Evidence is evaluated according to factors including:

• Relevance — whether the evidence demonstrates the control being assessed.
• Ownership — whether responsibility for the evidence and underlying control can be identified.
• Currency — whether the evidence reflects the relevant operating period.
• Sufficiency — whether the evidence provides an adequate basis for assessment.

Controls without sufficient evidence may remain open regardless of whether an organization states that a process exists.

This evidence-first approach is intended to strengthen comparability and reduce dependence on unsupported representations.

Rating actions over time

AI systems change.

Models are updated. Data sources change. Vendors change. Controls mature. Incidents occur. Deployment scope expands.

For this reason, AIGR^™ ratings are designed to operate within a lifecycle rather than as permanent labels.

Depending on circumstances, rating actions may include:

• initial rating;
• affirmation;
• review;
• upgrade;
• downgrade;
• conditional status;
• withdrawal;
• or not-rated status.

A material change to the assessed system or its governance environment may trigger additional evidence requirements or reassessment.

This approach is intended to preserve the relationship between the published rating and the actual operating conditions of the AI system.

Building comparability across AI systems

As the number of completed assessments increases, AIGR^™ intends to develop controlled sector cohorts and governance maturity benchmarks.

Benchmark development is expected to be subject to confidentiality, consent, methodological quality and minimum-cohort requirements.

The objective is not simply to rank organizations.

The longer-term objective is to establish a structured evidence base that can help organizations understand how AI governance practices evolve across sectors and operating environments.

Supporting responsible AI deployment

Organizations are moving from isolated AI experiments toward broader enterprise adoption.

That transition increases the need for mechanisms capable of answering fundamental governance questions:

• Who owns the system?
• What is it permitted to do?
• What evidence demonstrates that controls are functioning?
• Who approved the deployment?
• What material risks remain unresolved?
• What happens when the system changes?
• What evidence supports continued operation?

AIGR^™ is designed to make those questions part of a repeatable governance process.

The resulting rating provides a high-level signal, while the underlying assessment creates a structured record for governance teams and decision-makers.

Assessment availability

Organizations seeking an AIGR^™ assessment can begin by defining a specific AI system and its intended operating context.

AIGR^™ recommends assessing identifiable systems rather than attempting to assign a single organization-wide rating to an entire AI portfolio.

This creates a clearer relationship between governance controls, evidence, risk and the resulting rating.

Organizations interested in initiating an assessment can submit a request through aigrglobal.com.

About AIGR^™

AIGR^™ — Artificial Intelligence Governance Ratings is an independent AI governance ratings initiative focused on evidence-based assessment of artificial intelligence systems.

AIGR^™ evaluates governance controls, evidence quality, accountability, risk management and operational readiness to produce structured governance assessments and rating opinions.

Its methodology combines a Governance Score^™, critical-gate analysis and AIGR^™ rating designations to provide organizations and institutional stakeholders with a clear and interpretable view of AI governance readiness.

AIGR^™ initially supports sector-specific assessment frameworks for Real Estate, Health, Finance and Government.

For additional information, methodology materials and assessment requests, visit aigrglobal.com.