AI Governance for Regulated Industries: What Your Compliance Team Needs to Know

A recent Deloitte Canada survey found that 72% of regulated enterprises have adopted or are piloting AI in at least one business function, yet only 21% have a formal AI governance framework in place. That gap is not just a compliance risk. It is a strategic vulnerability that could stall AI programmes entirely when regulators come knocking.

For organisations in financial services, healthcare, and insurance, governance is not optional dressing on top of an AI initiative. It is the foundation that determines whether your AI programme can scale, survive an audit, and retain the trust of customers and regulators alike. This post breaks down what AI governance actually means for regulated Canadian enterprises, which frameworks apply, and how to structure a governance programme that supports innovation rather than blocking it.

What Does AI Governance Actually Mean for Regulated Enterprises?

AI governance is the set of policies, processes, and organisational structures that ensure AI systems are developed, deployed, and operated in a manner that is ethical, transparent, and compliant with applicable regulations. For regulated industries, governance goes beyond general best practices and must address sector-specific obligations.

In practice, AI governance operates at three layers:

1. Strategic layer. Board-level and executive oversight of AI strategy, risk appetite, and ethical principles. This is where the organisation defines what AI is permitted to do and where human oversight is non-negotiable.
2. Operational layer. Day-to-day management of AI models, including risk classification, impact assessments, documentation requirements, and monitoring protocols. This is where most of the practical governance work happens.
3. Technical layer. The engineering controls that enforce governance decisions: access controls, audit logging, model versioning, bias testing pipelines, and explainability tooling. Without this layer, governance policies remain theoretical.

The mistake most organisations make is treating governance as a compliance checkbox rather than an enabling structure. When governance is designed well, it actually accelerates AI deployment by giving teams clear guardrails and reducing the uncertainty that slows down approvals.

Which Regulatory Frameworks Apply to AI in Canada?

Canadian organisations face a layered regulatory landscape for AI. There is no single "AI law" that covers everything, so compliance teams need to understand how multiple frameworks interact.

Federal Frameworks

• PIPEDA (Personal Information Protection and Electronic Documents Act). Canada's federal privacy law applies to AI systems that process personal information. Key obligations include meaningful consent, data minimisation, and transparency about automated decision-making. For a detailed walkthrough, see our guide to PIPEDA-compliant AI.
• AIDA (Artificial Intelligence and Data Act). Part of Bill C-27, AIDA establishes requirements for "high-impact" AI systems, including risk assessments, transparency obligations, and prohibitions on certain AI uses. While the final regulations are still being refined, compliance teams should be preparing now.
• Treasury Board Directive on Automated Decision-Making. Mandatory for federal government agencies, this directive requires algorithmic impact assessments, peer review for high-impact systems, and meaningful explanations for affected individuals. Private-sector organisations working with government contracts may also need to comply.

Sector-Specific Requirements

• Financial services (OSFI). The Office of the Superintendent of Financial Institutions has issued guidance on model risk management (E-23) that explicitly covers AI and machine learning models. Banks, insurers, and trust companies must maintain model inventories, validate AI outputs, and demonstrate that AI-driven decisions can be explained to regulators.
• Healthcare. Provincial health privacy laws (PHIPA in Ontario, HIA in Alberta, PHIA in Manitoba, and equivalents in other provinces) impose additional consent and security requirements on AI systems that process health information. AI used in clinical decision support may also fall under Health Canada's medical device regulations.
• Insurance. In addition to OSFI requirements for federally regulated insurers, provincial regulators are increasingly scrutinising AI-driven underwriting and claims decisions for fairness and bias. Quebec's Law 25 adds privacy impact assessment requirements that directly affect AI deployments.

International Standards

Canadian organisations with global operations also need to consider the EU AI Act (which applies to AI systems used in or affecting EU residents), the NIST AI Risk Management Framework (widely adopted as a best-practice benchmark), and ISO/IEC 42001 (the emerging international standard for AI management systems). Even if these frameworks are not legally binding in Canada, auditors and regulators increasingly reference them.

How Should You Structure an AI Governance Framework?

A practical AI governance framework for regulated industries has five core components. Each one needs to be documented, operationalised, and regularly reviewed.

1. AI Policy and Ethical Principles

Start with a clear AI policy that defines the organisation's principles for AI use. This policy should address:

• What types of AI use cases are permitted, restricted, or prohibited
• Ethical boundaries (e.g., no AI-driven decisions that discriminate on protected grounds)
• Data handling requirements for AI training and inference
• Human oversight requirements for high-stakes decisions
• Accountability structures: who owns AI risk at the executive level

The policy should be approved at the board level and communicated across the organisation. It is not a technical document; it is a strategic commitment.

2. AI Risk Classification

Not all AI systems carry the same risk. A risk classification system allows governance resources to be allocated proportionally. A common approach uses three or four tiers:

• Low risk. Internal productivity tools, document summarisation, code assistance. Minimal governance overhead beyond standard IT security policies.
• Medium risk. Customer-facing chatbots, automated report generation, predictive analytics for operational planning. Requires documentation, testing, and periodic review.
• High risk. Credit decisioning, claims adjudication, clinical decision support, automated underwriting. Requires full impact assessments, explainability testing, bias audits, human-in-the-loop controls, and ongoing monitoring.
• Prohibited. Uses that violate legal requirements or organisational ethical principles, such as covert surveillance or discriminatory profiling.

The classification should be performed before deployment and reviewed whenever the AI system's scope or data inputs change.

3. Algorithmic Impact Assessments

For medium- and high-risk AI systems, an algorithmic impact assessment (AIA) evaluates the potential effects on individuals, groups, and the organisation. A thorough AIA covers:

• Purpose and intended use of the AI system
• Data sources, quality, and potential biases in training data
• Potential for discriminatory outcomes across protected characteristics
• Transparency and explainability capabilities
• Human oversight mechanisms and escalation procedures
• Privacy implications and PIPEDA compliance measures
• Remediation plans if the system produces harmful outcomes

The Treasury Board's Algorithmic Impact Assessment tool provides a useful starting template, even for private-sector organisations. OSFI-regulated entities should align their AIAs with E-23 model risk management expectations.

4. Model Documentation and Inventory

Every AI model in production should have a model card or equivalent documentation that records:

• Model purpose, scope, and limitations
• Training data description and known biases
• Performance metrics and validation results
• Version history and change log
• Owner, reviewers, and approval chain
• Monitoring thresholds and alert conditions

An organisation-wide AI model inventory gives the governance team visibility into what AI is running, where, and at what risk level. Without this inventory, governance is reactive rather than proactive. Organisations investing in AI infrastructure should build model inventory tooling into the platform from day one.

5. Ongoing Monitoring and Review

AI governance does not end at deployment. Models degrade over time as data distributions shift, and regulatory requirements evolve. Ongoing monitoring should include:

• Performance monitoring. Track accuracy, precision, recall, and other relevant metrics against baseline thresholds. Alert when performance drops below acceptable levels.
• Fairness monitoring. Regularly test for disparate impact across protected groups, especially for high-risk decision-making systems.
• Drift detection. Monitor input data distributions for significant changes that may indicate the model is operating outside its trained conditions.
• Incident response. Define procedures for handling AI-related incidents, including escalation paths, root cause analysis, and regulatory notification requirements.
• Periodic review. Schedule formal reviews (quarterly for high-risk, annually for medium-risk) that reassess the risk classification, update documentation, and verify that controls are operating effectively.

What Organisational Structure Supports AI Governance?

AI Governance Committee. A cross-functional body with representation from legal, compliance, IT, data science, business operations, and risk management. This committee sets policy, reviews high-risk AIAs, and escalates issues to the board. In larger organisations, this may be a subcommittee of an existing risk committee.

AI Ethics Lead or Officer. A designated individual responsible for day-to-day governance operations: maintaining the model inventory, coordinating impact assessments, tracking regulatory developments, and serving as the point of contact for internal teams with governance questions.

Embedded Governance Champions. Within each business unit that deploys AI, a governance champion ensures that teams follow established processes. This distributed model prevents governance from becoming a bottleneck while maintaining consistency.

External Advisory. For highly regulated environments, an external advisory panel or periodic third-party audits provide independent assurance. This is especially valuable for organisations preparing for OSFI examinations or responding to regulatory inquiries.

The key is that governance should not be siloed within IT or compliance alone. AI touches every part of the organisation, and governance must reflect that. Firms that invest in vendor consolidation and legacy modernisation often find that governance is easier when the technology stack is rationalised and well-documented.

How Do You Handle Third-Party AI and Vendor Risk?

Most regulated enterprises do not build all their AI in-house. SaaS platforms with embedded AI, cloud-based ML services, and third-party models introduce vendor risk that governance frameworks must address.

Key considerations for third-party AI governance:

• Vendor due diligence. Before adopting a third-party AI tool, assess the vendor's own governance practices, data handling policies, and ability to support your compliance requirements. Request model cards, audit reports, and data processing agreements.
• Contractual protections. Ensure contracts include provisions for data ownership, model transparency, audit rights, incident notification, and the ability to obtain explanations for AI-driven outputs.
• Ongoing monitoring. Third-party AI models can change without notice. Establish mechanisms to detect when vendor models are updated and assess the impact on your compliance posture.
• Exit planning. Avoid vendor lock-in for critical AI capabilities. Document how you would transition away from a vendor's AI system if governance or compliance concerns arise.

OSFI's B-10 guideline on outsourcing applies to AI services provided by third parties, and regulated financial institutions should integrate AI vendor risk into their existing third-party risk management programmes.

What Are the Costs of Getting AI Governance Wrong?

The consequences of inadequate AI governance in regulated industries extend well beyond regulatory fines:

• Regulatory action. OSFI can issue supervisory letters, impose conditions on business activities, or require remediation plans. Provincial privacy commissioners can order organisations to cease AI processing of personal information.
• Reputational damage. A biased AI-driven lending decision or a healthcare algorithm that produces disparate outcomes generates media coverage and erodes customer trust far faster than traditional compliance failures.
• Operational disruption. Without governance, AI projects proliferate in silos. When a compliance issue is discovered, organisations often respond by freezing all AI initiatives, creating an innovation bottleneck that can take months to clear.
• Legal liability. Individuals harmed by AI-driven decisions may pursue legal action. Without documentation of governance processes, impact assessments, and monitoring activities, defending those decisions becomes significantly harder.
• Talent attrition. Data scientists and AI engineers increasingly want to work for organisations that take ethics and governance seriously. Poor governance practices make it harder to attract and retain the technical talent needed to build responsible AI systems.

The cost of building governance up front is a fraction of the cost of retrofitting it after a compliance failure. Organisations that treat governance as an investment rather than an expense consistently outperform those that defer it. For a broader look at how to ensure transparency in automated ERP decisions specifically, see our post on audit-ready AI in ERP environments.

Key Takeaways