What Most Banks Are Missing about Canada’s E-23

Canada’s latest AI risk guidance isn’t just another compliance framework. It’s a warning shot about the future of AI governance.

For years, model risk management in banking followed a familiar rhythm: document the model, validate performance, monitor outcomes, repeat.

Then generative AI arrived.

Suddenly, financial institutions weren’t just managing static credit models or well-understood forecasting systems. They were deploying adaptive AI systems capable of generating content, influencing decisions, interacting with customers, and evolving faster than traditional governance processes could keep up.

That’s the backdrop for Canada’s new OSFI Guideline E-23: Model Risk Management for Federally Regulated Financial Institutions, a document many organizations still view as simply a Canadian update to model governance expectations.

That’s a mistake because buried inside E-23 is a much bigger message: traditional model governance operating models are no longer sufficient for modern AI systems that incorporate autonomous agents.

Many banks haven’t fully grasped what that means yet.

E-23 Isn’t Really About Documentation

At first glance, E-23 looks familiar.

It emphasizes:

• model inventories
• validation rigor
• governance structures
• accountability
• monitoring and controls

None of that is new to Canadian financial institutions.

But the spirit of E-23 is fundamentally different from older guidance like SR 11-7 (which SR26-2 recently supplanted).

OSFI repeatedly frames model risk management as an enterprise-wide, lifecycle-driven discipline, not a periodic compliance exercise. The guideline explicitly calls for ongoing oversight across development, implementation, use, monitoring, change management, and decommissioning.

While that may sound subtle, it isn’t. In fact, it carries a weighty implication: governance can no longer happen in isolated checkpoints. Instead, it must become operational.

The Shift Most Banks Are Underestimating

Historically, many validation programs were built around relatively stable models:

• quarterly monitoring
• annual reviews
• static documentation
• clearly defined development teams

AI systems (especially GenAI) break those assumptions.

Meanwhile, modern AI introduces:

• rapidly changing data environments
• third-party foundation models
• opaque model behavior
• prompt engineering risks
• continuous tuning and retraining
• decentralized experimentation across business units

E-23 acknowledges this reality more directly than many frameworks currently in force.

The guideline specifically highlights the need for institutions to govern:

• externally sourced models
• model changes over time
• data quality and lineage
• explainability challenges
• ongoing performance degradation
• evolving use cases and deployment contexts

In other words: the “validate once and monitor later” approach is dead.

E-23 Quietly Raises the Bar for AI Accountability

One of the most overlooked aspects of E-23 is that it expands accountability far beyond model developers and validators.

OSFI makes clear that senior management and boards are expected to understand:

• model limitations
• governance processes
• risk tolerances
• escalation procedures
• the institution’s aggregate model risk exposure

That’s a significant shift.

Many institutions still treat AI governance as a technical issue owned primarily by data science or MRM teams.

E-23 reframes it as an operational and strategic risk issue.

Why does that matter?

Because it means governance evidence can no longer live in disconnected spreadsheets, static PDFs, or siloed validation reports.

Executives and regulators increasingly expect:

• traceability
• centralized oversight
• repeatable workflows
• transparent approvals
• auditable monitoring processes

That’s difficult to achieve manually at enterprise scale.

The Real Challenge Isn’t Policy; It’s Execution

Most large financial institutions already have AI principles. Many have governance committees, and some even have GenAI policies. But policies are the easy part; operationalizing them is where things get complicated. Consider what E-23 effectively requires organizations to maintain continuously:

• model inventories
• validation evidence
• monitoring results
• testing documentation
• approvals and attestations
• data lineage
• performance thresholds
• remediation workflows
• change histories

Now multiply that across:

• hundreds of traditional models
• rapidly growing AI inventories
• multiple business lines
• third-party AI vendors
• evolving regulatory expectations

The operational burden becomes massive, leading many organizations to discover that AI governance is increasingly a systems problem, not just a policy problem.

E-23 Signals Where Global AI Regulation Is Heading

Even institutions outside Canada should pay attention.

E-23 reflects a broader global trend emerging across:

• the EU AI Act
• NIST AI RMF
• PRA and ECB supervisory expectations
• U.S. banking guidance on AI oversight

The common direction is becoming clear:

• continuous governance
• lifecycle-based oversight
• enterprise accountability
• stronger documentation expectations
• explainability and transparency requirements
• governance of third-party AI systems
• evidence-driven supervision

In many ways, E-23 acts as an early blueprint for what modern AI assurance may look like across financial services globally. This means that regulators are moving faster than many operating models can adapt.

What Forward-Looking Banks Are Doing Differently

The institutions getting ahead of this shift are moving beyond adding more governance committees. They’re rethinking the infrastructure behind AI governance itself. That includes:

• automating documentation generation
• centralizing validation workflows
• standardizing governance artifacts
• integrating monitoring into development pipelines
• creating real-time visibility into AI inventories and risk exposure
• enabling continuous validation and testing

In other words, they’re treating AI governance as a scalable operational capability instead of a collection of manual compliance activities. That distinction will matter increasingly over the next several years because the institutions that struggle most with E-23 likely won’t be the ones lacking policies. They’ll be the ones trying to operationalize modern AI governance using workflows designed for a much simpler era of modeling.

The Bigger Message Behind E-23

E-23 is easy to read as a Canadian regulatory framework, but that misses the bigger story.

The guideline is really a signal that financial institutions are entering a new phase of AI life cycle management where:

• governance becomes continuous
• oversight becomes enterprise-wide
• evidence becomes operational
• and scalability becomes essential

The banks that recognize this early will have a major advantage, while the ones that don’t may soon discover that managing AI risk manually no longer scales.