By Matthew Yildirim
Executive Summary
The Financial Industry Forum on Artificial Intelligence (FIFAI II), led by the Office of the Superintendent of Financial Institutions, signals how AI is evolving in Canada—from a technology initiative to a core risk domain alongside cyber security and operational risk.
Although not a formal regulation, it reflects where expectations are heading across financial services and beyond. AI is expanding attack surfaces, increasing third-party dependencies, and accelerating fraud capabilities.
For Canadian organizations, the challenge is no longer whether to adopt AI, but how to adopt and scale it in a controlled, risk-managed way.
What is FIFAI II?
The Financial Industry Forum on Artificial Intelligence (FIFAI II) is a collaborative initiative led by the Office of the Superintendent of Financial Institutions (OSFI), bringing together financial institutions, regulators, and academic partners to examine how AI is being adopted across Canada’s financial sector.
Rather than a regulation or standard, FIFAI II serves as a coordination and insight-driven effort. It points out how organizations are currently using AI, where risks are emerging, and how governance practices are evolving in response.
The second phase builds on earlier discussions by shifting focus from awareness to execution, specifically, how institutions can adopt AI in a way that balances innovation with operational and systemic risk.
At its core, FIFAI II reflects a broader reality: AI is no longer an experimental tool, but it is becoming part of critical financial infrastructure, and its risks must be managed accordingly.
A Brief History
The rapid rise of artificial intelligence in financial services did not happen in isolation. Over the past decade, Canadian institutions have steadily expanded their use of data-driven models, including machine learning, across fraud detection, credit scoring, and customer analytics.
Early discussions around AI focused primarily on potential efficiency gains, automation, and improved decision-making. However, as adoption expanded, so did concerns around model transparency, bias, and operational risk. Financial institutions began to recognize that AI systems, particularly those integrated into critical processes, could introduce new forms of vulnerabilities.
The initial phase of the Financial Industry Forum on Artificial Intelligence (FIFAI I) reflected this awareness stage, bringing stakeholders together to explore emerging risks and establish a shared understanding of AI’s role.
FIFAI II represents the next step in that evolution. The conversation has shifted from whether to use AI to how to deploy it responsibly at scale, while addressing governance challenges, third-party dependencies, and the broader impact of AI-driven decisions.
Within the Canadian context, this aligns with a regulatory approach that prioritizes resilience and systemic stability. Institutions are not expected to move the fastest, but they are expected to scale AI capabilities in a controlled, risk-aware way that preserves operational and systemic stability.
The AGILE Approach to AI Risk
A key outcome of FIFAI II is the emphasis on an adaptive approach to AI governance, often described through the concept of “AGILE.” While not a formal framework, it reflects a shift in how organizations are expected to manage AI risk.
Unlike conventional systems, AI models are not static. They evolve over time, depend on changing data, and are often integrated across multiple environments. This makes traditional, one-time risk assessments insufficient. Instead, FIFAI II promotes a continuous and iterative model of risk management.
Core principles include:
- Incremental adoption: Introduce AI gradually with validation at each stage
- Continuous monitoring: Track model performance and drift over time
- Feedback and adjustment loops: Adapt quickly as conditions change
- Integrated governance: Embed AI into existing risk and security functions
In practice, this means treating AI less like a project and more like a living system that requires ongoing oversight. Governance is no longer about approving a model before deployment, but it is more about maintaining confidence throughout its lifecycle.
What This Means for Canadian Businesses
While FIFAI II is rooted in financial services, its implications extend across the broader Canadian business landscape.
Large Enterprises
For larger organizations, especially in regulated sectors, AI is becoming part of formal governance, risk, and compliance (GRC) frameworks.
This includes:
- Treating AI as a critical system
- Strengthening model risk management and auditability
- Increasing scrutiny of third-party AI providers
- Aligning with evolving expectations from regulators such as the Office of the Superintendent of Financial Institutions
AI is, now, the part of the risk landscape.
Small and Mid-Sized Businesses (SMEs)
SMEs may not be directly regulated, but they are affected through ecosystem:
- Larger partners impose security and risk expectations
- Dependence on third-party AI tools introduces hidden exposure
- Limited internal controls increase governance challenges
AI adoption is often happening faster than maturing the risk management.
Cyber Security Implications
AI introduces a new layer of complexity to the threat landscape, expanding the attack surface in ways traditional controls may not fully address.
Expanded Attack Surface
AI systems create new entry points:
- APIs and model interfaces
- Data pipelines for training and inference
- Integrations with external platforms
They are also vulnerable to emerging attack types such as prompt injection, data poisoning, and model extraction.
Increased Third-Party Risk
Reliance on external AI providers reduces visibility and introduces shared responsibility challenges. This aligns with ongoing concerns from regulators such as the Office of the Superintendent of Financial Institutions around supply chain risk.
Detection and Response Challenges
AI is also enhancing attacker capabilities:
- More convincing phishing and social engineering
- Automated reconnaissance and attack scaling
This results in higher alert volumes and faster attack cycles, requiring security teams to evolve beyond traditional detection methods.
Operational Reality
Organizations must now:
- Secure their own AI systems
- Defend against AI-enabled threats
AI must be integrated into core security operations, including threat modeling, monitoring, and incident response.
Fraud in the Age of AI
AI is already widely used in fraud detection, but it is also enhancing fraud capabilities of the threat actors.
This creates a fraud arms race.
Evolving Fraud Techniques
Attackers can now:
- Generate convincing phishing campaigns
- Create synthetic identities
- Automate account takeover attempts
- Use deepfake-based impersonation
These attacks are faster, more scalable, and harder to detect.
Pressure on Detection Systems
Fraud detection must now contend with:
- Faster attack cycles
- Subtle, behaviour-based fraud patterns
- Reduced signal clarity
Continuous tuning and validation of detection models become essential. Canada’s concentrated financial system increases systemic exposure. Successful fraud techniques can propagate quickly across institutions, exploiting similar controls.
What Comes Next
FIFAI II signals a broader shift toward structured AI governance in Canada.
Increasing Regulatory Clarity
Regulators such as the Office of the Superintendent of Financial Institutions are expected to further define expectations around:
- AI governance
- Model risk management
- Third-party dependencies
Convergence with Existing Risk Domains
AI risk will increasingly integrate with:
- Cyber security
- Fraud and financial crime
- Operational risk
- Data governance
Industry Standardization
Over time, organizations can expect:
- More consistent governance practices
- Shared risk frameworks
- Increased regulatory coordination
Strategic Direction
AI governance will evolve from informal practices to a standard part of enterprise risk management. Organizations that align early will gain an advantage in both compliance and operational resilience.
Practical Recommendations
Organizations do not need to wait for regulation to act. Existing risk and security frameworks can be extended to cover AI.
For CISOs and Security Teams
- Treat AI systems as critical assets
- Extend threat modeling to AI-specific risks
- Improve monitoring and logging
- Prepare for AI-enabled threats
For Risk and Compliance Functions
- Integrate AI into risk and audit frameworks
- Strengthen third-party risk management
- Define clear ownership and accountability
For Business and Technology Leaders
- Avoid uncontrolled AI adoption
- Adopt incrementally
- Align AI initiatives with risk appetite
Conclusion
Organizations adopting AI do not need to build entirely new structures, but they do need to evolve their existing risk and governance frameworks. Those that act early by building visibility, integrating governance, and treating AI as a continuously managed capability will be best positioned to balance innovation and risk.