Canada's financial regulators have introduced a new framework for managing artificial intelligence risks in banking and insurance, drawing on insights from over 170 industry experts across government, academia, and financial institutions. The AGILE framework, unveiled in March 2026 by the Office of the Superintendent of Financial Institutions (OSFI) and the Global Risk Institute (GRI), addresses escalating threats from AI-enabled cybercrime, third-party vulnerabilities, and systemic financial risks that have emerged since the sector's first major AI governance initiative three years ago. The financial services industry faces a paradox: AI is essential for competing and defending against sophisticated threats, yet the same technology amplifies risks. Fraudsters and cybercriminals now operate with unprecedented speed and scale, automating attacks like spear phishing and creating synthetic identities to infiltrate organizations through hiring processes. At the same time, Canadian banks and insurers have earned global recognition for transparency in responsible AI practices, with Canada's five largest banks and two major insurers ranking among the top 15 globally for "transparency of responsible AI activities" in 2025, according to benchmarking firm Evident Insights. What Risks Are Financial Institutions Actually Facing? Between May and November 2025, OSFI and partner agencies conducted four workshops examining specific threat areas. The discussions revealed a complex risk landscape that extends far beyond internal governance concerns addressed in the sector's previous AI framework, known as EDGE (Explainability, Data, Governance, and Ethics). - Cybersecurity Threats: AI is enabling attackers to automate and scale sophisticated intrusions, from phishing campaigns to identity fraud targeting hiring departments and critical infrastructure. - Financial Crime: Money laundering, fraud detection evasion, and synthetic identity schemes are becoming harder to detect as criminals leverage AI's speed and sophistication. - Consumer Protection Gaps: Transparency and explainability failures in AI-driven lending, insurance, and advisory services expose consumers to bias, fraud, and hidden harms. - Third-Party Dependencies: Growing reliance on a small number of AI providers and opaque supply chain relationships heighten systemic fragility across the sector. - Financial Stability Risks: AI-driven operational disruptions, correlated trading behaviors, and potential credit risk impacts introduce new challenges for systemic stability. The workshops also identified talent shortages and uneven upskilling as barriers to responsible innovation. Many institutions lack the expertise to implement robust AI governance, creating execution risks and competitive pressures that can push firms toward shortcuts. How to Implement the AGILE Framework in Your Organization The AGILE framework provides a five-pillar approach for financial institutions to navigate AI risks while capturing innovation benefits. Each pillar addresses a specific dimension of responsible AI adoption: - Awareness: Stay ahead of AI-driven risks by understanding how technologies reshape the risk landscape through organizational enhancements such as AI oversight, board engagement, and expanded monitoring and stress testing scenarios. - Guardrails: Make best practice regular practice with strong controls and data integrity measures that prevent misuse, bias, and unintended consequences in AI systems. - Innovation: Pursue competitive advantage through responsible AI adoption that aligns with principle-based governance, maintaining trust and resilience while unlocking efficiency and improved decision-making. - Learning: Build organizational capability through continuous monitoring, feedback loops, and knowledge sharing that help teams adapt to rapidly evolving AI risks and opportunities. - Ecosystem Resiliency: Strengthen systemic defenses by reducing dependence on single AI providers, diversifying supply chains, and fostering collaboration across institutions and regulators. The framework builds on EDGE principles established in the sector's first AI governance initiative, which emphasized explainability, consumer-centric approaches, and strong risk-based governance. However, AGILE adds "agility" as a central theme, recognizing that financial institutions must move dynamically to respond to fast-evolving risks while capturing AI's benefits. Why Should Financial Leaders Care About This Now? The timing of this framework reflects an urgent shift in the threat environment. AI is no longer just a tool for internal optimization; it has become a weapon for adversaries and a critical defense mechanism simultaneously. Institutions increasingly need AI not only to compete but to strengthen their defenses and risk management capabilities. The report emphasizes that continued responsible AI adoption is necessary for both competitive resilience and effective management of inherent AI risks, as well as heightened defense against sophisticated external threats. Canada's financial sector has structural advantages that position it to lead responsibly in AI adoption. The country's strong data foundations, disciplined risk culture, and existing commitment to EDGE principles provide a foundation for implementing AGILE. However, the framework also signals that regulators expect institutions to move beyond passive compliance toward proactive, dynamic risk management as AI capabilities accelerate. The FIFAI II process involved representatives from banks, insurers, asset managers, non-financial corporations, consumer advocates, universities, research institutes, government agencies, and regulatory bodies. This broad collaboration underscores a shared recognition that AI risks and opportunities cannot be managed by individual institutions alone; they require coordinated action across the financial ecosystem and between public and private sectors. For financial institutions, the AGILE framework serves as both a roadmap and a reality check. It acknowledges that AI is reshaping operating models and competitive dynamics globally, and that institutions must balance innovation with principle-based governance to maintain trust and resilience. The framework's emphasis on awareness, guardrails, innovation, learning, and ecosystem resiliency provides a structured approach to managing the dual challenge of capturing AI's productivity and growth potential while defending against unprecedented threats.
