Karsten Wenzlaff, Advisor
August 26th, 2025
Karsten Wenzlaff is a Crowdfunding and Fintech Expert from Germany. He is Secretary-General of Digit[...]
Canada Reviews AI Transparency and Agent Governance
September 11, 2026 | NCFA Regulatory Insight | Artificial Intelligence And Data, Regulation And Policy, Risk Compliance And Regtech
AI Literacy, Transparency and Agent Governance
On September 9, 2026, the Government of Canada launched a National AI Literacy Initiative with the Alberta Machine Intelligence Institute. The $13 million partnership is expected to reach up to 1 million post secondary students and more than 50,000 K to 12 educators, alongside free learning for workers and other Canadians. The program sits under Canada's AI for All strategy and focuses on helping people understand AI, use it responsibly and recognize risks such as bias, misinformation and privacy loss.
Ottawa is working on the governance side at the same time. Its AI transparency consultation remains open until September 23 and asks whether Canada needs stronger ways to identify AI generated content, tell people when they are interacting with AI, explain system capabilities, track serious incidents and record what AI agents actually do. The consultation paper says 19.2% of Canadian companies used AI to produce goods or deliver services in the second quarter of 2026, up from 12.2% a year earlier and three times the 2024 level.
The federal government has already been working through many of those questions for its own use. On May 22, it published an agentic AI guide for departments and agencies. Ottawa says agentic AI is defined more by what a system “does” than what it produces because these systems can plan tasks, use tools, interact with other systems and act with limited human supervision.
The guide does not create new legal requirements for banks, fintechs or other private companies. It does offer a useful view of how Ottawa thinks AI governance changes once software gets permission to act rather than simply produce an answer.
Canada Defines Four Levels of AI Agent Autonomy
Ottawa describes four levels of autonomy.
- Level 1, AI suggests an action while a person decides what happens
- Level 2, it prepares an action for approval
- Level 3, lets an agent act under delegated permissions, record what it did and notify the user
- Level 4, an adaptive agent can monitor changing conditions, act within set limits and escalate exceptions
The government says agents generally provide the most value on work that is repeatable, time consuming and verifiable, with people retaining oversight and clear accountability. It flags higher risk uses in grants, procurement, regulation, financial decisions and services that affect people's rights or access.
See: AI Governance for Canadian Financial Advisors
The first agent specific principle is bounded autonomy. An agent should receive only the data, tools, permissions and authority required for its job. Ottawa recommends permission levels such as “draft only” and “read only,” along with data limits, rate limits, unique agent IDs and a clear indication of whether an agent is suggesting an action or actually carrying it out.
Actions that send, publish, approve, spend or update records should normally require human confirmation unless the expected impact is low and easy to reverse. Teams are also expected to test hostile inputs and realistic edge cases before granting wider permissions. Access can expand as the organization gains evidence that the controls work.
Agents Need Owners, Logs and Recovery Controls
Ottawa's second principle is recoverability. Organizations should be able to pause or stop an agent, return systems to a safe state and reconstruct what happened. The guide recommends logs the agent cannot alter, external pause controls and recovery plans for actions that can't simply be undone.
The guidance assumes agents, tools or credentials may eventually be compromised. Federal teams are told to preserve time stamped records, use previews and human approvals where appropriate, and plan for recovery before deployment. These controls become particularly important when an agent can change another system, spend money or trigger an action that can't be cleanly reversed.
See: AI Agents Gain Identity and Wallet Access
Every agent also needs a named human owner. Accountability stays with that person even when the agent acts autonomously inside approved permissions. If ownership becomes unclear, the agent should be paused or deactivated. When an employee changes roles or leaves, responsibility and access should be formally transferred or removed.
Ottawa also tells teams to watch for changes in quality and behaviour as tools, data and settings change. Spot checks, comparisons with human work and fresh risk assessments are recommended when permissions, data sources, scope or legal requirements change. Retiring an agent means removing its access, preserving required records and documenting what was learned.
Prompt injection gets specific attention because agents can read outside material and then act on other systems. Ottawa says emails, documents and user supplied content should be treated as data to analyse rather than instructions to follow automatically. An attacker who manipulates an agent's input becomes much more dangerous when that agent can also access accounts, update records or trigger transactions.
AI Agent Controls Are Becoming a Financial Buying Issue
The current AI transparency discussion paper asks whether organizations should disclose when agents are used, what actions they can take, how human oversight works and how responsibility can be traced when agents interact with one another. Ottawa also discusses detailed activity logs, digital identity credentials and tools that monitor agent behaviour, while noting that some of these approaches are still developing.
Canada currently does not have a regulatory framework specifically governing agentic AI. Existing consumer protection and civil liability rules can still apply when AI systems cause harm, while regulated firms already have obligations around privacy, security, records, supervision and operational risk. The consultation is asking for input on possible transparency measures, not announcing new private sector requirements.
For financial institutions, the buying questions already exist. A bank giving an agent access to customer records, payments, trading, underwriting or compliance systems will want to know whose identity it uses, exactly what it can access, which actions require approval, where its logs are stored and how quickly access can be shut off. Questrade's AI brokerage access offers a practical Canadian example of why permissions and customer approval become important once an agent reaches financial accounts.
Vendors also need credible answers on permissions, ownership, auditability, recovery and security. Narrow access can make early deployment easier, strong logs can simplify audits and investigations, and clear ownership reduces the risk of agents remaining active after staff or vendors change.
These controls also affect cost and adoption. Firms need people and systems to manage identities, permissions, testing, logs, incidents and retirement. NCFA's analysis of the cost of deploying AI shows why governance is becoming part of the commercial case for enterprise AI rather than a separate compliance exercise.
Talking Point
Canada is funding AI adoption while getting more specific about how autonomous systems should be controlled. For financial firms, the advantage will go to AI vendors that can prove who owns an agent, what it can do, what it did and how quickly it can be stopped.
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