Large language models have moved from novelty to everyday tools inside Canadian organisations. Management teams are using them to draft reports, analyse documents and support customer communication. For boards, this raises a straightforward question: where do LLMs actually fit in governance, and what does good oversight look like in the Canadian market?
This overview sets out practical use cases, explains the risk and regulatory context and suggests questions directors can ask to keep control over how LLMs are deployed.
Why LLMs belong on the board agenda
LLMs matter for Canadian boards for three main reasons.
First, they change how information is produced and consumed in the organisation. Board packs, management reports and briefing notes may already be written with the help of AI. That affects how directors interpret quality, bias and completeness.
Second, LLMs frequently touch sensitive data. They may process customer, employee, health or financial information, which triggers duties under Canadian privacy and sector specific laws. The Office of the Privacy Commissioner has already set out principles for responsible, privacy protective use of generative AI, including limits on purpose, transparency and accountability.
Third, regulators are paying close attention. Recent Canadian commentary highlights how securities regulators, privacy authorities and sector regulators are updating guidance to reflect AI use in financial services, health and other industries. Ignoring AI in this environment is becoming difficult to defend.
Where LLMs can support board governance
The most useful way to think about LLMs is as assistive tools that sit around, not inside, core board decisions. They can streamline work and improve insight, as long as directors keep human judgement at the centre.
Typical use cases include:
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Board information and preparation
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Drafting and refining board papers and committee reports.
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Summarising long documents such as regulatory filings, audit reports or transaction data rooms.
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Highlighting key changes between policy versions or contract drafts.
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Oversight and risk analysis
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Producing first draft risk heat maps or scenario narratives that management then validates.
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Collating public information on peers, regulatory actions or emerging risks to inform strategy sessions.
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Stakeholder engagement
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Assisting with first drafts of shareholder letters, ESG narratives and Q&A documents.
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Supporting analysis of stakeholder feedback from surveys or consultations.
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Board efficiency and administration
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Generating draft minutes, action lists and follow up trackers for refinement by the corporate secretary.
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Helping to compare charters, committee mandates and governance policies across entities.
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In all of these areas, LLMs should be treated as accelerators, not decision makers. Management remains responsible for the accuracy and completeness of the information that reaches the board.
Key risk themes for Canadian directors
Alongside the benefits, LLMs introduce specific risks that boards need to understand.
1. Privacy and data protection
Using personal data in prompts or training data can trigger obligations under federal and provincial privacy laws. The federal privacy regulator has emphasised principles such as necessity, proportionality and transparency for generative AI use. Boards should confirm that LLM projects follow these principles and that staff are trained not to paste sensitive information into uncontrolled tools.
2. Accuracy, bias and reliability
LLMs are known for fluent but occasionally wrong outputs. When used in risk reporting or external communication, this can create misleading statements, biased analysis or gaps in disclosure. Directors should insist on clear validation processes and audit trails that show how LLM outputs were checked before being used in board materials.
3. Model governance and third party risk
Many organisations rely on external LLM providers. This adds contractual, security and resilience questions to existing vendor management frameworks. Recent analysis of Canadian regulatory trends stresses that market participants remain responsible for outcomes even when they rely on third party AI systems.
4. Cybersecurity and information leakage
Public LLM interfaces can become an unintentional channel for leaking confidential deal terms, strategy documents or legal advice. Boards should ask whether staff have clear rules on which tools are permitted and how prompts are monitored or logged.
The emerging Canadian regulatory picture
There is no single, comprehensive AI law in force in Canada yet, but expectations are tightening. Key developments include:
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The proposed Artificial Intelligence and Data Act, which would regulate high impact AI systems and introduce duties around risk management and incident reporting.
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Guidance from the Office of the Privacy Commissioner on responsible generative AI, which already shapes how privacy compliance is assessed.
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Sector specific moves, such as securities regulators spelling out how technology neutral laws apply to AI systems in capital markets and calling for robust governance and risk management. (Bennett Jones)
For boards, the practical message is that LLMs should be treated as part of mainstream risk and compliance, not as experimental tools sitting outside normal rules.
Practical questions for boards to ask about LLMs
To locate LLMs correctly within governance, Canadian directors can build a simple set of questions into their regular dialogue with management:
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Where are LLMs currently used in our organisation, and which data sets do they touch?
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Which board and committee materials are drafted or summarised with LLM support, and how is accuracy checked?
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How do our privacy, security and model risk policies apply to LLMs, including public tools used by staff?
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Which regulators are most relevant to our use of AI, and how are we tracking new guidance or enforcement trends?
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Do we have the right skills on the board and in management to challenge LLM related proposals?
These questions help the board move from abstract discussion about AI to concrete oversight of real systems and decisions.
Role of technology platforms in AI informed governance
As LLM use grows, the volume of digital material moving through the boardroom also increases. Board collaboration platforms, including solutions such as board-room, can centralise AI related policies, training materials and risk reports so directors work from a controlled, consistent environment.
Used well, these platforms can:
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Provide a secure space for AI generated drafts, board packs and evaluations.
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Help track which documents have been reviewed and approved by human owners.
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Support version control when policies on AI, cybersecurity and privacy are updated.
The platform is only one part of the answer, but it makes it easier to embed LLM governance into day to day board practice.
Positioning LLMs within the broader governance agenda
LLMs are one piece of a wider shift toward data driven governance. They sit alongside analytics, automation and digital reporting tools that are already reshaping how boards see their organisations.
Canadian boards that succeed with LLMs tend to treat them as:
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An extension of existing digital and data strategies.
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A driver of better information quality, not a shortcut that replaces judgement.
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A source of new risk that needs structured oversight and clear accountability.
By framing LLMs this way, directors can keep their focus on long term value creation and resilience. The goal is not to chase every new AI feature. The goal is to ensure that when LLMs are used in or around the boardroom, they support sound governance, comply with Canadian law and contribute to a clearer, more informed view of the business.