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How Canadian Boards Are Bringing LLMs Into the Boardroom Safely

Large language models (LLMs) have quietly moved from innovation labs into Canadian boardrooms. Directors now see AI-generated summaries of board packs, suggested questions for management, and draft policy language before meetings start. The opportunity is clear, but so is the risk. Boards must balance efficiency gains with privacy, security, and regulatory expectations.

For Canadian directors, the real question is not whether to use LLMs, but how to introduce them safely and in line with fiduciary duties.

Why Canadian boards care about LLMs

LLMs can support better board oversight when used with care. Typical benefits include:

  • Short summaries of very long board documents

  • Quick comparisons of policy drafts or disclosures across reporting periods

  • Support for drafting charters, mandates, and stakeholder communications

  • Scenario ideas that enrich strategy discussions and risk conversations

At the same time, regulators and standard-setters are sharpening their views on AI. The OECD AI Principles, adopted by over 40 countries including Canada, emphasise trustworthy, human-centred AI with clear accountability and transparency. 

Boards that ignore LLMs risk falling behind competitors. Boards that adopt them without guardrails risk privacy breaches, litigation, and reputational damage.

Key risks of bringing LLMs into the boardroom

Before approving any AI initiative, Canadian boards usually focus on four risk areas.

1. Confidentiality and privacy

  • Sensitive financial data, non-public strategy, and deal terms entering external tools

  • Personal information covered by PIPEDA or provincial privacy laws

  • Prompts that inadvertently combine data from different business units or jurisdictions

2. Reliability and “hallucinations”

  • Confident but wrong answers embedded in board papers

  • Fabricated references in draft reports or ESG disclosures

  • Subtle errors that are hard to detect under time pressure

3. Accountability and documentation

  • Lack of a clear record of when AI was used and by whom

  • Unclear ownership when AI-assisted analysis misleads the board

  • Difficulty demonstrating due care to regulators or courts

4. Cybersecurity and third-party risk

  • Use of consumer AI tools that sit outside the organisation’s security perimeter

  • Weak contractual protections around data residency and incident response

  • Integration of AI tools into existing systems without proper testing

Governance foundations: policies, principles, and board education

Leading Canadian boards are treating LLMs as part of broader AI governance, not as isolated tools.

Common steps include:

  • AI and LLM usage policy
    A written policy that defines approved tools, banned tools, allowed use cases, and red lines. For example, no personal employee data and no undisclosed M&A documents may be entered into public models.

  • Use-case classification
    Many organisations classify LLM use into low, medium, and high-risk categories. Low-risk tasks might include simplifying public documents. High-risk tasks include decisions affecting capital allocation, legal interpretation, or people decisions.

  • Alignment with public guidance
    Boards and CIOs increasingly reference public frameworks. The Government of Canada’s guide on the use of generative AI sets out principles for responsible deployment in federal institutions, including human oversight, transparency, and risk assessment. 

  • Director education
    Short, focused teach-ins help directors understand both the strengths and limits of LLMs. Some boards run table-top exercises where they review AI-generated content alongside human drafts to compare quality and risk.

Data protection and Canadian regulatory context

LLM adoption sits at the intersection of privacy, cybersecurity, and disclosure.

Boards should expect clear answers from management on questions such as:

  • Which privacy laws apply to our use of LLMs (PIPEDA, provincial rules, and sector-specific regulation)?

  • Where is data stored and processed, and are cross-border transfers properly governed?

  • How do existing cyber policies, incident response plans, and disclosure practices apply when an AI-related failure occurs?

Canadian regulators have already issued expectations around cyber risk and disclosure. Guidance on cyber risk oversight and incident reporting can often be adapted to cover AI-related failures, because the underlying questions about governance, controls, and accountability are similar. (MLT Aikins)

Practical use cases that make sense for boards

Most Canadian boards are starting in low-risk, high-utility areas and gradually expanding.

Examples include:

  • Board pack support

    • Summarising long management reports for committee chairs

    • Highlighting key changes between this quarter’s and last quarter’s materials

  • Drafting assistance

    • First drafts of charters, policies, or briefing notes that are then revised by legal and compliance teams

    • Draft stakeholder letters or FAQs that IR and communications refine

  • Risk and strategy enrichment

    • Generating alternative scenarios or questions for strategy offsites

    • Listing potential second-order effects of regulatory changes or technology trends

In every case, human judgement remains central. The board’s role is to ensure that LLMs support, rather than substitute, the exercise of independent oversight.

Secure platforms and the role of board technology

To reduce risk, many organisations prefer to experiment with LLMs inside secure governance platforms instead of copy-pasting data into public tools.

When assessing technology, boards often ask whether:

  • AI features run in a private environment with strong encryption

  • Prompts and documents are excluded from training public models

  • There are detailed audit trails of who used AI features and for what

  • AI capabilities can be turned on or off at role, committee, or entity level

Specialist governance platforms such as board-room and other board software providers can help centralise board materials, permissions, and security settings, which makes it easier to apply consistent rules to any AI features that are introduced.

Questions Canadian directors should ask about LLMs

To fulfil their oversight role, directors can use a simple set of questions:

  • What are the top LLM use cases we plan to support this year?

  • Which data categories are strictly prohibited from entering any LLM?

  • How are we aligning with frameworks such as the OECD AI Principles in our AI strategy? 

  • What contractual protections and technical controls are in place with AI vendors?

  • How will AI usage and incidents be reported to the board or risk committee?

  • What training will management and directors receive to keep skills current?

The path forward: disciplined experimentation

Canadian boards cannot ignore LLMs, but they also cannot treat them as ordinary productivity tools. The safest path is disciplined experimentation: clear policies, careful choice of platforms, close alignment with public guidance, and regular review of what is working and what is not.

Handled this way, LLMs become another instrument in the governance toolkit. They give Canadian boards better information, more time for strategic discussion, and a deeper view of emerging risks, without losing sight of the fundamental duties of care, loyalty, and accountability.