AI Policy & Governance Framework

Why AI Governance Cannot Be Deferred

Organisations that deploy AI tools without governance frameworks face predictable problems: staff use AI in ways that create legal or reputational risk, senior leaders discover AI-assisted work after the fact and have no way to assess whether it was appropriate, and when something goes wrong there is no defined process to respond.

The window to establish governance before problems occur is short. Once AI tools are in widespread use, retrofitting governance is harder than establishing it from the outset — because it requires changing behaviour that has already formed.

What We Develop

Acceptable Use Policy. A clear, practical document that tells staff what they may do with AI tools, what they may not do, and what they must do when using AI in their work — including disclosure requirements, verification obligations, and prohibited use cases. Written in plain language, not legal boilerplate.

Risk Classification System. A framework that categorises AI use cases by the potential impact of an error or misuse — from low-risk drafting assistance to high-risk automated decision-making affecting individuals. Each risk tier carries different requirements for human oversight, approval, documentation, and review.

AI Deployment Approval Process. A structured process for evaluating and approving new AI tools or AI use cases before they are deployed. This includes assessment criteria, the roles responsible for approval decisions, and documentation requirements. The process is scaled to risk level — simple approvals for low-risk tools, more rigorous assessment for high-impact applications.

Roles and Accountability Structure. Clear assignment of AI governance responsibilities — who is accountable for AI policy compliance, who reviews AI incidents, who approves new AI use cases, and who is responsible for keeping the governance framework current. For larger organisations, we recommend an AI governance committee with defined membership and terms of reference.

Incident Response Procedures. A defined process for identifying, escalating, and responding to AI incidents — cases where an AI system produces harmful, erroneous, or unexpected output that affects operations or stakeholders. The absence of incident response procedures means organisations improvise when something goes wrong, typically not well.

The Canadian Regulatory Landscape

Canada's approach to AI governance is evolving rapidly. The Artificial Intelligence and Data Act, introduced as part of Bill C-27, will establish mandatory requirements for high-impact AI systems once enacted. The Privacy Commissioner has published guidance on AI and the Privacy Act and PIPEDA. The Treasury Board Secretariat has issued directives governing AI in federal departments.

We design governance frameworks that account for this landscape — not just current requirements, but the direction of regulatory travel. Organisations that build governance now, calibrated to anticipated regulatory requirements, will be better positioned when AIDA and subsequent regulations come into force than those that wait.

The Legal Sector Context

Law firms and in-house legal departments using AI face obligations that go beyond general privacy and AI governance requirements. Professional conduct rules impose obligations on competence, supervision of work, and confidentiality that apply to AI-assisted legal work. We develop governance frameworks for legal organisations that address these professional obligations specifically — including guidance on supervising AI output, disclosing AI use to clients, and managing privilege considerations when AI tools access confidential information.

Governance That Works in Practice

We are not in the business of producing governance documentation that sits on an intranet. Every framework we develop includes an implementation plan: how to communicate the policy to staff, how to train managers on their governance responsibilities, how to build compliance mechanisms into existing workflows, and how to measure whether the governance is working. A governance framework is only as valuable as the behaviours it produces.

Delivery Process

Step 1: Current State Assessment (Weeks 1–2). We review any existing AI-related policies, acceptable use guidelines, IT governance frameworks, and risk management structures. We interview executives, legal counsel, HR, IT, and a cross-section of frontline staff to understand how AI tools are currently being used, what governance gaps are creating risk, and what constraints the governance framework must work within. For regulated organizations, we map the applicable regulatory requirements at this stage.

Step 2: Framework Design (Weeks 2–4). We develop the governance framework components in sequence: first the risk classification system (which defines the tiers that everything else references), then the acceptable use policy, then the deployment approval process, then the roles and accountability structure, and finally the incident response procedures. We present each component to your designated review group before finalizing, ensuring the framework reflects your organization's specific context rather than generic best practice.

Step 3: Stakeholder Review and Refinement (Weeks 4–5). We circulate the draft framework to legal, HR, IT, and representative operational stakeholders for review. We facilitate a structured review session to work through substantive feedback and finalize the documentation. For organizations with union environments or collective agreement implications, we ensure that review includes appropriate consultation.

Step 4: Implementation Planning and Activation (Weeks 5–6). We develop the implementation plan — how to communicate the framework to staff, how to integrate compliance requirements into existing workflows, how to train managers, and how to measure whether the governance is working. We support the initial activation, including staff communication drafting and manager briefing sessions.

Typical Engagement

Duration: 6–10 weeks from initial assessment to approved, implemented governance framework for a medium-sized organization. Larger organizations with complex governance structures, multiple regulated subsidiaries, or significant union consultation requirements may require 12–16 weeks.

What the client needs to provide: Access to legal, HR, IT, compliance, and operational stakeholders for interviews and review sessions; executive sponsor with authority to approve the framework; existing policy documentation for review; designated policy owner who will maintain the framework going forward.

What Remolda provides: Current state assessment, full framework drafting, stakeholder facilitation, regulatory mapping, implementation plan, manager briefing materials, staff communication drafts, and initial activation support. We also provide a 6-month review cycle design so the framework stays current as your AI environment evolves.

Technology & Integrations

An AI governance framework must be implemented in the systems and processes your organization actually uses — not as a standalone document. We design governance workflows into your existing platforms: AI deployment approval processes integrated into your IT service management system (ServiceNow, Jira Service Management, or your government equivalent); acceptable use acknowledgement workflows in your HR system or learning management platform; incident reporting workflows in your existing case management or ticketing system; and audit log review processes integrated with your SIEM and access management infrastructure. For government organizations using GC Risk Management tools, we align framework components with existing Treasury Board-mandated risk management processes to reduce duplication and increase adoption by connecting AI governance to familiar frameworks rather than creating a separate governance bureaucracy.

Canadian Regulatory Context

Canada's AI governance landscape is the most active regulatory environment in which Canadian organizations operate today. The Artificial Intelligence and Data Act (AIDA), as part of Bill C-27, will introduce mandatory requirements for organizations developing or deploying high-impact AI systems — including mandatory risk assessments, risk mitigation obligations, monitoring requirements, and incident reporting with potential penalties up to 3% of global revenue for the most serious violations. We design governance frameworks with AIDA's anticipated requirements explicitly addressed, so organizations are not rebuilding from scratch when the legislation comes into force. The Privacy Commissioner of Canada has published updated guidance (2024) on AI and PIPEDA, clarifying that automated decision-making, profiling, and repurposing of personal information for AI training are areas of active enforcement focus. Quebec's Law 25, in force since 2023, adds requirements that directly affect AI governance including mandatory privacy impact assessments for profiling, mandatory transparency notices for automated decision-making, and new individual rights to contest automated decisions. For federal departments, the Treasury Board Secretariat's Directive on Automated Decision-Making continues to evolve, with regular guidance updates that require governance frameworks to be maintained as living documents rather than set-and-forget policy artifacts.

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Further reading: AI Governance: Before Tools | AI Bill C-27 Canada Compliance