AI Oversight in Cybersecurity: Preparing for Canada’s Upcoming AI Regulations
Bill C-27 may be paused, but AI governance isn’t waiting.
Here is how cybersecurity and compliance leaders can prepare for Canada’s AI and Data Act starting now.
The Regulatory Pause That Isn’t a Pause
Let’s be honest about where we stand.
In January 2025, Parliament was prorogued. All bills on the Order Paper including Bill C-27, which housed Canada’s proposed
Artificial Intelligence and Data Act (AIDA) died. An election followed. New legislation will need to be reintroduced from scratch.
“Great. AI regulation is on hold. We can focus on other priorities.”
That would be a mistake.
Why This Still Matters
| Factor | Implication |
|---|---|
| International momentum | EU AI Act is in force. Colorado’s AI Act is coming. US NIST AI RMF is established. Canada will need to align. |
| OSFI is moving | Federally regulated financial institutions already have AI risk management expectations effective May 1, 2027. |
| Provincial activity | Provincial regulators are issuing AI guidance and privacy interpretations in parallel. |
| Market pressure | Enterprise customers are asking about AI governance now—not when laws pass. |
| AIDA’s substance | The core requirements are known. Waiting for passage means falling behind. |
The smart play: treat the current period as a runway, not a vacation.
The organizations that build foundational AI governance now will be ready the day new legislation lands.
What AIDA Proposed (And Why It Still Matters)
Bill C-27’s Artificial Intelligence and Data Act would have established Canada’s first comprehensive AI regulatory framework.
While the specific bill died, its structure reflects where policy is heading and aligns with international trends.
The Core Requirements
| Requirement | Who It Applies To | What It Means |
|---|---|---|
| Manage anonymized data | Anyone responsible for AI systems | Data governance isn’t optional |
| Conduct impact assessments | “High-impact” systems | Determine if AI poses meaningful risk |
| Maintain compliance records | All AI operators | Prove what you did and why |
| Develop mitigation plans | High-impact systems | Have a plan to address identified risks |
| Monitor mitigation measures | High-impact systems | Ongoing oversight—not one-and-done |
| Public disclosure | High-impact systems | Plain-language description of use, outputs, mitigations |
| Notify of material harm | All, if harm occurs | Report when things go wrong |
The Penalty Structure That Got Everyone’s Attention
| Offence Type | Maximum Penalty |
|---|---|
| Contravention of requirements (corporation) | $10 million or 3% of global revenue |
| Serious AI-related offences (corporation) | $25 million or 5% of global revenue |
| Individual penalties | Discretionary fines + up to 5 years imprisonment |
The Transparency Debate
One of the most contentious aspects of AIDA was public disclosure for high-impact systems.
The likely outcome: Canada will try to balance transparency with legitimate confidentiality concerns.
But some form of public accountability is almost certain to remain.
The International Context: Canada Is Not an Island
Canadian AI regulation will not develop in a vacuum. Understanding global movement helps you predict direction and prepare for overlap.
United States: NIST AI RMF and Colorado
The NIST AI Risk Management Framework organizes AI governance around four core functions:
| Function | Purpose |
|---|---|
| Govern | Set ground rules, assign accountability, establish policies |
| Map | Understand context, intended uses, data sources, potential misuse |
| Measure | Evaluate fairness, accuracy, explainability, security |
| Manage | Monitor, mitigate, improve continuously |
OSFI: Canada’s First Mover
Even without AIDA, AI governance requirements are already landing in regulated sectors.
OSFI’s model risk guidance sets expectations for lifecycle testing and monitoring.
| Requirement | Timeline |
|---|---|
| Ongoing testing, monitoring, review throughout lifecycle | Effective May 1, 2027 |
| Engagement throughout full model lifecycle | Expected by OSFI |
What “High-Impact” Could Mean
AIDA hinges on “high-impact” AI systems. While the final definition would come through regulation, the logic is clear:
focus strict requirements where harm is plausible.
| Factor | Examples |
|---|---|
| Scale of use | Systems affecting large numbers of people |
| Nature of decisions | Employment, credit, housing, healthcare, law enforcement |
| Potential for harm | Economic loss, discrimination, safety impacts |
| Autonomy | Level of human oversight |
| Data sensitivity | Personal or sensitive information |
The Governance Framework: What to Build Now
1) Inventory Your AI Systems
You cannot govern what you don’t know exists. Start by adding AI systems to your asset inventory especially vendor-provided AI features.
| Action | Why It Matters |
|---|---|
| Identify all AI systems in use | Shadow AI is a compliance risk |
| Document purpose, scope, data sources | Required for impact assessment |
| Classify by risk level | Focus effort where it matters |
| Track vendor AI | You’re responsible even if you didn’t build it |
2) Establish Governance Structures
| Element | What to Implement |
|---|---|
| Accountability | Assign an AI governance lead (often CISO/Privacy) |
| Policies | AI development, procurement, risk management policies |
| Oversight | AI review board/committee for high-risk systems |
| Documentation | Keep records of decisions, tests, mitigations |
3) Conduct Impact Assessments
For each system (especially high-risk), document intended use, potential misuse, data sources, bias risks, transparency, and mitigations.
Bias testing isn’t optional treat it like security testing.
4) Implement Continuous Monitoring
AI systems drift. They get used in new ways. Build monitoring for performance, output quality, user behavior, and incidents then track changes.
5) Prepare for Transparency
Disclosure requirements may evolve, but transparency is coming. Prepare plain-language descriptions of system purpose, outputs, and mitigations.
Separate “public summary” from “sensitive technical detail.”
The Vendor Question: AI You Didn’t Build
Many organizations use vendor AI features or external AI APIs. You’re still responsible for how they’re used with your data.
Update your vendor assessment process to include AI-specific questions.
| Vendor Question | Why Ask |
|---|---|
| “What data does your AI train on?” | Avoid your data being used to train models without consent |
| “Where is data processed and stored?” | Data residency and cross-border risk |
| “Do you conduct bias testing?” | You need evidence, not promises |
| “Can you explain decisions/outputs?” | Transparency obligations flow through vendors |
| “What happens to data after processing?” | Retention, deletion, secondary use |
| “Do you have independent audits?” | Proof of controls |
The Opportunity: Competitive Advantage
Enterprise customers are already asking how you use AI with their data, what governance exists, and whether you can prove it.
Teams with answers win deals.
| Mindset | Outcome |
|---|---|
| Reactive | Scrambling when laws pass |
| Compliant | Meeting minimum requirements |
| Strategic | Differentiating with trust + proof |
The 12-Month AI Governance Roadmap
| Quarter | Focus | Key Activities |
|---|---|---|
| Q1 | Inventory | Identify AI systems, classify by risk, assign owners |
| Q2 | Foundation | Governance structure, draft policies, assessment framework |
| Q3 | Assessment | Impact assessments for high-risk systems, bias testing |
| Q4 | Monitoring | Continuous monitoring, documentation, transparency prep |
How Canadian Cyber Helps
Canadian Cyber’s SharePoint ISMS platform gives you the structure to build AI governance that lasts inventory, assessments, workflows, and evidence trails.
| Feature | How It Helps |
|---|---|
| AI systems register | Metadata for purpose, risk, data sources, owner |
| Vendor management | Track AI vendors, assessments, due diligence evidence |
| Risk register | AI-specific risks, mitigations, monitoring |
| Policy templates | AI governance, procurement, testing framework |
| Impact assessment workflows | Automated routing, approvals, evidence storage |
| Audit trails | Regulator- and customer-ready documentation |
“We had AI systems scattered across departments. Now we have one inventory, one assessment process, and one source of truth for auditors and customers.”
— CISO, Canadian Financial Services Firm
The 15-Minute AI Governance Assessment
We’ll review your AI systems, governance practices, and compliance gaps and tell you exactly what to do next.
The Question Every Leader Must Answer
“If a customer asked today how we govern our AI systems, could I give them a confident answer?”
If the answer is “no” or “I’d have to check,” you have work to do. The good news: you have runway. Use it.
Conclusion: From Uncertainty to Action
Canadian AI regulation is uncertain in timing but clear in direction: impact assessments, mitigation, transparency, and accountability.
You can wait or build now.
- Know what AI you use
- Assess its risks
- Mitigate what you find
- Monitor continuously
- Document everything
About the Author
Canadian Cyber helps organizations build compliance programs that are ready for whatever comes next.
We don’t guess at regulations we build foundations that work regardless of final form.
