by Maple & North
Opening Hook
Organizations spend significant time understanding their dependencies on cloud providers, telecommunications networks, software vendors, and critical suppliers.
Artificial intelligence should be treated no differently.
As AI becomes embedded in business operations, security workflows, software development, customer service, and decision support systems, organizations are creating new dependencies that are often poorly understood and rarely documented.
The risk is not that organizations use AI.
The risk is that critical workflows become dependent on tools, models, platforms, or autonomous agents whose availability and authority sit outside the organization's control.
You cannot manage AI concentration risk until you know where those dependencies exist.
The Visibility Problem
Most organizations cannot answer a simple question:
Where does AI influence critical business decisions today?
The answer is often broader than leaders expect.
AI capabilities increasingly appear in:
- Security operations.
- Software development platforms.
- Productivity tools.
- Customer service workflows.
- Threat intelligence analysis.
- Identity and access management.
- Cloud administration.
- Financial and operational reporting.
In many cases, these capabilities were adopted incrementally.
A team begins using AI to accelerate work.
Another integrates AI into a workflow.
A vendor adds AI functionality into an existing platform.
Over time, dependency develops without formal governance.
Organizations may not realize how much operational reliance exists until a disruption occurs.
Why This Matters for Canada
Canada's AI strategy emphasizes responsible adoption, innovation, and long-term competitiveness.
Those goals depend on trust, resilience, and governance.
Canadian organizations increasingly operate within a global technology ecosystem where AI capabilities may be developed, hosted, regulated, and controlled across multiple jurisdictions.
That reality creates both opportunity and risk.
The opportunity comes from access to world-class capabilities.
The risk comes from concentration.
If multiple critical functions depend on the same provider, platform, or jurisdiction, a single disruption can affect numerous business processes simultaneously.
Understanding those dependencies is becoming a governance requirement rather than a technical exercise.
Building an AI Dependency Map
The purpose of an AI dependency map is not to eliminate risk.
The purpose is to make risk visible.
Every organization should begin by identifying:
What AI Services Are Being Used?
Many organizations discover AI adoption is broader than expected.
Inventory:
- External AI platforms.
- Embedded AI features within existing products.
- Internal AI applications.
- Autonomous agents.
- AI-powered automation workflows.
What Business Processes Depend on Them?
For each capability, determine:
- Which teams use it.
- What decisions it influences.
- Whether operations can continue without it.
- How long the organization can tolerate an outage.
Who Controls the Capability?
For each dependency, document:
- Provider.
- Hosting location.
- Jurisdiction.
- Contractual terms.
- Data ownership considerations.
- Alternative options.
What Happens If It Disappears?
This is the most important question.
If access is lost:
- Does the process stop?
- Does it slow down?
- Is there a manual alternative?
- Is another provider available?
- Can a local model provide degraded capability?
An untested fallback is not a fallback.
A Practical Classification Model
Maple and North recommend classifying AI dependencies into four categories:
Non-Critical
AI improves efficiency but operations continue normally without it.
Examples:
- Draft generation.
- Meeting summaries.
- Internal research assistance.
Important
AI significantly accelerates work but manual alternatives exist.
Examples:
- Threat intelligence summaries.
- Vulnerability prioritization.
- Software development assistance.
Critical
Operations become materially impaired if the capability becomes unavailable.
Examples:
- Security monitoring workflows.
- Customer-facing automation.
- Fraud detection systems.
- Automated investigation pipelines.
Systemic
Multiple critical functions depend on the same provider or platform.
Examples:
- A single AI platform supporting security, engineering, customer service, and business operations simultaneously.
Systemic dependencies deserve the highest level of governance and oversight.
The Cybersecurity Impact
AI dependency is increasingly becoming a cybersecurity issue.
As organizations grant AI systems access to sensitive data, security tooling, cloud environments, and operational workflows, availability becomes part of the security conversation.
Questions leaders should consider include:
- What AI systems have privileged access?
- Which systems influence security decisions?
- Which providers process sensitive organizational data?
- How are AI-generated actions reviewed and approved?
- What happens during an outage?
The answers will vary across organizations, but the exercise itself is becoming increasingly important.
What Leaders Should Do
Security and technology leaders should treat AI systems and autonomous agents as governed identities rather than informal tools.
Practical steps include:
- Inventory critical AI dependencies.
- Assign accountable owners to AI-enabled workflows.
- Document access controls and approval boundaries.
- Track provider concentration risk.
- Evaluate jurisdictional and regulatory exposure.
- Test fallback and continuity procedures.
- Review AI dependencies during procurement processes.
- Reassess dependencies annually.
The objective is not to eliminate every dependency.
The objective is to understand which dependencies matter most.
What Not to Overstate
This is not an argument that every AI service requires the same level of oversight.
Nor is it an argument that organizations should avoid external AI providers.
Many organizations will continue to rely on global AI platforms because of their capabilities, scale, and innovation.
The challenge is not dependency itself.
The challenge is unmanaged dependency.
Closing
Organizations cannot govern what they cannot see.
As AI becomes embedded in operational processes, dependency mapping will become as important as asset inventories, supplier assessments, and business continuity planning.
The durable lesson is not that Canada must own every capability.
It is that resilience depends on understanding where critical dependencies exist, who controls them, and what happens when those capabilities are no longer available.