KM is where sovereignty risk is either controlled or amplified

When legal leaders talk about sovereignty, the first question is often about the AI model: Is it hosted in Canada? Is it a U.S. provider? Is it public or private?

Those questions matter, but they’re no longer sufficient. In practice, sovereignty risk is far more often created by the plumbing underneath legal work:

• Where documents are stored
• How they’re indexed and retrieved
• Whether search and AI tools respect ethical walls and matter-level security
• Whether lawyers are copying privileged text into tools never designed for legal confidentiality
• Whether the firm can reconstruct what happened later, through logs, audit trails, and retention records

In other words, sovereignty is shaped by knowledge pathways, not press releases. A firm can proudly announce that it uses a “secure, enterprise AI platform”, and still quietly expose itself through everyday KM workflows that were never designed with cross-border risk in mind.

Why this is uniquely acute for Canadian firms

Canada’s legal market sits in a particularly exposed position. Canadian firms routinely handle matters involving:

• U.S. counterparties
• cross-border transactions
• multinational investigations
• regulators operating under different privacy regimes

At the same time, Canadian clients , especially in regulated sectors, are becoming far more explicit about where their data can live, who can touch it, and how it can be processed.

This aligns closely with how the Government of Canada itself frames digital sovereignty: not as isolation, but as the ability to manage and protect data, systems, and infrastructure in a globally connected environment. That framing maps cleanly onto legal work. Law firms are not just service providers; they are custodians of some of the most sensitive commercial, regulatory, and litigation information in the economy.

Which means sovereignty decisions can’t be deferred to IT alone.

The three decisions firms can no longer avoid

Canadian legal organizations need to make, and clearly articulate, three sets of decisions. Avoiding them doesn’t reduce risk; it just pushes risk into ungoverned corners of the organization.

1. Where knowledge lives:

Firms need explicit positions on: what must remain in Canada (or within defined jurisdictions), what may be processed cross-border but not stored, and what must never be sent to third-party systems, including “free” or consumer-grade AI tools

2. How knowledge moves

Firms need clarity on how knowledge is allowed to move across systems, including:

• documents are exported into external tools for drafting or analysis
• privileged passages are pasted into public chat interfaces
• internal work product is shared through systems that don’t align with matter-level security

KM leaders can’t control every individual action, but they can design systems that make the safe path the easy path.

That usually means:

• integrating AI and search tools directly into the DMS instead of relying on copy-paste
• using permission-aware retrieval so tools respect ethical walls automatically
• requiring provenance and citations so outputs can be verified and defended

3. Who owns enforcement

Firms need to define clear ownership across KM (knowledge architecture, content standards, retrieval rules), IT (system integration, access controls, logging), Risk / Privacy (policy, escalation, audit requirements), and Practice leadership (behavioural enforcement).

The quiet battleground: vendor data rights

Here’s the part many organizations underestimate: vendor contracts are now sovereignty instruments. Even where vendors emphasize security in marketing, the fine print can quietly reshape control:

• rights to use customer data for model improvement
• retention periods that outlive the matter
• telemetry collection that reveals sensitive usage patterns
• subcontractor chains that extend cross-border exposure

None of this is inherently malicious, but all of it has consequences.

This is why AI governance is more than ethics discussion. It’s procurement discipline, contract standardization, and architectural design. And it’s why “audit-ready safeguards” are becoming non-negotiable. Courts have already signalled that unverified AI use and fabricated citations carry real consequences. Scrutiny is moving closer to everyday legal practice, not further away.

Why sovereignty is now a KM problem

The old mental model treated data sovereignty as an external constraint, something imposed on legal work.

The new reality is different.

Sovereignty is produced internally, every day, by how knowledge is captured, stored, retrieved, transformed, and reused.

That makes KM the place where sovereignty risk is either designed out, or quietly scaled.

And that’s why data sovereignty is no longer a footnote. It’s a KM design requirement.