Putting an owner against every AI system in a bank

A financial institution in Canada had teams adopting AI across cloud infrastructure, internal applications, repositories and external model services.

The existing security and asset tools captured parts of the estate but gave no single operating model for AI. That left basic accountability questions unanswered: which systems were active, which business or technical owner was responsible for each, what information each could reach, and what risks were still open.

An AI security and posture system built around the institution's environment, combining discovery, ownership, posture assessment, access visibility, runtime control and remediation tracking in one place.

We built it for the institution's internal use, so AI could be managed as an operating environment rather than a collection of separate pilots.

• Discovers AI systems across the platforms the institution already runs
• Puts a named business or technical owner against each system
• Shows what information each system can reach
• Records which systems have completed review and which have not
• Tracks open risks through to remediation

Every AI system got a named owner, and the security team had one register to see them all in.

Before that, accountability was spread across teams and tools, so establishing who was responsible for a given system took a round of asking.

The first release did not cover the whole estate. Formal intake, deeper access-governance depth and several platform integrations were left for a later release rather than shipped half-built.