Shadow AI is a registry problem
Why teams need an AI system inventory before regulators ask — and what to log without creating another spreadsheet graveyard.
Why teams need an AI system inventory before regulators ask — and what to log without creating another spreadsheet graveyard.
You cannot govern AI systems you cannot name. That is the shadow AI problem: teams adopt assistants, automation tools, document processors, browser extensions, agents, and SaaS copilots faster than governance teams can inventory them.
A spreadsheet is a start, but it is not a registry. A useful AI system registry is live, owned, searchable, and exportable.
Every entry should answer practical questions:
The registry should cover formal systems and shadow systems. Otherwise, the highest-risk tools may be exactly the ones nobody has reviewed.
Discovery should combine procurement data, SSO logs, network signals, endpoint telemetry, finance spend, browser extensions, and interviews with business teams. Each system then moves through a lifecycle: discovered, profiled, classified, documented, approved, monitored, and retired.
Do not make legal or compliance fill every field manually. Product owners should provide context. Security should enrich access and data-flow signals. Engineering should expose telemetry and model dependencies. Compliance should own classification and approval criteria.
A regulator-ready registry can export structured data on demand. It should include system name, purpose, owner, vendor, risk level, deployment domain, data categories, model/provider, documentation status, oversight status, incident history, and review date.
The goal is not bureaucracy. It is operational clarity. When a regulator, client, or board asks “where are we using AI?”, the answer should take minutes, not weeks.
Further reading: AI system registry guidance and AI Act compliance for agentic platforms.