Shadow AI Intelligence

Define the observation before the label#

Shadow AI refers here to potential AI use outside an organization's approved operating model. Establishing it requires more than seeing an unfamiliar destination: the organization needs a reviewed source, a reliable attribution method and a definition of approved use.

LSAS Shadow AI Intelligence remains In Development. Current Gateway records show requests passing through supported integrations. They cannot discover every tool used by employees or prove that an unobserved tool is absent.

Source quality determines the conclusion#

Potential integrations can provide different evidence: an application inventory, identity records, network observations or provider administration data. Each has its own coverage, delay and false-attribution risks. An IP address may identify a shared egress point rather than a person; a service record may identify a team account rather than the accountable owner.

A useful observation should retain source, time, identity basis, confidence and known gaps. Human review should decide whether it indicates approved, unresolved or prohibited use. Do not automatically convert a weak inference into an enforcement action.

Do not confuse source assessment with tool discovery#

The Discovery Scanner evaluates approved files and separately configured sources for supported findings. It does not scan an organization's entire network for AI products, and it does not supply a complete application inventory. Assessment findings and Shadow AI observations have different collection and qualification requirements.

A bounded first study#

Agree the source population, access permissions, retention and review process. Measure attribution quality against a reviewed sample and report unavailable areas explicitly. No current product promise establishes an organization-wide discovery rate or a count of incidents prevented.