Shadow AI Governance: Closing the Gap IT Never Approved

Shadow AI refers to AI tools, models, and agents connected to organizational data or systems without formal IT or security approval, creating risks beyond traditional shadow IT because such tools can read, alter, and trigger actions involving data and business processes. The passage argues that identity and access management systems can identify permissions but generally cannot show what an AI agent actually reads, writes, or affects downstream, making code-level data lineage and mapping necessary for effective governance. It cites September 2026 survey findings that 72% of healthcare organizations have unapproved AI tools or agents and that only 4% consider their AI governance mature, presenting healthcare as an example of a broader regulated-industry challenge. Foundational is presented as a platform that analyzes source code, applications, pipelines, integrations, and other data-touching systems to create a deterministic map of AI data access and dependencies, which it says can support compliance, audits, and trustworthy AI deployment.

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