Shadow AI and the Governance Vacuum: Confronting the Next Phase of Digital Trust Risk

Shadow AI is the use of AI tools, often public generative apps without an organization’s approval or oversight, typically when employees turn to convenient services to move faster at work. Because these tools operate outside IT’s visibility, they create hidden data flows and governance gaps that can lead to leakage, non-compliance, and reputational harm. Unlike general “shadow IT,” the risk here is uniquely AI-shaped: models ingest and learn from sensitive inputs, generate outputs that can influence decisions, and often leave no clear audit trail. The real challenge for 2025 is not adoption but governance, regaining visibility into which models are in use, who owns them, and how they’re monitored so organizations can balance innovation with accountability.

The governance vacuum in modern enterprises

In many organizations, the pace of AI experimentation has outrun the ability to oversee it. Employees reach for public generative tools to summarize reports, draft code, or analyze data, but because these tools live outside sanctioned channels, their use is rarely inventoried, logged, or reviewed, creating the blind spot commonly described as shadow AI (IBM). The risk is not only that information leaves the enterprise perimeter; it’s that AI systems ingest and learn from what users paste into them, then generate outputs that shape decisions without a clear audit trail or accountable owner, which is a different problem than traditional shadow IT (Palo Alto Networks).

This vacuum persists because most controls were designed to discover devices and apps, not model interactions or prompt flows. As a result, sensitive inputs can move through external models with limited visibility, and their downstream influence may be impossible to reconstruct, later raising questions about compliance, explain ability, and trust (IBM; Palo Alto Networks). Closing the gap starts with treating visibility as control: knowing which AI systems are in use, what data they touch, and how their outputs are governed, so innovation doesn’t outpace accountability (IBM).

Global policy acceleration

Governments are starting to close the visibility gap that lets shadow AI thrive. In the United States, the Office of Management and Budget’s M-24-10 sets a clear baseline for federal agencies: appoint a Chief AI Officer, stand up governance boards, keep an inventory of AI use cases, and apply minimum risk practices. In effect, anything unregistered or opaque becomes a governance exception, not business as usual.

The European Union’s AI Act pushes in the same direction with a risk-based regime that requires documentation, transparency, and human oversight for higher-risk systems. Even though it doesn’t name “shadow AI” explicitly, the Act’s registration and audit obligations make covert or unsanctioned model use far harder to sustain inside organizations.

China moved early on the provider side: its Interim Measures for the Management of Generative AI Services require public-facing services to register, label content, and verify users, an accountability layer that changes how models are offered and monitored. Internal research uses are out of scope, but the direction of travel is unmistakable: visibility, provenance, and traceability.

Taken together, these frameworks send a common signal: AI innovation is welcome, but untracked AI isn’t. That’s the context in which enterprises should evolve their own controls, moving from informal experimentation to auditable, policy-aligned use.

Mapping shadow AI to established frameworks

A practical way to translate policy into operations is to align controls with the NIST AI Risk Management Framework and ISO/IEC 42001. NIST provides a lifecycle for governance: identify and document all AI use cases, including unsanctioned tools; assign ownership; assess security, bias, and performance risks; and implement mitigation and monitoring so model use is visible and traceable over time.

ISO/IEC 42001 complements this by specifying how an organization manages AI systematically. It sets expectations for roles and responsibilities, approvals, internal audits, and continual improvement, so activities such as inventories, testing, and logging are carried out consistently and can be evidenced during reviews.

Used together, these references help convert shadow-AI concerns into repeatable practices: maintain a current inventory, classify risk, enforce access and data controls, record testing and outcomes, and review results on a defined cadence. This supports both day-to-day operations and board-level assurance.

Shadow AI is best understood as a visibility problem that becomes a governance problem. Unsanctioned use of generative tools creates data flows and model influence that traditional IT controls don’t capture. The policy direction is clear, governments are raising expectations for transparency and oversight through measures such as OMB M-24-10 and the EU AI Act and organizations can align operations to that trajectory by mapping their practices to recognized references like the NIST AI RMF and ISO/IEC 42001. The outcome isn’t a larger checklist; it’s a durable operating model where AI use is known, owned, and evidenced so innovation and accountability advance together.