Because they cannot keep pace with short-lived notebooks, temporary endpoints, and agents that appear and disappear across environments. By the time a spreadsheet is updated, the risk may already have changed. Manual methods also miss runtime behaviour, which is where data exposure and misuse become visible.
Why This Matters for Security Teams
Spreadsheets and surveys create a false sense of control because they measure declared use, not actual use. shadow ai is often introduced through browser-based assistants, ad hoc notebooks, API calls, and embedded agent workflows that leave little trace in the usual inventory process. That gap matters because governance fails when security, privacy, and legal teams only see what employees choose to report, not what is running with access to data and systems. Current guidance in the NIST AI Risk Management Framework treats ongoing monitoring, measurement, and accountability as core requirements, which is exactly where manual tracking falls short.
The real problem is not just completeness. It is timing. Shadow AI can be deployed, modified, or abandoned faster than a quarterly questionnaire can capture it, and that creates blind spots in data handling, model provenance, and privilege assignment. For NHI and agentic ai governance, the risk extends further because service accounts, tokens, and connector permissions may persist after the human user forgets the workflow. In practice, many security teams encounter Shadow AI only after sensitive data has already moved into an unmanaged tool, rather than through intentional discovery.
How It Works in Practice
Effective Shadow AI governance starts with observable signals, not self-reporting. Teams need to combine control-plane telemetry, identity logs, proxy or CASB events, endpoint activity, and application allowlists to identify where AI services are actually being used. A spreadsheet can document an app name, but it cannot show prompt content, file uploads, model version drift, or whether a tool is pulling from regulated data sources. That is why the NIST Cybersecurity Framework 2.0 is useful as a baseline for governance, asset visibility, and continuous risk management.
In practice, a workable program usually includes:
- Discovery of sanctioned and unsanctioned AI services through network, endpoint, and identity telemetry.
- Classification of AI use by data sensitivity, business function, and whether the system is human-operated or agentic.
- Control mapping for data loss prevention, approval workflows, and retention rules tied to actual usage patterns.
- Review of secrets, API keys, and delegated access granted to AI tools or automation scripts.
- Periodic revalidation of owners, purpose, and risk acceptance so stale entries do not linger after a pilot ends.
Where generative systems are involved, governance must also consider prompt and output handling, because unsafe content can be introduced through normal use rather than malicious intent. The NIST AI 600-1 Generative AI Profile and the NIST Cyber AI Profile (IR 8596) both reinforce the need for monitoring, validation, and incident response around AI-specific failure modes. This is also where NHI governance becomes relevant: if an AI workflow depends on long-lived tokens or service identities, those identities need ownership, rotation, and revocation controls just like any other privileged asset. These controls tend to break down in large, decentralised organisations where teams can spin up new tools without central registration because discovery arrives after the workflow has already embedded itself in operations.
Common Variations and Edge Cases
Tighter AI governance often increases friction for product teams, requiring organisations to balance speed of experimentation against visibility and accountability. That tradeoff is real, especially when teams are trying to validate new use cases quickly or when an AI tool is embedded inside another SaaS product and does not present itself as a standalone system.
Best practice is evolving for these edge cases. There is no universal standard yet for how to classify every assistant, plugin, or autonomous agent, so organisations need a pragmatic policy that distinguishes between low-risk experimentation and tools that touch confidential data, regulated records, or production workflows. The NIST AI Risk Management Framework and the EU AI Act both point toward risk-based oversight, but neither removes the need for local controls that reflect actual business context.
Edge cases also include personal devices, contractor access, and agentic automations that inherit human privileges. In those environments, surveys are especially weak because the user may not understand all downstream integrations, and spreadsheets often miss hidden dependencies such as embedded retrieval pipelines or unmanaged connectors. For maturity and auditability, organisations should align policy, discovery, and control evidence under an AI management system such as ISO/IEC 42001:2023 AI Management System Standard. Where AI is deployed through third-party platforms with opaque telemetry, the governance model becomes dependent on vendor logs and contractual disclosure, which means the control design breaks down when the provider cannot expose enough evidence for trustworthy review.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST CSF 2.0, NIST AI RMF, NIST AI 600-1 and NIST IR 8596 set the technical controls, while EU AI Act define the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OC | Shadow AI governance depends on knowing actual assets and business context. |
| NIST AI RMF | GOVERN | Govern function covers accountability, monitoring, and risk ownership for AI. |
| NIST AI 600-1 | GenAI systems need prompt, output, and usage monitoring beyond surveys. | |
| NIST IR 8596 | Cyber AI profiles address runtime misuse and attack surfaces created by AI use. | |
| EU AI Act | Risk-based AI obligations require documented oversight and lifecycle controls. |
Establish continuous AI asset visibility and ownership rather than relying on periodic declarations.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on August 21, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
