AI users are employees or stakeholders who interact with AI services to support their work. In an identity security context, they still require governance because their accounts, access paths, and data-sharing behaviour can create shadow AI risk, unauthorised usage, and exposure of sensitive information.
Expanded Definition
AI users are not just passive consumers of output. In NHI security, the term covers employees, contractors, and other stakeholders whose accounts, prompts, approvals, uploads, and sharing decisions shape how AI services are used. That matters because the identity risk is often created at the human-to-AI boundary, where access paths, permissions, and data handling determine whether AI stays within governed use or becomes shadow ai.
Definitions vary across vendors, but the practical distinction is clear: an AI user may not administer the model, yet still influence what the model can see, return, or store. That makes the account itself, the assigned role, and the surrounding control plane part of the security surface. Governance should align with principles in the NIST Cybersecurity Framework 2.0, especially where access control and data protection intersect.
The most common misapplication is treating AI users as ordinary application users, which occurs when organisations ignore prompt content, file uploads, and sharing behaviour as governed identity activity.
Examples and Use Cases
Implementing AI user governance rigorously often introduces friction, requiring organisations to weigh faster AI-assisted work against tighter controls on data exposure and approved tools.
- An analyst uses a corporate AI assistant to summarise incident notes, but the workflow blocks sensitive attachments unless the user is authorised for that dataset.
- A marketer uploads draft campaign copy into a public AI service, and the organisation applies policy controls to prevent confidential product details from leaving approved environments.
- A developer uses an internal coding assistant with scoped access to repositories, secrets, and tickets, reducing the chance of accidental disclosure through prompts.
- An operations manager approves an AI-generated report, but the system logs the user identity, prompt context, and downstream exports for review and audit.
- A security team investigates shadow AI after finding unsanctioned accounts linked to a DeepSeek breach-type exposure pattern, where sensitive data was handled outside expected controls.
For identity-aware AI use, external guidance such as the NIST Cybersecurity Framework 2.0 helps translate policy into access, monitoring, and response requirements.
Why It Matters in NHI Security
AI users are a governance boundary because their behavior determines whether AI systems become controlled business tools or uncontrolled data channels. When organisations fail to classify AI usage by identity, they lose visibility into who prompted what, which data was exposed, and whether the output was acted on in an approved context. That creates shadow AI risk, weakens auditability, and can turn a routine workflow into a secrets leakage event.
NHIMG research shows that only 44% of developers are reported to follow security best practices for secrets management, which is a useful signal of how human behavior can undermine technical controls in AI-enabled environments. The same pattern appears when users paste credentials, internal records, or regulated data into AI tools without governance. Security teams should pair identity policy with data handling rules, logging, and role-based restrictions, especially where AI assistants can access sensitive systems or export content.
Organisations typically encounter the consequences only after a prompt leak, unauthorized upload, or compliance incident, at which point AI user governance becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | AI user behavior creates identity-linked misuse and shadow AI exposure paths. |
| NIST CSF 2.0 | PR.AC-4 | AI users need access governance and least privilege over AI-connected data flows. |
| NIST AI RMF | AI user interactions shape risk, misuse, and data governance outcomes in AI systems. | |
| NIST Zero Trust (SP 800-207) | SC-7 | AI users should be constrained by continuous verification and data-flow control. |
| OWASP Agentic AI Top 10 | A2 | User interactions can trigger unsafe tool use, prompt injection, and data leakage. |
Apply least privilege to AI users and review access to prompts, tools, and data regularly.
Related resources from NHI Mgmt Group
- How should security teams handle SaaS offboarding when users also use AI tools?
- Why do service accounts and AI agents need different controls from human users?
- How can organisations reduce unsafe AI outputs without over-restricting users?
- What is the difference between managing AI agents as users and as NHIs?
