An AI power user is an employee whose work depends on frequent, deep, and multi-platform AI interaction. In governance terms, this is not just heavy usage. It is a concentration point for data exposure, account switching, and policy drift that standard user-wide controls may miss.
Expanded Definition
An AI power user is not simply a frequent AI consumer. In NHI governance, the term describes a person whose daily workflow depends on repeated prompt submission, model switching, file uploads, and tool use across multiple platforms, creating a concentrated zone of identity, data, and policy risk.
Usage in the industry is still evolving, but the governance concern is consistent: the more an employee relies on AI to draft, analyse, summarise, or execute work, the more likely they are to move sensitive information into places that standard user controls do not fully observe. That makes the AI power user distinct from a casual user and from a developer, even though the same individual may be both. The right control lens is closer to NIST Cybersecurity Framework 2.0 style governance than to a simple software-usage policy, because the risk includes data handling, authorisation scope, and persistent account context across systems.
The most common misapplication is treating AI power users as ordinary productivity users, which occurs when organisations apply only broad acceptable-use rules and ignore prompt content, connected tools, and cross-platform credential exposure.
Examples and Use Cases
Implementing AI power user controls rigorously often introduces friction, because tighter guardrails can slow fast-moving knowledge work and require more review of prompts, uploads, and approved integrations.
- A finance analyst uses several AI tools in one day to summarise filings, compare scenarios, and draft commentary, making prompt logs and uploaded spreadsheets a governance concern rather than a convenience feature.
- A recruiter copies candidate notes into an AI assistant to rewrite outreach, creating potential exposure of personal data and internal hiring decisions if data handling rules are unclear.
- A product manager switches between internal and external AI platforms, which increases the chance of policy drift when one tool is approved for public content only and another is not.
- A support lead pastes incident details into an AI tool to accelerate response drafting, which can create retention and sharing issues if the tool is not bound to approved NHI controls.
- For threat context, NHIMG’s LLMjacking research shows how compromised NHIs can be used to hijack AI access, while DeepSeek breach illustrates how AI environments can expose secrets and sensitive records at scale.
Where standards language is needed, AI power user controls should align with the identity, access, and monitoring expectations described in NIST Cybersecurity Framework 2.0, especially where data flow and access boundaries overlap.
Why It Matters in NHI Security
AI power users become especially important in NHI security because they often sit at the intersection of human intent, machine access, and sensitive data movement. Their workflows can create repeated opportunities for credential exposure, unapproved connectors, and shadow ai usage that bypasses central oversight. This matters because NHIs are frequently what make AI work possible in the first place: API keys, delegated tokens, service accounts, and connected applications. When those assets are overused or loosely governed, the AI power user can become an unintentional amplification point for compromise.
NHIMG research highlights how quickly exposed credentials can be abused. In the LLMjacking analysis by Entro Security, attackers attempted access to publicly exposed AWS credentials within an average of 17 minutes. That speed matters because AI power users often work across many services and may paste, upload, or connect data faster than security teams can intervene. In parallel, NHIMG’s The State of Secrets in AppSec research reports that only 44% of developers follow secrets management best practices, showing how easily policy drift spreads across daily workflows.
Organisations typically encounter the consequences only after a secret leak, account takeover, or AI policy breach, at which point the AI power user 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 address the attack and risk surface, while NIST CSF 2.0 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-02 | Addresses secret exposure and misuse risks that AI power users can amplify. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access and access review apply to high-use AI accounts and connectors. |
| NIST Zero Trust (SP 800-207) | Zero trust principles fit AI users who switch tools, contexts, and data sources often. |
Restrict AI tool access to approved secrets handling paths and review usage for sprawl.
Related resources from NHI Mgmt Group
- Why do AI platforms create NHI risk even when user sessions are short?
- How should security teams use AI agents for user access reviews?
- What is the difference between managing user access and NHI access for AI projects?
- What is the difference between delegated user access and machine authority for AI agents?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 9, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
