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Security teams can govern workplace AI more effectively by creating an approved path for useful work, then matching controls to what each use case can access and do. The goal is not simply to list AI tools or block them: it is to make use visible, limit exposure, and strengthen safeguards as systems gain autonomy and influence over important systems.
Start with use cases, not just AI tools
A tool inventory can tell you which services employees use, but it does not show the risks of each workflow. The same assistant might summarize public material in one setting and handle sensitive data or initiate changes in another. For each use case, record what information it can reach, what actions it is allowed to take, and which systems may be affected.
Use those facts to build a practical risk picture. Alongside access, actions, and affected systems, consider data sensitivity and reach, degree of autonomy, reversibility, and potential impact. These additional factors are useful planning dimensions, not a formal scoring system prescribed by NIST.
- Data: What can the AI system read, receive, retain, or produce? Could the workflow expose confidential, personal, or operational information?
- Permissions and actions: Can it only draft or summarize, or can it also send messages, use credentials, execute code, or change records?
- Systems and impact: Which business services, repositories, or production environments could be affected if the system makes a mistake?
- Autonomy and reversibility: Does a person approve each consequential step, and can an error be undone quickly?
This inventory should be revisited as a workflow changes. A tool that begins as an assistant may later receive more data, permissions, or authority, changing its risk profile even if the product name stays the same.
#1 Best Overall
Match safeguards to autonomy and impact
A workflow that summarizes approved documents is not equivalent to an agent that can use credentials, run code, or modify production systems. Controls should rise with the system’s autonomy and the consequences of its actions. A useful starting point is to distinguish assistance from execution, then identify the boundary where a human must review or authorize an action.
| Use pattern | Typical concern | Governance response |
|---|---|---|
| Read-only assistance, such as summarizing material the user is already permitted to access | Disclosure or inaccurate output | Limit inputs to approved data, define handling expectations, and keep a person responsible for evaluating outputs. |
| Workflow assistance that drafts or recommends actions | Incorrect recommendations being treated as verified | Require review before consequential actions and make clear which outputs are suggestions. |
| Agentic execution with credentials, code execution, or production access | Unauthorized changes, credential misuse, or wider operational impact | Constrain permissions and network reach, isolate execution, log activity, and enforce approval or other boundaries outside the agent. |
This is a practical comparison, not a NIST-mandated tiering scheme. The important design question is what the system can actually do, not whether it is marketed as an assistant or an agent.
Rank #2
Give employees a sanctioned route and find shadow AI
A blanket prohibition may leave employees to use personal accounts or untracked workflows. John Sapp’s sponsored article for The New Stack argues for a practical approved path that offers enough utility to make visible, governed use viable. That is the author’s recommendation, not proof that a particular blocking policy causes shadow AI.
Make the approved route clear: identify which services and use cases are permitted, what data they may handle, and where employees should request an exception or a new use case. Then check whether the route works in practice. Track visibility of AI use, approved versus unapproved use, exception requests, and signs that employees continue to work around the policy. Use those signals to improve access and governance rather than treating a written policy as evidence that activity is controlled.
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Agent execution should be treated as untrusted until its behavior and boundaries are verified. Apply least privilege, restrict credentials and network access to what the task requires, isolate execution from sensitive systems, and enforce limits outside the agent itself. A model’s instruction to behave safely is not a substitute for controls that prevent unauthorized access or changes.
Also govern the software an AI-assisted workflow selects or generates. Generated code can inherit risks from packages, libraries, container images, and other dependencies. Provide developers and agents with trusted, approved, minimal, and maintained components, and apply ordinary software supply-chain and deployment controls. AI does not eliminate familiar concerns around confidentiality, integrity, availability, data security, or underlying software and hardware.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use NIST as a voluntary lifecycle guide
NIST’s AI Risk Management Framework (AI RMF) is a voluntary framework, not a regulation or mandatory certification. Its four functions—Govern, Map, Measure, and Manage—organize risk work across the AI system lifecycle, with governance cutting across the other functions. NIST says AI RMF 1.0 is being revised; consult the AI RMF page for current information.
Rank #4
For generative AI, NIST AI 600-1, the Generative AI Profile, was published July 26, 2024 as a cross-sector companion resource. It proposes actions to govern, map, measure, and manage generative AI risks. NIST also identifies security and resilience as characteristics of trustworthy AI and notes that some AI cybersecurity concerns overlap with risks in ordinary software development and deployment; see its AI security and resilience overview.
In practice, use the framework to structure the work rather than as a checklist that replaces judgment: establish ownership and rules, map each use case and its context, evaluate relevant risks, and manage them with safeguards that evolve as the system changes.
Quick Recap
Turn governance into a working roadmap
- Establish visibility: Create an inventory of AI use cases, including informal or employee-initiated workflows where they can be identified.
- Describe authority: For each use case, document accessible data, permitted actions, affected systems, autonomy, and likely impact.
- Set the approved path: Publish permitted tools and uses, data-handling expectations, and a route for review of new use cases or exceptions.
- Apply controls proportionately: Keep lower-impact assistance constrained to appropriate data; isolate and tightly limit agents with credentials, code execution, or operational access.
- Secure dependencies: Encourage use of approved, minimal, maintained software components and retain normal review and deployment safeguards for generated code.
- Measure and revise: Monitor visibility, approved and unapproved use, exceptions, and workarounds. Update controls when systems move from assisting people to taking actions.
The New Stack article “Transform AI from a security blind spot into a roadmap,” published October 1, 2026, is sponsored by Chainguard, and its author John Sapp is Chainguard’s Field CISO. Its recommendations, including the emphasis on trusted software components, should be read as the perspective of a vendor-affiliated security executive, not as an independent evaluation of a product.
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