User access lets someone operate the tool, while approval authority lets them accept, publish, or act on the AI output. Those are not the same control. In mature governance, a user may be allowed to draft with AI but still need separate permission and oversight to commit the result into a business process.
How user access and approval authority differ in AI-assisted work
User access is about the ability to interact with the AI-enabled tool, prepare a draft, or trigger a workflow. Approval authority is about the power to accept the output, publish it, or let it move into an operational process. The separation matters because a person can be trusted to draft without being trusted to commit, especially where the AI output creates customer, financial, legal, or production impact.
In practice, that distinction is a control boundary, not a wording preference. If the same role can both generate and approve with no independent check, the organisation has collapsed two different decisions into one, which weakens oversight and makes review harder to evidence. Mature design keeps the drafting step productive while treating approval as a higher-trust action.
That separation is often the difference between “assistive use” and “business acceptance.” A reviewer with approval authority is not just looking for typos or style issues, they are asserting that the output is fit for purpose and safe to execute. If the output will be sent, published, submitted, or used to trigger another system, the approval step needs clearer accountability than ordinary tool access.
Where the control boundary matters most
The boundary matters most when AI output can change records, commitments, or external communications. Drafting access may be broad because it is low consequence, but approval authority should narrow as the downstream impact rises. IAM and IGA Basics is useful background here because the same governance logic that separates entitlement from approval also applies to AI-assisted workflows.
It also matters when multiple roles are involved in the same workflow. A team member may be allowed to generate a recommendation, another may validate the context, and a supervisor may hold final approval. That three-step pattern reduces the risk of rubber-stamping and makes it easier to prove who decided what. The control is strongest when approval authority is explicit, logged, and different from the person who created the draft.
For access design, the practical question is not only “Can they use the AI?” but “Can they cause the output to become operational?” That distinction is especially important where human review is used as the final safeguard before payment, publication, customer communication, case closure, or system change. Access Reviews and Certification Guide aligns well with this because approval rights should be reviewed as carefully as the underlying access that enables the draft.
What good governance looks like in day-to-day operations
Good governance treats user access and approval authority as separate entitlements with separate evidence. The user should have enough access to do the drafting job efficiently, but approval should require a narrower assignment, a clear reason, and traceable oversight. If the same person must occasionally do both, that exception should be time-bounded and reviewed, not treated as the default operating model.
The cleanest test is whether the workflow preserves an independent decision point after the AI step. If the person can both generate and approve without a meaningful pause, second review, or system checkpoint, the control is probably too weak. Human vs Non-Human Identity is a useful reference for thinking about how people, tools, and delegated actions meet in practice, especially when the workflow combines human judgement with machine-assisted output.
Teams should also distinguish between editing and authorising. Editing changes the draft; authorising makes the organisation responsible for it. That is why some processes allow broad collaboration on content while reserving final approval for a smaller group with stronger accountability, tighter audit requirements, and a clearer conflict-of-interest boundary.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
NIST SP 800-53 Rev 5 sets the technical controls, while ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | AC-6 — Least Privilege | Approval rights should be narrower than draft-only access for AI-assisted workflows. |
| AU-2 — Event Logging | Workflow approval needs traceable evidence of who drafted, reviewed, and approved the output. | |
| Recommendation — Separate drafting access from approval authority and limit each role to the least privilege it needs. Log drafting, review, and approval events so each AI-assisted action is attributable. | ||
| ISO/IEC 27001:2022 | A.5.15 — Access control | The question is about separating use rights from authorisation rights in a business process. |
| A.8.2 — Privileged access rights | Approval authority is a higher-trust permission than ordinary user access. | |
| Recommendation — Define separate access and approval permissions for AI-assisted workflows. Restrict approval authority to a smaller, reviewed set of privileged users. | ||
Practitioner Guidance
What to verify: Check whether the workflow system records who drafted the AI output, who reviewed it, and who approved it. If those events are not separated in the audit trail, the control is too weak to rely on for higher-impact processes.
Decision rule: If a person can cause money movement, customer notification, regulatory submission, or production change, treat approval as a separate privilege from general tool access. Drafting can be broad; approval should be narrower and easier to revoke.
Common mistake: Organisations often grant “review” rights that are effectively the same as approval rights, then assume they have segregation because the labels differ. Labels do not matter if the same user can still finalise the action without independent oversight.
Practitioner takeaway: The key governance choice is not whether people may use AI, it is whether the people who can create AI-assisted content are also the people trusted to make it operational. Keeping those rights separate preserves accountability and prevents convenience from becoming uncontrolled approval.
Related resources from NHI Mgmt Group
- What is the difference between delegated user access and machine authority for AI agents?
- What is the difference between reviewing human access and reviewing NHIs?
- What is the difference between role-based access and API key governance for NHI security?
- What is the difference between protecting applications and protecting access?
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Reviewed and updated by the NHIMG editorial team on October 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org