Delegated access means the agent acts on behalf of a signed-in user and should stay within that user’s authorized scope. App-only access means the agent acts under its own application identity and permissions. The first is bounded by user context, while the second is bounded by assigned application rights. Both require tight scoping, but the governance failure modes are different.
Delegated and app-only access solve different trust problems for AI agents
delegated access is the safer mental model when an AI agent is supposed to act as a person, because the user’s authentication and consent define the boundary. App-only access is different: the agent is operating under its own application identity, so the security question becomes whether the application’s assigned permissions are narrow enough for the task. That distinction matters because the same workflow can be acceptable under one model and over-privileged under the other.
For teams building copilots, automations, or orchestrated workflows, the governance issue is not just “can the agent do the job?” but “whose authority is being used, and how far does it extend?” Delegated access usually fits user-centric actions such as reading a mailbox, creating a draft, or updating a record within an approved user context. App-only access is more appropriate for background tasks, service-to-service operations, or scheduled actions where no user is present, but it can become a standing privilege problem if the application accumulates broad rights. For agentic systems, that difference also shapes auditability, consent, revocation, and incident response. OWASP Agentic AI Top 10 frames the risk well: agents become dangerous when authority is broader, longer-lived, or less observable than the task requires. In practice, many security teams only notice the distinction after an agent has already inherited a permission model that was designed for humans or services, not for autonomous execution.
How the access model changes controls, auditability, and blast radius
In delegated access, the agent inherits the user’s identity context and should be constrained to the user’s already-approved scope. That makes it easier to explain to auditors and users, but it does not automatically make the design safe. If the agent can chain actions, reuse tokens, or call downstream tools without step-up checks, it may still exceed the intent of the original consent. The practical test is whether every action remains attributable to the user and whether the application can enforce the user’s effective permissions at the moment of use.
App-only access shifts the control burden to application governance. The agent is no longer limited by a human session, so privilege must be intentionally granted, periodically reviewed, and tightly segmented. This is useful for unattended tasks, but it enlarges the blast radius if the app credential is exposed or the permissions are too broad. The relevant control question is not “who is logged in?” but “what can this app identity reach, change, or delegate on its own?”
A useful way to compare the models is:
- Delegated access ties actions to a user session, so it is usually better for user-specific decisions and traceable personal workflows.
- App-only access ties actions to the application itself, so it is usually better for background automation and shared operational tasks.
- Delegated access can fail when the agent amplifies a user’s rights through chained steps.
- App-only access can fail when the application’s standing rights outgrow the original use case.
NIST AI Risk Management Framework is useful here because the decision is partly about trust, accountability, and operational oversight, not just technical authentication. Where the guidance breaks down is in mixed workflows, where a single AI agent can alternate between user-on-behalf-of actions and independent application actions without a clean control boundary.
Where delegated and app-only models blur in real deployments
Tighter access scoping often increases integration overhead, requiring organisations to balance convenience against revocation, monitoring, and approval complexity. The clean textbook distinction becomes less clear when an AI agent uses delegated access to start a task and then relies on app-only permissions to finish it. That hybrid pattern can be legitimate, but it needs explicit boundary management because the effective authority changes mid-workflow.
Common edge cases include shared workspaces, helpdesk automations, and agent orchestration platforms. In those environments, a delegated token may look like the safest option, yet the real risk is that the downstream tool is still making decisions that the user never directly reviewed. Conversely, app-only access may be operationally necessary for scheduled processing, but the organisation can easily mistake “service account” for “low risk” and forget that an autonomous agent can exercise those rights repeatedly at scale. NIST AI Risk Management Framework and OWASP Top 10 for Agentic Applications 2026 both support the same practical conclusion: the risk is less about the label on the token and more about whether the agent can exceed intended authority without obvious review points.
Practitioners should treat the boundary as broken when one identity model is being used to cover another. If the agent’s job requires repeated human approval, delegated access is usually the more defensible choice. If it needs autonomous execution, app-only access may be justified, but only when the application identity is narrowly scoped and its actions are fully observable.
Risk and Threat Considerations
The material risk in both models is privilege inflation. Delegated access can quietly turn a user’s authority into a machine-speed execution path, while app-only access can create a durable, reusable identity with broader reach than any individual user session. In agentic environments, that difference affects not only misuse but also containment when things go wrong.
Failure mechanism: Delegated access fails when the agent is allowed to chain actions beyond the user’s immediate intent, reuse tokens longer than the workflow requires, or call downstream systems that do not re-check effective scope. App-only access fails when the application identity becomes a standing privilege with broad API access, weak revocation discipline, or poor separation between test, production, and high-value resources. Both patterns can be abused if an attacker compromises the agent, steals its credentials, or manipulates its tool calls.
Impact: The likely consequence is unauthorized data exposure, unapproved transactions, or persistence of access after the original need has ended. In an incident, delegated access may complicate attribution because actions appear user-authorised, while app-only access can complicate containment because the same application rights may be reusable across many workflows.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, OWASP Non-Human Identity Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST AI RMF and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 — Excessive Agent Privilege | Agent authority scope is the core distinction between delegated and app-only access. |
| Recommendation — Limit agent authority to the minimum scope needed for each workflow. | ||
| OWASP Non-Human Identity Top 10 | NHI-01 — Identity and Secret Inventory | App-only access relies on machine identities and credentials that must be owned and tracked. |
| Recommendation — Inventory and govern every application identity and its credentials. | ||
| NIST AI RMF | GOVERN — GOVERN | The choice affects accountability, oversight, and trust in AI-enabled actions. |
| Recommendation — Define accountable ownership for each agent action path and escalation boundary. | ||
| CIS Controls v8 | 6 — Access Control Management | Both access models depend on least privilege, review, and revocation discipline. |
| Recommendation — Apply least-privilege access reviews to every agent credential and scope. | ||
| MITRE ATT&CK | T1550 — Use Alternate Authentication Material | Token misuse and credential theft are plausible abuse paths for delegated or app-only access. |
| Recommendation — Detect and hunt for stolen or reused authentication material. | ||
Practitioner Guidance
What to prioritise: Start by deciding whether the agent is acting as a person or as a service, because that decision should drive consent, logging, and revocation. If the workflow needs user attribution and user-specific scope, delegated access is usually the cleaner model. If it needs unattended execution, app-only access may be acceptable only when the application permissions are deliberately narrower than the task’s worst-case path.
What to verify: Confirm that the agent cannot silently switch from one authority model to the other without an explicit control point. Also verify that token lifetime, downstream tool permissions, and audit records all reflect the same boundary, since a mismatch there is where many implementations become unsafe.
Practitioner takeaway: The safest design is the one where authority matches the task, not the one that merely works fastest; most failures come from granting an AI agent a human-shaped identity model or a service-shaped privilege set and assuming the other side of the boundary will save you.
Related resources from NHI Mgmt Group
- What is the difference between managed identities and hardcoded secrets for AI agents?
- What is the difference between workload identity and API keys for AI agents?
- What is the difference between governing human access and governing AI agent access?
- What is the difference between logging actions and logging intent for AI agents?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 6, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
