They need to manage both. Collaboration is the operating surface, but identity is what determines who or what is allowed to act, and under what authority. If those controls are separated, organisations will keep spotting events without being able to govern the actor behind them.
Why AI assistants and agents are not just collaboration tools
AI assistants and agents do sit in collaboration workflows, but the security question is whether they are acting with a defined principal, scoped authority, and auditable boundaries. Once a system can send messages, call tools, move data, or approve actions on behalf of a user or team, it stops being only a productivity surface and becomes an identity-bearing actor that must be governed as such.
The practical distinction is that collaboration explains the work the system supports, while identity explains the authority it exercises. That distinction matters because the same interface can mask very different access paths, from benign copilots to agents holding tokens, delegated permissions, or long-lived credentials. Agentic AI Identity Guide is useful here because it frames how agents are registered, authenticated, delegated, and retired.
Good operating models therefore treat collaboration as the channel and identity as the control plane. If you only govern the chat experience, you miss the questions that actually decide risk: who can act, what can it touch, whether it is acting as itself or on behalf of a human, and how the authority is revoked when the relationship changes. For that reason, AI Agent Authorisation Guide is a natural companion because it focuses on least privilege, task-scoped access, and per-action decisions.
Where collaboration breaks down without identity
Collaboration systems are designed to coordinate people and content, but agentic systems can create actions, not just conversations. The moment an assistant can retrieve data, post updates, open tickets, trigger workflows, or consume APIs, the central issue becomes authorization, delegation, and the provenance of the action. AI Agents vs Agentic AI helps separate lightweight assistance from higher-autonomy systems where identity and access decisions become materially more important.
That is also why collaboration controls alone are insufficient. A comment thread, approval queue, or team channel can show what happened, but it does not inherently constrain standing privilege, token reuse, or cross-system authority. When the identity layer is weak, the organisation may be able to observe the agent’s output while still being unable to govern its effective permissions.
This becomes especially important when agents reuse human credentials, share tokens across workflows, or inherit permissions from an overly broad integration. In those cases, the collaboration surface looks familiar, but the failure mode is identity abuse: the system is acting with authority it should not have, or authority no longer appropriate to the task.
What good governance looks like in practice
A mature model starts by inventorying agents the same way you would inventory other actors with access: what they are, who owns them, what principal they use, which systems they can reach, and how their access ends. Agentic AI Identity Maturity Model is a useful lens for judging whether an organisation has moved beyond ad hoc deployment into repeatable governance.
From there, the practical control questions are straightforward:
- Does the agent have its own identity, or is it borrowing human credentials?
- Are actions authorised per task or only at login time?
- Is access short-lived and revocable, or effectively standing?
- Can the organisation attribute an action to the agent, the user, and the delegated scope?
- Is offboarding real, meaning credentials, links, and permissions are actually removed?
Those questions are not academic. They determine whether the organisation can contain misuse, investigate incidents, and prove that an agent was acting within approved bounds. The best operating model is usually a split one: collaboration tools govern the conversation and workflow, while identity and authorization govern the authority to act. AI Agent Observability, Audit and Incident Response Guide fits this model because auditability and attribution are what make governance enforceable.
Risk and Threat Considerations
When organisations treat agents as collaboration-only systems, they often miss the risk that the real asset is delegated authority. That creates exposure to token theft, overbroad permissions, hidden tool use, and actions that are difficult to attribute after the fact. The same oversight also makes third-party or supply-chain compromise more damaging, because an attacker inherits whatever authority the agent was already trusted to exercise.
Failure mechanism: the organisation monitors the interaction layer but not the actor layer, so an agent can retain access, reuse credentials, or operate outside its intended scope even when the conversation looks normal.
Impact: misuse can spread across email, SaaS, API, and workflow systems, creating unauthorized actions, data exposure, and incident response problems that are harder to unwind than a standard user account issue.
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 and OWASP Non-Human Identity Top 10 address the attack surface, NIST SP 800-53 Rev 5 sets the technical controls, and ISO/IEC 27001:2022 defines the regulatory obligations.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | AI agents acting on behalf of users create privilege and delegation risk. |
| ASI02 — Tool Misuse | Agents can call tools and services beyond intended collaboration use. | |
| Recommendation — Apply per-action authorization and remove standing agent privilege. Constrain tool access to approved actions and scopes. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | Agents commonly inherit excessive permissions from reused credentials or broad scopes. |
| Recommendation — Audit agent permissions and reduce them to task-scoped least privilege. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Identification and Authentication (Service Users) | AI agents and services need machine-to-machine authentication and trust control. |
| AC-6 — Least Privilege | Agent authority must be limited to the minimum needed for each task. | |
| IA-5 — Authenticator Management | Agent tokens, keys, and secrets need lifecycle control to prevent lingering access. | |
| Recommendation — Authenticate agents with service-appropriate identities and managed credentials. Restrict agent permissions to the minimum required for each approved action. Rotate and revoke agent authenticators on a defined lifecycle. | ||
| ISO/IEC 27001:2022 | A.5.15 — Access control | Agent authority and collaboration access both require governed access control. |
| A.5.16 — Identity management | Agents need ownership, registration, and lifecycle handling as managed identities. | |
| Recommendation — Define and enforce access rules for every agent and integration. Maintain a complete inventory of agent identities and owners. | ||
Practitioner Guidance
What to prioritise: treat the first control boundary as the agent’s authority, not the user interface. If you cannot answer what principal the agent uses, what scope it has, and how that scope is revoked, the deployment is not ready for broad operational use.
What to verify: confirm that each agent has an owner, a distinct identity or delegated trust model, and a documented offboarding path. Verify that the system can attribute an action to the agent and the delegated context, not just to the conversation thread.
Common mistake: allowing collaboration tooling to become the de facto security model. A shared workspace can support teamwork, but it cannot replace least privilege, per-action authorization, or credential lifecycle controls for anything that can take material action.
Practitioner takeaway: the right operating model is not identity versus collaboration, it is collaboration on top of identity, with authority, scope, and revocation designed before the agent is trusted to act.