Yes, because AI can generate deceptive output, imitate people and operate through delegated or non-human paths. The right model is not to distrust AI automatically, but to verify it continuously and separately from the human who initiated the task. That reduces false confidence without blocking useful automation.
How AI differs from human users and devices as a trust category
AI should be trusted on the basis of observed behaviour, policy enforcement and runtime context, not on human assumptions about intent, accountability or persistence. A person may be unreliable, but they are still a single accountable actor. A device may be automated, but it is usually governed through a clearer identity, posture and ownership model than an AI system that can change outputs across prompts, tools and sessions.
This difference matters because AI can produce convincing but wrong responses, imitate human style and act through delegated paths that blur who is actually making the decision. The practical question is not whether AI is “good” or “bad”, but whether the specific action it is about to take is bounded, attributable and continuously verified.
Human vs Non-Human Identity is useful here because it frames the ownership and governance gap between people, machines and delegated access. For a broader control model, Zero Trust Identity Guide helps translate that difference into continuous verification across users, workloads and devices.
What should be verified differently when AI is involved?
When AI is part of the workflow, the verification target changes. You are not only verifying the human who started the task or the device that carries it. You are also verifying the agentic process, the permissions it can reach, the data it can see and the tool calls it can make. That is why trust needs to be separated into input trust, decision trust and execution trust.
In practice, AI output should never be treated as self-authenticating. The right control question is whether the system can prove which model, prompt context, policy, identity and tool access produced the action. Without that chain, apparently confident output can become a weak source of authority inside an otherwise sound process.
Zero Trust for AI Agents is the clearest operational model for this, because it applies verify-every-request logic to the agent, principal and action. The companion Agentic AI Security Guide is also relevant where tool use, memory and orchestration create a larger attack surface.
Where the trust boundary breaks down in real deployments
The main failure mode is not that AI exists, but that organisations let it inherit trust meant for a human or a trusted device. Once that happens, an AI assistant can be treated as if it were an approved employee, a vetted endpoint or a long-lived service, even when its actual behaviour is more variable and its privileges are broader than intended.
Risk also rises when AI operates through delegation chains, shared accounts or embedded integrations that hide the original requester. In those cases, misuse can look like normal automation, and legitimate output can conceal an unsafe action. That creates a false sense of confidence that is often worse than an obvious denial.
Agentic AI Security Guide covers the common control failures behind that pattern, including tool misuse and trust exploitation. For systems where agents talk to each other, Multi-Agent and A2A Security Guide is especially relevant because inter-agent trust failures can multiply quickly.
Risk and Threat Considerations
Trusting AI like a human user or a managed device can hide privilege, attribution and delegation problems until a harmful action already looks routine. The risk is not limited to bad answers, it includes misuse of access, unsafe tool execution and weak accountability when the system acts on behalf of someone else.
Failure mechanism: Organisations grant AI the same standing as a person or endpoint without rechecking the actor, context and permissions at the moment of action. That allows deceptive output, delegated misuse or chained automation to cross trust boundaries unnoticed.
Impact: The result can be data exposure, unauthorised actions, privilege abuse, and delayed detection because the activity appears to come from a legitimate workflow rather than an attack.
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 and risk surface, while NIST Zero Trust (SP 800-207) and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST Zero Trust (SP 800-207) | PR.AA-05 — Identity Management, Authentication and Access Control | AI trust here depends on continuous verification and least privilege per action. |
| Zero Trust Architecture | Zero trust directly matches the verify-continuously model for people, devices and AI agents. | |
| Recommendation — Apply PR.AA-05 to verify each AI action and enforce least privilege at runtime. Apply zero trust principles so AI decisions are continuously verified and never implicitly trusted. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The question centers on AI acting through delegated authority and excessive trust. |
| Recommendation — Constrain AI privileges and validate every delegated action under ASI03. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | AI is a non-human actor whose permissions can exceed its intended task scope. |
| NHI-10 — Human Use of NHI | The question asks when humans should trust AI differently from the human initiating the task. | |
| Recommendation — Reduce standing permissions and scope AI identities to the minimum required. Separate human intent from machine execution and require explicit approval for high-risk actions. | ||
| NIST SP 800-53 Rev 5 | IA-9 — Identification and Authentication (Non-Organizational Users) | AI and devices need strong authentication when they act as non-human principals. |
| AC-6 — Least Privilege | The answer depends on limiting what AI can do even when it is technically connected. | |
| Recommendation — Use IA-9 to authenticate AI and device principals before allowing access. Enforce AC-6 so AI tools and agents only receive the minimum access needed. | ||
Practitioner Guidance
What to verify: Confirm that every AI-driven action has an identifiable human sponsor, a scoped runtime identity and a policy decision that can be inspected after the fact. If you cannot tie the action to a bounded principal and a specific permission set, treat it as untrusted until proven otherwise.
Decision rule: If the AI can read sensitive data, call tools, send messages or change state, do not rely on output quality alone. Require continuous authorization, explicit action boundaries and a rollback path for anything that can cause external impact.
What practitioners underestimate: The hardest problem is usually not model accuracy, it is trust transfer. Teams often secure the model but forget to secure the path from prompt to permission, which is where most harmful automation mistakes become operational incidents.
Practitioner takeaway: Treat AI as an actor whose trust must be earned per request, not as a category that is inherently trusted or distrusted. The safest pattern is to verify the action, not the impression the output creates.