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Logging an AI agent in does not authorize every action it might take later. Organizations need to identify each agent, define whose authority it represents, limit what it can do, and evaluate sensitive actions against current policy and risk. That is the central argument John Cannava, CIO at Ping Identity, makes in his 6 October 2026 article for The AI Journal. His recommendations are a leadership perspective, not a measured study of security outcomes.
Why an agent’s login is not enough
Authentication establishes that an identity has presented acceptable credentials. Authorization determines what that identity may do. Cannava argues that the distinction matters especially for agents: a valid sign-in at the beginning of a session should not be treated as blanket permission for every subsequent action.
An agent may access sensitive data, seek higher privileges, initiate a payment, or create another agent. Cannava recommends assessing consequential actions at runtime using the current context, applicable policy, and risk. He identifies device posture, threat intelligence, and behavioral analytics as possible signals for adaptive policy—not mandatory inputs for every system. His point is that trust should be reassessed as the agent acts, rather than inferred indefinitely from its initial authentication. Read Cannava’s article in The AI Journal.
Give every agent an identity, owner, and defined authority
To govern an agent, an organization needs to know what it is, who is responsible for it, and what authority it has been granted. Cannava recommends explicit identity issuance, an accountable owner, and permissions scoped to the agent’s purpose. Delegated authority should be traceable to a responsible person or an enterprise system of record.
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This creates a chain of accountability: which human or system authorized the agent, which permissions it received, and which actions it took under those permissions. An agent carrying out an action does not erase responsibility. As Cannava puts it, “Accountability doesn’t just disappear because an agent performs the action.”
Manage access across its full lifecycle
Access governance cannot end when an identity is created. Cannava calls for familiar identity controls adapted to agents’ changing and potentially autonomous behavior: least privilege, just-in-time access, short-lived credentials, automatic expiration, revocation, ongoing policy enforcement, and audit trails. Annual access reviews and static role assignments may not keep pace with rapidly changing agent capabilities and tasks.
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- Grant narrowly: Give an agent only the permissions needed for its defined task.
- Grant temporarily: Use just-in-time access and credentials that expire instead of leaving access open-ended.
- Reassess and revoke: Apply policy as actions occur, and withdraw access when an agent, task, or authorization is no longer valid.
- Record delegation: Preserve visibility into who or what delegated authority, what the agent did, and which permissions were in effect.
These measures address operational concerns that extend beyond AI agents. OWASP’s 2025 Non-Human Identities Top 10 includes improper offboarding, secret leakage, overprivileged identities, long-lived secrets, reuse of non-human identities, and human use of those identities. The list is a risk taxonomy for non-human identities; it is not an AI-agent-specific standard or a ranking of how often enterprises experience each risk. OWASP also notes that application secrets can authenticate into other applications within a trust domain. See the OWASP Non-Human Identities Top 10.
Apply a common governance model without losing accountability
Cannava recommends governing human, machine, and AI identities within a common model rather than treating agents as an exception to identity management. In practice, common governance should mean consistent ownership, access policy, lifecycle handling, and auditability—not that every identity receives identical permissions or is managed in the same way.
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He also argues for transparent policies, auditable delegation, avoiding unnecessary concentration of personally identifiable information, and safe failure when a high-risk authorization decision cannot be made. These are recommendations in his article, not reported results from deployments. For implementation decisions, organizations can assess whether their approach covers human, machine, and agent identities; supports fine-grained runtime authorization; handles credential expiration and revocation; records ownership and delegation; provides useful audit visibility; and integrates with existing identity systems.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use NIST and OWASP for context, not as agent IAM recipes
NIST’s AI Risk Management Framework provides broad, voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation. It is not a binding regulation or a detailed catalog of identity and access-management controls for agents. NIST’s framework page says version 1.0 is being revised and references a concept note for a trustworthy AI profile for critical infrastructure, released on 7 April 2026. Consult NIST’s AI Risk Management Framework page.
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OWASP’s non-human identity list serves a different purpose: it helps identify security risks associated with identities such as applications and their secrets. Used together, these sources offer general risk-management context and concrete NHI concerns, while Cannava’s article supplies the leadership argument for extending identity governance to autonomous agents. None of the cited material establishes that a particular platform or control set will produce a quantified reduction in risk or cost.
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