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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Login alone cannot secure an AI agent’s work. In a 6 October 2026 article, John Cannava, CIO at Ping Identity, argues that organizations must govern an agent’s authority throughout its interactions: assign it an accountable owner, limit what it can do, check consequential actions against current context and policy, and make delegation traceable.
Why an agent’s login is only the start
Authentication establishes that an identity has presented valid credentials. Authorization determines what that identity may do. Cannava’s central point is that a successful initial authentication should not authorize every later action in an agent session. The context and potential impact can change as the agent works, so access decisions for consequential actions should be evaluated when those actions occur.
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Examples Cannava highlights include accessing sensitive data, escalating privileges, making payments, and spawning other agents. He recommends runtime authorization that can consider policy and risk as they change. Device posture, threat intelligence, and behavioral analytics are examples of signals his article proposes—not a mandatory checklist for every deployment. His short formulation is: “Trust can no longer be assumed. It must be continuously proven.”
This is a leadership argument and a set of recommendations, not a measured study. The reviewed article reports no quantified reduction in risk or cost and does not establish that a particular platform or signal set produces a specific result. Read Cannava’s article in The AI Journal.
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Give each agent an identity, owner, and bounded authority
An agent should not operate as an anonymous extension of a user account or inherit broad access simply because a person launched it. Cannava recommends issuing explicit identities to agents and defining their authority in a way that is limited to the task and traceable to a responsible person or enterprise system of record.
- Assign ownership: Identify the person or system accountable for the agent’s purpose, access, and lifecycle.
- Scope permissions: Grant only the access needed for the task rather than broad standing privileges.
- Record delegation: Preserve which human or enterprise process authorized the agent, what authority was delegated, and which downstream agents or systems it invoked.
- Separate identity from authority: Knowing which agent is acting does not, by itself, mean every requested action should be allowed.
This makes accountability part of the identity record rather than an assumption about whoever initiated a workflow. As Cannava puts it, “Accountability doesn’t just disappear because an agent performs the action.”
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Manage agent access as a lifecycle
Agent permissions and credentials need controls for their full lifecycle, not just initial provisioning. Cannava calls for least privilege, just-in-time access, short-lived credentials, automatic expiration, revocation, continuous policy enforcement, and audit trails. In practice, a team can turn those recommendations into a sequence of operational questions:
- Before access is issued: Is the agent’s owner known, and is each permission necessary for its stated task?
- When access is needed: Can it be granted only for the required period or action, instead of remaining available indefinitely?
- As the agent acts: Are sensitive or high-impact requests checked against current policy and context?
- When circumstances change: Can access be revoked promptly, and do credentials expire automatically?
- After actions occur: Can an auditor reconstruct the agent’s identity, authority, delegation chain, and actions?
Annual access certifications and static role-based access controls can still be useful, but Cannava warns they may not keep pace on their own with dynamic AI systems. The practical implication is to retain governance checkpoints while adding controls that respond to task-level access and changing conditions.
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Use non-human identity risks to test the controls
AI agents are one form of non-human identity (NHI), but the OWASP Non-Human Identities Top 10 is a broader risk taxonomy, not an AI-agent-specific standard. Its 2025 risks include improper offboarding, secret leakage, insecure authentication, overprivileged identities, long-lived secrets, weak environment isolation, NHI reuse, and human use of NHIs. These are useful prompts for checking whether an agent identity’s credentials, permissions, and retirement process are adequately governed; the list does not establish how prevalent each risk is.
OWASP notes that “Application secrets may be used to authenticate into other applications within the trust domain.” That illustrates why a secret’s scope and reuse matter: a credential intended for one connection can carry risk across a broader trust boundary if it is exposed or over-permissioned. Review the OWASP Non-Human Identities Top 10 as a set of risk categories, not proof that any one product resolves them.
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Coordinate human, machine, and agent identity governance
Cannava argues for governing human, machine, and AI identities within a common model. That does not mean every identity should receive identical controls. It means policies should make ownership, authority, lifecycle status, delegation, and auditability understandable across identity types.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteHis recommendations also include transparent policies, limiting unnecessary concentration of personally identifiable information, and ensuring systems fail safely during high-risk authorization events. For example, an organization should decide in advance what an agent may do when an authorization check cannot be completed, rather than allowing an uncertain high-impact action by default. The article presents these as leadership recommendations and does not report deployment data validating their effects.
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Where NIST’s AI Risk Management Framework fits
NIST’s AI Risk Management Framework is a broad, voluntary reference intended to help incorporate 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. Organizations can use it for wider AI risk-management context while developing specific identity policies for agents.
NIST’s framework page says, “The AI RMF 1.0 is being revised as part of the White House AI Action Plan.” The page also references a concept note for a critical-infrastructure trustworthy AI profile released on 7 April 2026. These status notes are reasons to check NIST’s current materials when using the framework; they do not change the distinction between broad risk-management guidance and operational agent authorization. See NIST’s AI Risk Management Framework page.
Questions to ask before an agent goes live
- Who owns this agent, and where is that ownership recorded?
- What is its explicit identity, and which permissions are necessary for its defined task?
- Which actions require a fresh authorization decision rather than relying on the original login?
- How are credentials scoped, expired, rotated, and revoked?
- Can the organization trace delegated authority through downstream agents and systems?
- What happens if a high-risk authorization check fails or cannot be completed?
- Can the audit trail show who or what authorized each consequential action?
These questions translate Cannava’s proposed controls and OWASP’s NHI risk categories into a governance review. They are not a substitute for evaluating an organization’s own systems, policies, and threat model.
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