The accountability gap in autonomous AI: 

AI agents are no longer experimental. They are already executing trades, modifying infrastructure, querying sensitive data, and acting across enterprise systems—often with little human intervention. What’s changed isn’t just automation. It’s agency.

And agency changes everything about identity, access and accountability.
These systems don’t just execute instructions; they decide what actions to take, delegate authority, and operate continuously rather than only when triggered. They autonomously start tools, APIs and systems, often acting on behalf of users, teams or other machines. In doing so, they introduce ambiguity around intent, authority and responsibility.

Most enterprises still govern access by using identity models designed in the past two decades—models built for human operators, not machines. As a result, organizations are at risk of deploying autonomous agents without the ability to confidently answer basic questions of accountability.

These questions will not just come from auditors, regulators and your board. Organizations run the risk of their clients challenging transactions that occur. These questions come in many forms:

• Who authorized this end-to-end transaction?
• Which humans own the overall process and, if required, provided runtime consent?
• Did every agent and human authenticate by using the strongest method of authentication available?
• What exactly was the agent configured to do, and did it do only that?
• Did the agent lose privilege once that transaction occurred?
• Can you show me an audit trail that links full accountability across the transaction?
• Can we revoke the agent’s authority instantly—everywhere?

If you can’t answer those questions, you don’t have the governance you need to avoid failed audits and damage to your reputation.

Traditional identity access management (IAM) is fundamentally rooted in the idea that access can be planned in advance—assigned to known identities, reviewed periodically, and enforced at login. That approach works when behavior is predictable and bounded. Traditional IAM assumes three things:

1. Identities are human or static machines.
2. Access is session-based and provisioned according to a periodic lifecycle.
3. Authentication unlocks the session, and authorization is largely coarse-grained, whereas access is granted based on broad, general attributes, such as a user’s role or department.

Agentic AI challenges all three of these assumptions. Autonomous agents are non-deterministic, operate at machine speed, and act continuously across APIs, tools and environments. In this model, identity is no longer stable human account or long-lived service principal. It can be ephemeral and purpose-built for a specific capability, workflow or even a single transaction, then revoked immediately after execution.

An end-to-end agentic flow has many agents logging in once, each running a discrete activitity. They don’t “log in” once. They might make thousands of decisions per hour, often on behalf of users, systems or other agents. They have an identity themselves and engage with each other in end-to-end transactional flows that span trust boundaries.

When organizations apply human IAM patterns to agents, 4 systemic failures emerge:

• Over-privilege without visibility: Agents accumulate standing access because revoking and reapproving permissions slows innovation. That access rarely expires.
• Invisible delegation:
  Many agents reuse user tokens instead of receiving delegated authority. This method erases audit separation and shifts liability onto individuals who never approved the action.
• No enforcement where actions occur: Policies might exist, but the actual actions (such as API calls, data access and configuration changes) often happen without real-time controls.
• Zero accountability when things go wrong: After an incident, teams cannot reliably reconstruct what happened, why or with whose authority.

This issue isn’t a tooling gap; it’s a governance gap that must be closed.

The core mistake enterprises make is treating agent identity as a credential problem. Credentials are important, but in agentic systems they’re no longer a single login event that establishes trust for everything that follows. 

Instead, agents operate continuously, start multiple tools, cross systems, and take many independent actions over time. That means identity and authorization can’t be “set once” at the start of a session; they must be validated and enforced throughout the workflow.

Agentic AI requires a shift from access control to authority control. This means:

• Every user engaged in an agentic flow must be strongly authenticated to start the integrity and accountability guarantee.
• Every agent must have a first-class, verifiable identity.
• Every action must be tied to explicit intent and approval.
• Every permission must be scoped, time-bound and revocable.
• Every authorization decision must be provable after the fact.

This isn’t about slowing down AI; it’s about making autonomy safe. While authentication remains a core demand of any system, authorization must become a first-class actor in an access management system. This has serious implications for those implementing IAM programs.  

Many organizations try to reduce agent risk by implementing governance controls in fragments. Each fragment solves part of the problem, but none can prove who authorized what, under what conditions and with which credentials. These fragments include:

• Identity platforms that understand users but not machines
• Secrets platforms that issue credentials but lack context
• Policy engines disconnected from runtime enforcement

None of these alone can govern agents. Real governance emerges only when identity, policy and credentials operate within a single control plane—where identity answers who and why, and credentials answer what and how long. They blend across a use case to deliver integrity and accountability.

This is where the IBM Verify + HashiCorp Vault combination becomes uniquely powerful. Together, they close the gap between identity context and credential control—linking intent, policy and approval to the actual secrets and tokens agents use at run time. Instead of treating identity and secrets as separate domains, Verify and Vault create a unified control plane for governing agent authority with real enforcement, not just documentation.

IBM Verify governs who a user and an agent are, what an agent is approved to do, and under what conditions the user and agent are approved to do it.  

HashiCorp Vault securely stores and brokers secrets, generates dynamic credentials on demand, enforces TTLs, renewal and revocation and provides centralized policy and audit logging. Thus, agents never need standing credentials hardcoded into code or workflows.

Together, they create something enterprises have never had for AI agents: provable control without human bottlenecks.

Regulators are already moving. Auditors are not satisfied with the traditional approach of human identities and lifecycle management; they are now looking for runtime process evaluations. They demand:

• Unique agent identities with strong access management controls
• Designs that consider human and machine identities together
• Full end-to-end audit trails
• Clear processes around just-in-time and just-enough access with revocation

Attackers are moving faster than defenders—targeting AI agents precisely because they likely operate with accumulated privilege and low oversight.

Agentic AI isn’t coming. It’s here. The question is no longer whether agents will execute business-critical actions—it’s whether enterprises can prove that those actions were authorized, scoped and accountable.

The organizations that invest now in deliberate governance will be ready: audit-ready by default, resilient under scrutiny, and able to deploy autonomy without fear. That’s how you turn agentic AI from a risk into a durable advantage.