Agentic AI is transforming how work gets done. These autonomous systems plan, decide, act, and improve without requiring human input at every step. They’re already being deployed in industries like healthcare, banking, and government, delivering faster services and smarter operations.

But the more power we give these systems, the more critical it becomes to rethink something foundational: Identity. Who, or what, is taking action in your digital environment? And how do you manage and control that access?

This article explores why identity management must evolve alongside Agentic AI, the new risks that emerge, and how companies can build access systems that are safe, accountable, and built for the future.

Why Agentic AI Is Different

Traditional automation works on static rules. For example, “If this condition occurs, then perform that action.” It’s predictable but limited. Agentic AI, in contrast, is goal-oriented. It can break down complex tasks, make context-aware decisions, and interact with multiple systems to complete its objectives without explicit step-by-step instructions.

Consider a few examples of what Agentic AI can do:

• A cybersecurity agent monitors login activity and locks an account after detecting suspicious behavior. It doesn’t just alert, it acts.
• In finance, an agent can identify fraudulent transactions in real-time and block them before damage occurs.
• In healthcare, an agent can analyse patient data, detect anomalies, and schedule urgent care autonomously.

These systems don’t tire, don’t forget policies, and don’t require retraining. They can scale rapidly from one to thousands instantly. That makes them powerful, but also introduces new risks when governance is not in place.

The Identity Gap

The current identity systems we use things like usernames, passwords, and static roles were designed for humans. They assume a human understands the implications of their access and actions. But with Agentic AI, that assumption breaks down.

Here’s why:

• The “user” behind the action is no longer human, but a machine.
• Actions are executed faster than any human can monitor in real time.
• These agents may perform multiple roles, switch tasks on the fly, or interact with high-privilege systems.
• Some may even inadvertently escalate their own access levels due to poorly designed permission boundaries.

Without new approaches to identity, organisations risk losing visibility and control over who or what is doing what in their systems.

A Practical Risk: The Confused Deputy Problem

One of the most well-known and urgent risks in this space is the Confused Deputy Problem. This occurs when an AI agent uses a system that has more privileges than it should, effectively gaining access it was never explicitly granted.

For example:

• A chatbot sends a request to a backend system to retrieve user data.
• That backend system has wide-ranging access privileges.
• Because of how the integration works, the chatbot now has the ability to perform actions beyond its original scope.

This isn’t intentional. It’s a byproduct of poorly defined trust boundaries. But it’s dangerous, especially when no one is monitoring these interactions in real time.

What Needs to Change: Identity by Design

To manage Agentic AI safely, identity can no longer be treated as an afterthought. It must be designed into the architecture from day one.

Here are the key principles that organisations should implement:

• Every AI agent should have a unique digital identity, just like a human employee. This ensures traceability and accountability.
• A designated human owner must be responsible for each agent’s behaviour, lifecycle, and access.
• Agents should be granted only the minimum access they need to do their job following the principle of least privilege.
• All actions performed by agents should be logged in detail, enabling audits and forensic analysis.
• Access must be revocable in real time. If an agent behaves unexpectedly or if risk levels change, its access should be shut off immediately.

Quarterly access reviews or periodic audits are no longer sufficient. Oversight needs to happen continuously, at machine speed.

How to Earn Trust in a Machine World

Humans earn trust through behaviour over time. Machines must now do the same, using digital signals and monitoring to demonstrate safe, predictable actions.

Future identity systems will likely incorporate several adaptive controls:

• Trust scores will track how safely an agent behaves. These scores could rise or fall based on historical actions, policy adherence, and risk metrics.
• Permissions could adjust dynamically. For example, if an agent consistently operates in low-risk environments, its access might be broadened. If it behaves unusually, its access would shrink immediately.
• Real-time monitoring is essential. If an agent begins making unfamiliar or high-risk requests, systems should automatically trigger alerts or lockdowns.

Trust must be earned, not assumed. AI systems need digital mechanisms to prove they can be trusted to act independently.

A Roadmap for Identity Management in the AI Era

As organisations prepare for widespread Agentic AI adoption, here’s a practical roadmap to build identity systems that are future-ready:

1. Inventory your agents: Begin by identifying all autonomous systems in use. Understand what they do and what access they require.
2. Assign human ownership: Every agent must be linked to a responsible human who can manage risk, compliance, and lifecycle.
3. Implement least privilege: Review and minimise permissions for each agent. Ensure no agent has more access than necessary.
4. Deploy continuous monitoring: Use tools to log actions, detect anomalies, and track behaviour patterns.
5. Build rapid-response systems: If something goes wrong, ensure you can revoke access or shut down an agent within seconds.

This is not just a technical transformation. It’s an operational and cultural shift requiring cross-functional collaboration between IT, security, compliance, and business leadership.

Early Wins and Clear Risks

Agentic AI offers enormous benefits when deployed responsibly:

• Governments can streamline citizen services, reducing wait times and paperwork.
• Financial institutions can detect and block fraud in real time, protecting both revenue and reputation.
• Healthcare systems can act proactively, identifying patient risks and automating care responses.
• Retailers can personalise customer experiences, improving engagement and loyalty.

But the risks are equally significant:

• A mistaken configuration or flawed AI decision could impact multiple connected systems.
• Sensitive personal or business data could be exposed in milliseconds.
• Autonomous decisions without oversight can trigger cascading failures.

Every gain comes with an equal need for control.

Final Thought: Autonomy Requires Control

Agentic AI is no longer science fiction. It’s being adopted now. It offers speed, accuracy, and scalability at levels humans alone can’t match. But to harness that power safely, we need to rethink how we define, manage, and secure identity in digital systems.

If organisations build strong identity systems rooted in accountability, real-time monitoring, and access control, AI agents can be trusted members of the digital workforce.

If not, we risk handing over control to systems we don’t fully understand and can’t easily stop.

“The future is autonomous. But trust still depends on access, identity, and human accountability.”