Reason, Plan, Act: Putting AI Agents to Work in IAM

Today, the term AI Agent is gaining traction, but what exactly does it mean? It signals a move beyond AI that simply processes prompts to systems designed for autonomy and action.

Unlike standard applications or even sophisticated LLMs, an AI Agent possesses the core capabilities to reason through problems, plan effective solutions, and utilize a toolkit of functions or APIs to interact with the environment. This capacity for independent operation makes them particularly powerful.

Could AI Agents be the key to truly dynamic IAM?

What is an AI Agent? More Than Just a Smart Chatbot

First, let's clarify what we mean by an AI agent. It's more than just a system that responds to prompts. An AI agent is designed to be autonomous. It possesses the ability to:

1. Reason: Analyze a goal or a situation;
2. Plan: Break down the goal into actionable steps;
3. Act: Execute those steps by interacting with its environment.

Crucially, AI agents are equipped with a set of tools.

These aren't physical tools but rather functions, API calls, database queries, or other capabilities that allow them to interact with other systems and data sources. The agent autonomously decides when and how to use these tools based on its reasoning process to achieve its objective.

Agent vs. Prompt Chain: The Key Difference is Autonomy

You might wonder how this differs from simply chaining prompts together.

While you can create sequences where the output of one LLM prompt feeds into another or even use LLM outputs to generate code (like SQL queries) for external interaction, this approach lacks the core element of agentic AI: autonomous reasoning and tool selection.

• Prompt Chaining: Follows a more pre-defined path. The interaction logic is largely determined by the human designer chaining the prompts.
• AI Agent: The LLM is in the driver's seat. It assesses the situation, reviews its available tools, decides the best course of action, executes it, observes the result, and then reasons about the next step. It has a holistic view and control over the process.

Applying Agentic AI to IAM: A Workflow Revolution

Now, let's bring this concept into the world of IAM. Consider the often complex and rigid nature of access request management and workflow implementation in traditional systems. Agentic AI offers
a paradigm shift.
Imagine defining complex access workflows using natural language. Instead of clicking through configuration menus, you could simply instruct the system:

“Access requests for resources classified as "High Risk" require sequential approval. First, approval must be obtained from the requestor's direct manager. Second, if the manager approves,
approval must be obtained from the designated Application Owner. Third, if the Application Owner also approves, final approval is required from the relevant Entitlement Owner associated with the
specific access being requested”.

An AI agent specifically designed for IAM could then:

1. Understand & Structure: Parse this natural language request, understand the intent, and structure the necessary workflow steps.
2. Reason & Verify: Initiate a reasoning process. Who is the requestor? Is the request compliant with existing policies? Who is the designated manager?
3. Utilise Tools: Use its available tools (API calls, directory lookups) to:

• Verify the new requestor's details.
• Identify the correct manager.
• Check relevant security policies related to the requested access.
  

4. Act & Provision: Once all checks pass and approvals are secured, use another tool like an API call to your existing IGA platform to actually trigger the provisioning of the requested access rights.
5. Report: Confirm completion or report any issues encountered.

Integrating Human Oversight: The Human-in-the-Loop

Of course, not all IAM decisions can or should be fully automated.

Many processes, particularly those involving high-risk access or periodic reviews, mandate human judgment and explicit approval.

This is where the concept of "human-in-the-loop" becomes essential, and agentic AI is well-suited to accommodate it.

Imagine an access review campaign or our previous high-risk access request workflow. When the AI agent reaches a pre-defined step that requires human intervention (like manager certification), its reasoning identifies this requirement. Instead of proceeding autonomously, the
agent pauses the workflow. It can then utilize its tools to notify the designated human approver through appropriate channels (e.g., email or a notification in the IGA platform), providing them with the necessary context.

The workflow remains suspended until the human provides their decision (approve, deny, revoke). Once the input is received, the agent resumes the workflow, incorporating the human decision into its next steps – either proceeding with provisioning/attestation, escalating, or closing the task based on the feedback received, and logging the entire interaction for audit purposes.

This seamless integration ensures that automation boosts efficiency without bypassing critical human control points and compliance requirements.

Why This Matters for IAM

• Efficiency: Automates complex decision-making and actions, speeding up processes.
• Flexibility: Allows for dynamic workflows defined more easily.
• User Experience: Enables natural language interfaces, simplifying tasks for managers and end-users.
• Intelligence: Enhances workflow reasoning by incorporating natural language understanding and semantic analysis, enabling the agent to interpret complex instructions, policies, and context accurately during execution.

Conclusion

Agentic AI represents a significant step beyond simple LLM interactions. By granting AI the ability to reason, plan, and autonomously use tools, we unlock powerful new possibilities. In the IAM space, this could lead to more intelligent, efficient, and user-friendly ways to manage identities and access, transforming complex processes like workflow management into dynamic and flexible
operations. The future of IAM looks increasingly autonomous and intelligent.