The sales ops team provides an AI agent with access to their CRM, and it helps in qualifying the lead, updating the records, and scheduling follow-ups. Now, AI can have access to customer records and share information with another AI tool from the marketing team. No single change looks serious enough to raise an alarm. The problem is that nobody has a complete picture of what the agent can access or everything it has been doing.
The challenge becomes harder as companies deploy more agents across functions. A permission that made sense when an agent was first deployed may no longer be appropriate months later.
Identity Governance is crucial for Agentic AI as discussed in this article.
Agentic AI Evolved Sooner than the Access Control Meant to Manage It
Most organizations did not design the access control keeping in view of Agentic AI because these were meant for people, applications, and conventional software.
The issue here is that organizations are deploying Agentic AI without rethinking their management of identity and access. Employees have defined roles assigned to them, but Agentic AI are built for one task and then used again for other tasks. Gradually, it gets difficult to address the question of what an agent is allowed to do.
Organizations are seeing this divide as they are shifting from piloting their AI technologies to deployment. They must define the access of the AI, the reason behind it, and the auditing process before the deployment of the technology.
Why Monitoring Matters as Much as Controlling Access AI Has
1. Small Permission Changes Creates Access Issues
Agentic AI gain new capabilities as teams connect them to additional tools. In the absence of continuous monitoring, an agent that starts off with restricted capabilities might eventually become more potent than intended.
Initially, a customer service agent had access to support tickets. Later, the agent is integrated into the CRM. Monitoring helps identify whether it still matches its role.
2. Agents Act Faster than Humans Can Review Them
An employee makes changes in an hour. The AI agent can perform several tasks within the same interval. The delay until the next review cycle results in identifying the problem very late.
An agent is instructed to clean duplicate customer records. A mistake in its instructions causes it to modify records. Monitoring can flag the unusual volume before the issue spreads further.
3. Monitoring Helps Detect the Influence between AI Agents
One agent can set in motion a workflow that is carried out by another agent. In such cases, the initial event creates an impact that goes beyond the initial system.
A marketing agent recognizes a valuable prospect and triggers a sales agent to generate an opportunity. Companies require this chain of events and not separate occurrences to be known.
Audit Issues Arise from Identity Governance Loopholes
1. Old Permissions Remain Active Even After the Role Changes
The original access is never reviewed. Over time, the agent can have accumulated privileges which it does not need anymore.
Action: Examine agent access and strip privileges that the agent no longer requires. It should be based on the role currently held by the agent and not on roles assigned before.
2. Audit Trail does not Show all the Information
The audit trail may show the execution of an action without specifying the AI agent executing the same. It will create issues for compliance reviews.
Solution: Companies should track down the activity done by the agent in the system it has accessed.
3. Agent-to-agent Activity Can Make Accountability Unclear
When an organization just considers the result, it could be difficult for them to pinpoint from where the process began and which agent started it.
Solution: AI agent auditing must indicate which agent started the task, which agents joined the process, and what they all did.
Building Agentic AI Identity Governance
1. List the Actions an Agent is Allowed to Perform
First, define the minimum permissions an agent needs to perform its tasks, rather than full permission.
The agent who qualifies leads requires privileges to modify CRM information and not delete or view any financial information.
2. Connect Access to the Agent’s Role
This is crucial in the case of agentic AI, which may take up new tasks later. Temporary permission added for a project should not become permanent.
If a marketing agent is given access to customer data for a campaign, that access should be reviewed or removed once the campaign ends.
3. Monitoring Activities from Agent to Agent
An agent could initiate another agent into performing a task. Companies need to track such a chain of events.
The sales agent acknowledges the prospect, activates the pricing agent to make an offer, and passes the information. All these actions should be accountable to the agents.
4. Regularly Review Access
Review the access periodically as well as whenever there is change in the agent’s function, inter-connected systems, or ownership.
Pose simple questions:
– Is the agent still requiring this access?
– Is the current owner still responsible for it?
– Has its role changed?
– Is it still being used?
5. Establish Guidelines for Inactive Agents
Sometimes, agents remain attached to the system even after completing the project. Set up an appropriate process for stopping those agents that are no longer needed.
An agent that has been created for a six-month customer migration project must not stay enabled beyond the end of that project.
Closing the Crisis Before It Produces a Serious Incident
Agentic AI is moving faster than the business’s ability to track and govern it. The problem does not arise because you are giving too much access, but rather because you lose sight of what this access can allow.
The issue is: In the case of an agent performing an action, will we be able to trace back to whom and why it did that? If not, then the problem must be addressed sooner rather than later.
