AI Agents: The New Identity Governance Challenge for Shared Services

The AI conversation inside enterprises is moving beyond chatbots and generated content. The bigger question now is not only what AI says, but also what it is allowed to do. 

Organizations are beginning to deploy AI agents that can access business systems, retrieve records, call tools, update workflows, and support operations decisions. 

That creates a new governance challenge. AI agents are not ordinary software accounts. They behave more like active users, but without the judgment or accountability expected of human employees. 

This is why they should be governed as non-human identities (NHI), not treated as simple automation scripts or background integrations. 

AI agents may operate through service accounts, APIs, document repositories, ticketing systems, finance systems, HR tools, or procurement applications. To be useful, they need access. To be safe, that access must be tightly controlled. 

The issue is already visible. Reports show that 87% of enterprises use Microsoft Copilot. More than half of autonomous agents can access sensitive information, while 90% operate with excessive permissions. 

Some AI agents hold up to ten times the privileges they actually need. Other reports indicate that 80% of organizations have already experienced unintended AI agent behavior, including access to systems or data that were not properly authorized. 
This is not a secondary technical issue. It is an operating model problem. Poorly governed agents can create audit gaps, expose sensitive data, or approve actions outside policy. 

The practical question is simple: when an AI agent performs an action that looks legitimate in a system log, how does the organization know it was appropriate? 

Three Ways AI Agents Increase Risk 

The security community has begun mapping these risks in more detail, including through work such as the OWASP Top 10 for Agentic Applications. For enterprise operations, the clearest way to view the issue is through three practical patterns. 

1. Goal Hijacking 

Goal hijacking happens when an attacker influences what the agent tries to accomplish. 
The instruction may not come through a direct prompt. It can be hidden in a help desk ticket, a shared document, an email, or a knowledge base article. Because the content appears to be part of the task, the agent may treat it as trusted context. 

A ticket could contain hidden instructions telling an agent to reveal extra information, change a priority, skip a check, or call another tool. A workflow note could influence how a future case is handled. This type of risk is difficult to spot because the malicious instruction may look like ordinary business content. 

2. Abuse of Legitimate Access 

Many AI agent failures come from excessive trust rather than broken authentication. 

An agent connected to a service desk may retrieve user records, update tickets, or support access requests. A finance agent may pull invoice information or review payment status. A procurement assistant may search supplier records and contract data. 

Those capabilities are useful. They are also risky when permissions are too broad. An attacker may not need stolen credentials. Instead, they may manipulate the agent into using its approved access in an inappropriate way. 

That is why AI agents should not inherit wide permissions simply because a process spans multiple systems. Access needs to match the task, context, and sensitivity of the action. A support agent can summarize a ticket without being able to approve privileged access. A finance assistant can retrieve invoice status without changing payment records. An HR agent can answer policy questions without exposing employee data outside an approved process. 

3. Persistent Context Risk 

Some agents rely on memory, saved context, or knowledge sources. That makes them more useful over time, but it also creates new exposure. 

If the underlying data is manipulated, the agent may repeat the incorrect assumption in subsequent interactions. A corrupted knowledge entry could cause incorrect routing. A flawed workflow note could influence later cases. Bad context may lead the agent to ignore warning signs or apply an outdated rule. 

The risk increases when agents work together. One AI system may pass flawed context to another, creating a chain of poor decisions across teams or functions. Shared services environments are especially exposed here because work often moves between HR, finance, IT, procurement, and external providers. 

How Organizations Should Govern AI Agents 

The answer is not to slow adoption. AI agents can reduce repetitive work, improve response times, and help teams manage high volumes of requests. The real need is stronger governance. 

AI agents should be managed as powerful non-human identities. 

1. Assign Ownership 

Every AI agent needs a named business owner and a technical owner. Someone must approve its purpose, review its access, validate its data sources, and decide when it should be changed or retired. 

Without ownership, agents become another layer of hidden automation. That is already a problem with service accounts and bots. Agentic systems make this more urgent because they act quickly and may span multiple business functions. 

2. Limit Access 

Least privilege must apply to autonomy as well as data. 

Agents should receive only the permissions needed for a clearly defined task. High-risk actions should require human review and temporary access. That includes changes to employee records, financial data, access rights, vendor information, and policy-controlled workflows. Broad access may make implementation easier, but it weakens control. 

3. Put Controls Between Agents and Systems 

Agents should not call sensitive systems directly without a control point in place. A policy layer should evaluate what the agent is trying to do, which identity it is using, whether the action fits the assigned task, and whether approval is required. Written policies are not enough. Controls need to operate in the workflow itself. 

4. Monitor Behavior 

Standard access logs only show part of the picture. Organizations need to understand AI agent behavior over time. 

Useful signals include unusual data access, unexpected tool use, repeated failed actions, abnormal ticket updates, and other deviations from standard process flow. 

Audit teams should be able to answer basic questions: which agent acted, what system it touched, what data was involved, and whether approval was required. 

5. Validate Tools and Integrations 

AI agents depend on connectors, plugins, document sources, and business applications. Each component should be reviewed before it is included in a live workflow. Software supply chain security tools can extend that review by evaluating each AI agent dependency for unverified provenance and permission scopes that exceed the agent's defined task. 

Organizations also need an inventory showing what each AI agent can access, where it gets information, and which permissions it holds. Without that visibility, it becomes difficult to assess exposure after a flawed integration or compromised tool is discovered. 

Conclusion 

AI agents are becoming operational actors inside enterprise workflows. They access information, call tools, trigger actions, and influence decisions. The risk is about accountability, auditability, and process integrity. 

Organizations do not need to slow agentic automation. They need to govern it with the same seriousness applied to privileged users, service accounts, and critical business systems. Autonomy creates value only when authority is constrained. Otherwise, enterprises may automate the very blind spots they are trying to remove.