As organisations move beyond AI experimentation and begin deploying agents into everyday workflows, a new challenge is emerging.

Most businesses have established processes for managing employees. They know how to onboard users, assign permissions, review access, monitor activity and remove access when someone leaves.

Yet many organisations are now introducing AI agents without applying the same level of governance. This creates knock on challenges around ownership, access control, compliance and accountability particularly as AI adoption begins to scale.

As agents become more capable, they are no longer simply generating content or answering questions. They are accessing data, interacting with systems, automating decisions and carrying out business processes on behalf of employees. In many cases, they can access information across multiple platforms and operate with minimal human intervention.

The question organisations should be asking is not just "How many AI agents do we have?"

It's "Who is responsible for them?"

 The overlooked gap in AI governance  

The excitement surrounding AI has understandably focused on productivity.

Employees are using Microsoft Copilot to summarise meetings, draft communications and analyse information. At the same time, organisations are beginning to develop more specialised agents to automate processes, support business operations and streamline decision-making.

But while the conversation often focuses on what agents can do, less attention is given to how they should be governed.

For example:

• Who approved the agent?
• What data can it access?
• What actions can it perform?
• Who reviews its permissions?
• How is its activity monitored?
• What happens if its owner leaves the organisation?

These are not future challenges. They're governance questions organisations already face today.

 Treating agents as digital employees

One of the easiest ways to understand agent governance is to stop thinking about agents as software and start thinking about them as digital workers.

If a new employee joins the organisation, they typically go through a clearly defined process.

They are assigned an identity, granted access to the systems they need, provided with appropriate permissions and linked to a manager or owner. Their activity can be audited and reviewed. When they change role or leave the organisation, their access is updated or removed.

AI agents require the same discipline.

Without ownership and oversight, agents can quickly become another source of risk, creating security vulnerabilities, compliance concerns and operational blind spots.

 Visibility comes before control 

Many organisations have a surprisingly limited understanding of their existing AI landscape.

As adoption grows, agents may be created by different teams, departments or individuals. Some may be actively managed while others are forgotten altogether.

For years, organisations have battled shadow IT, unmanaged applications and unused accounts. Without proper governance, AI agents have the potential to become the next version of the same challenge.

Before organisations can govern agents, they first need visibility into:

• Which agents exist
• Who owns them
• What systems they are connected to
• What information they can access
• How they are being used

You cannot secure what you cannot see.

 AI governance starts with identity 

When organisations think about AI governance, the conversation often starts with compliance.

In reality, governance frequently comes back to identity.

An AI agent needs an identity in much the same way a person does. It requires permissions, access controls and clear accountability.

Questions that were traditionally asked about employees increasingly need to be asked about agents:

• Should this agent have access to sensitive information?
• Does it still require its current permissions?
• Has its owner changed roles?
• Is the agent still serving a legitimate business purpose?

This is why identity and access management are becoming critical components of AI strategy.

The more agents an organisation deploys, the more important it becomes to manage them consistently and at scale.

 Security and compliance don't disappear in an AI world 

Some organisations see agent governance as a separate initiative from security and compliance.

The reality is the opposite.

AI governance depends on a strong security and compliance foundation.

If data is overexposed, permissions are poorly maintained or sensitive information is inadequately protected, introducing AI will often make those issues more visible rather than solve them.

That's why technologies such as Microsoft Purview, Microsoft Defender and Microsoft Entra are becoming increasingly important in AI adoption strategies.

Together they help organisations understand what data exists, control who and what can access it, monitor activity and respond to emerging risks.

Without that foundation, it becomes difficult to deploy agents confidently at scale.

 From experimentation to accountability  

Many organisations are still early in their AI journey.

The first phase was experimentation.

The next phase is operationalisation.

As agents become embedded in business processes, organisations will need to move beyond asking what AI can do and start asking how AI should be managed.

The businesses that achieve the greatest value from AI are unlikely to be the ones that deploy the most agents. They will be the organisations that build the right governance, security and identity foundations around them.

Because ultimately, every AI agent should have the same things every employee has:

An owner. Defined permissions. Accountability. Oversight.

Without them, organisations risk creating a workforce they cannot fully see, manage or govern.