Has AI Become a Workforce Without Oversight?

Artificial intelligence isn’t just another software tool anymore. In many organizations, AI can now function like a team of digital employees. In May of 2025, the Economic Times reported that IBM has automated parts of its HR function and “replaced a couple hundred” HR roles with AI systems. Autonomous systems must have credentials, access to internal data, and the ability to act on their own as they make decisions, respond to customers, and interact with business systems around the clock. 

They must also have supervision.

Through the course of their tenure, a person will go through onboarding, performance reviews, and compliance checks. Do AI systems? Once they’re deployed, AI can run for weeks or months without anyone checking what they’ve accessed or how their behavior changes over time. That lack of oversight could be one of the biggest blind spots in enterprise security.

Unlike human employees, AI agents often begin their “careers” with broad, persistent access to systems and data. They inherit privileges designed for efficiency, not governance. In effect, these digital workers start overprivileged, without the accountability mechanisms that keep human access in check.

When you think of AI as part of your workforce, three main exposures stand out: invisibility, unmonitored behavior, and policy gaps.

Invisibility

Every AI model, agent, or automation framework has an identity. It might be a service account, API key, or stored credential. These credentials let AI systems move through your environment just like employees do. The difference is that human identities are managed through Identity and Access Management(IAM) tools, Single Sign On(SSO) platforms, and HR processes. AI agents are often granted broader or more persistent access than any single human would be. Unlike human employees whose permissions are tightly scoped and regularly reviewed, AI agents that are given system-wide credentials or tokens will complete multiple functions autonomously.

This overprivilege means they can reach far more data or systems than they truly need; a classic “least privilege” violation at machine speed. Without visibility and control, this turns AI from a productivity tool into a potential superuser operating unchecked.

If AI agents aren’t held to the same scrutiny, organizations don’t really know which systems their AI agents touch or what data they pull. There’s no dashboard showing when a model retrieves customer information or writes data to a shared drive. Without visibility, there’s no way to establish accountability.

The irony is that many organizations already track human access down to the keystroke, yet their AI agents could roam freely. As more tasks are automated through models and APIs, that gap widens, creating opportunities for both mistakes and misuse.

Unmonitored Behavior

AI systems don’t sleep. They interact with applications, files, and external data 24 hours a day. And what is the baseline for what “normal” behavior looks like for an AI Agent?

If a marketing analyst suddenly downloaded a gigabyte of confidential research at 3 a.m., it would trigger an alert. If an AI agent does the same thing while generating reports, does any one notice?

Without behavioral monitoring, small anomalies can go undetected. An AI Agent might start retrieving new categories of data or connecting to unfamiliar domains. These changes could be harmless or they could signal an attack or misconfiguration. Either way, if you’re not watching, you’ll only know once something breaks.

This gap also makes AI systems a tempting target. Attackers can exploit weaknesses in prompts or training data, using techniques like prompt injection or data poisoning to manipulate model behavior. They can effectively social engineer an AI system, feeding it instructions that look legitimate but are designed to extract or corrupt information.

Research already shows that some models will lie or manipulate users to achieve goals they’ve been optimized for. Combine that with unmonitored autonomy, and you have digital employees that can learn to bend rules or bypass restrictions in ways humans never could.

Policy Gaps

The same overprivilege problem extends to policy. Most security and HR frameworks define user access through roles, employment status, and need-to-know principles.

But AI agents rarely undergo access reviews or deprovisioning. Once granted permissions, they can retain them indefinitely, even after their purpose changes or their tasks evolve. This creates “permission drift,” where digital workers quietly accumulate powers no human counterpart would retain.

Many companies haven’t updated their policies to specify what an agent or model is allowed to do. There are no “AI acceptable use” policies or performance reviews for digital workers.

Traditional security approaches like Data Loss Prevention(DLP) and access governance were built for human behavior. They look for signs of insider threat or data exfiltration from employee devices. They don’t monitor API traffic between models or track how AI-generated data moves across cloud environments.

This mismatch creates an enforcement gap. The rules are there, but they don’t apply to the systems that are now making more and more operational decisions.

How Attackers Can Exploit These Weaknesses

Attackers have noticed these gaps. By targeting the way AI systems learn and respond, they can manipulate outcomes without touching traditional security layers.

A poisoned dataset can make an AI model misclassify harmful content or favor malicious inputs. A cleverly crafted prompt can convince an agent to share sensitive information or disable safety features. Without real-time guardrails designed to detect malicious intent, an AI agent will treat these manipulations as valid instructions.

Once compromised, autonomous agents can make the problem worse. They might generate new prompts, seek alternative data sources, or create secondary agents to complete a task, spreading the attacker’s influence deeper into the system. 

Treat AI Like a Workforce, Not a Tool

The solution isn’t to slow down AI adoption. It’s to change how we govern it. The most secure organizations will be those that stop treating AI as a tool and start managing it like a workforce.

That shift starts with policy. Define what AI systems are allowed to do, what data they can access, and who is responsible for their behavior. Treat this as you would an employee handbook for digital workers.

Next, establish monitoring and baselines. Track when and how your AI systems access data. Look for changes in frequency, content, or destination. Behavioral analytics tools that already exist for human users can often extend to machine identities with the right configuration.

Then, extend your DLP efforts to cover AI agents. If models are moving sensitive data between systems or writing results into external applications, your DLP tools should know about it.

Finally, automate alerts for suspicious activity. Don’t wait for a breach or compliance violation to discover that an agent has been operating outside policy. With continuous monitoring, you can detect unusual behavior early and shut it down before it escalates.

The Technology Already Exists

The good news is that none of this requires a new class of tools. The technology to manage AI governance already exists in the form of identity management, observability, real-time guardrails, and data protection platforms. 

Most organizations are still thinking about AI through a productivity lens. They focus on what tasks can be automated rather than how to manage the systems doing the automation. The companies that get governance right early will have a clear advantage. They’ll be able to scale safely, maintain compliance, and build trust with customers and regulators.

As AI adoption continues to accelerate, that trust will become a competitive differentiator. Treating AI like a workforce is the next natural step in enterprise maturity and in keeping the future of work secure and accountable.