AI Agents: How Your New Employee Brings More Security Risks

AI agents aren’t applications. They’re employees. So why are we treating them like applications?

AI agents don’t behave like classic applications. They access systems. They make decisions. They operate continuously. They interact with humans and other systems without being explicitly triggered each time. That’s not automation. That’s not scripts. That’s a digital worker. And yet, most organizations still treat AI agents like disposable applications—something you deploy, configure once, and forget. That mindset is going to create the next generation of insider risk.

If AI agents are closer in their behavior to humans than they are to applications, we need to think of the AI agent lifecycle in HR terms and apply the same checks and balances we have with employees. According to a 2026 Microsoft report, more than 80% of Fortune 500 companies employ AI agents. However, according to a 2025 Harvard Business Review report, only 6% of organizations trust AI agents to autonomously handle core end-to-end business processes.

We need to rethink how we hire, work with, and fire AI agents.

Hiring an AI agent

When you hire a human employee, you don’t skip due diligence. You run background checks. You verify credentials. You define their role clearly. You don’t give them access to every system on day one.

But when companies deploy AI agents, that’s often exactly what happens. Haven’t we been talking about Zero Trust for over a decade now? Why are we handing all the keys and permissions to a new employee (be it an AI one)? Excessive agency galore.

We spin them up quickly. We don’t question training data provenance. We don’t validate the model supply chain. We don’t clearly define what the AI agent should never do. And we frequently over-provision access “just to make it work.”

What are the consequences? You may have just hired a digital employee with unknown influences, unclear boundaries, and excessive privileges. If that AI agent is compromised, manipulated, or simply misaligned, the blast radius is enormous. Just like a bad hire in finance with unrestricted access can lead to insider risk, the damage caused by AI agents won’t be theoretical. It will be measurable.

We cannot ask “Will this AI agent be susceptible to prompt injection?” or “Will it hallucinate?” Without running the proper background checks, such as the underlying model and training data, we may end up with the AI agent performing tasks (based on its model, guardrails, restrictions, etc.) that do not align with what we want or need.

Working with an AI agent

Employees aren’t trusted blindly after onboarding. They’re supervised. Their performance is reviewed. Their access is periodically reassessed. If their behavior changes, someone notices it. AI agents need the same discipline.

AI agents are different from applications in that they accumulate permissions. They evolve based on input and environmental context. They can fail confidently and quietly. A human making a bad decision might raise eyebrows. An AI agent can make thousands of bad decisions before anyone looks.

If you’re not logging behavior, monitoring anomalies, reviewing access regularly, and assigning a named human owner, you’ve effectively created a privileged insider that never sleeps and never asks for guidance.

Furthermore, as a manager, I always want my team to continuously evolve and expand their knowledge. Sending them to conferences and online courses to learn is a top priority. We need AI agents to evolve and learn as well. But who is feeding them new data? Do they understand the context? Did we test to see if the information is used properly?

The consequence isn’t just security risk. It’s governance risk. Compliance risk. Reputational risk. When regulators or boards ask, “Who authorized this decision?” following up with “the model did it” is not a defensible answer.

Firing an AI agent

When an employee leaves, there’s a process. Access is revoked. Credentials are disabled. Responsibilities are reassigned. There’s closure. With AI agents? Not so much. AI agents keep running. Credentials remain active. API tokens don’t expire. And often, nobody can clearly answer who owns them.

That’s how shadow AI is born. AI agents are “ghost employees” with legitimate credentials and no supervision. The consequence is predictable: orphaned access becomes an attack vector. And because these identities are non-human, they’re often harder to detect in audits. with legitimate credentials and no supervision. The consequence is predictable: orphaned access becomes an attack vector. And because these identities are non-human, they’re often harder to detect in audits.

The bottom line

We already know how to manage risk at scale. We’ve been doing it for decades with people. Structured hiring. Active supervision. Formal offboarding. Clear accountability.

AI agents now sit inside our organizations making decisions and taking action. The mistake is pretending they’re just software. They’re not. They’re a new class of digital employee, and they need lifecycle governance to match.

The question isn’t whether AI agents should be treated as part of the workforce. The real question is: Do you know how many digital employees are working for you right now and who’s managing them?

For enterprises, it’s important that you get in front of security risks for AI agents. If you do that, you are better positioned to observe, orient, decide, and act on these risks.