AI Agents Reveal Hidden Cybersecurity Risks in Business Networks

AI Agents Deployed on Business Networks Don’t Just Create Risk—They Reveal It

Artificial intelligence is changing how businesses operate. Companies are deploying AI agents to automate workflows, analyze information, write software, interact with applications, and perform tasks that previously required employees.

Unlike a traditional chatbot that mainly answers questions, an AI agent can also act. Depending on its setup, an agent might access files, talk to other systems, query databases, use APIs, run scripts, and handle tasks throughout a company’s network.

This ability can make businesses much more efficient. But it also brings up an important cybersecurity question: What happens when an AI agent starts working with the systems, data, and permissions already in your business?

Last week I gave a presentation to a room full of CFO’s and Controllers. At the conclusion of my presentation, a few of them asked me to write up and share my notes with everyone. This blog post reviews my presentation. Feel free to reach out if you have questions about how AI agents deployed on a business network don’t just create risk; they reveal it.

AI Agents Can Expose Security Weaknesses Traditional Security Reviews Miss

Most businesses have some form of cybersecurity program. They may use firewalls, antivirus software, endpoint detection, multifactor authentication, and vulnerability scanning. They may also conduct periodic penetration tests and IT risk assessments.

These controls are important, but they don’t guarantee that every way into a network is secure.

Business networks become more complex over time. Employees change roles, new applications are added, vendors get access, service accounts gain extra permissions, and old systems remain connected because the business still needs them.

It’s like a commercial building that has grown over 20 years. The original building may have had only a few entrances and clear access areas. After many additions, renovations, and ownership changes. A business might have hallways, maintenance doors, storage rooms, and access points that haven’t been checked in years.

The building may still have locks, cameras, and security guards. But that doesn’t mean every entrance is truly secure.

Business networks can have similar issues.

AI agents can help find these weaknesses because they interact with systems, follow links between resources, and check access paths that might not be obvious during normal work.

1. Excessive Permissions: AI Agents Reveal Who Has Too Much Access

One of the most common security problems in business environments is excessive permissions.

Employees frequently retain access to information and systems they no longer need. Someone who changes departments may keep access to files from a previous role. A service account created for one application may eventually receive additional permissions to support other systems.

These permissions can add up over time.

In cybersecurity, this is often called privilege creep.

Imagine giving an employee a keycard that opens the front entrance, accounting office, server room, executive offices, and records storage. The employee might only need access to the front entrance and accounting office, but the keycard opens everything.

This is what happens when a user account has more permissions than it needs.

Now, if you add an AI agent that can use that employee’s account or works through a service account with broad permissions, the agent might find information, access files, or use applications that the employee rarely touches.

The agent didn’t create the extra permissions. It just showed what those permissions allow.

This becomes especially important when AI agents have access to Microsoft 365, SharePoint, Active Directory, cloud platforms, databases, and internal applications.

A company might believe sensitive information is safe because employees aren’t supposed to access it. But security shouldn’t depend on what employees are expected to do. It should depend on what their accounts are allowed to do.

Businesses should regularly check permissions and follow the principle of least privilege. Each user and AI agent should have only the access needed to do their jobs.

I have previously written about identity and access weaknesses in How Do Attackers Exploit Active Directory?. The same underlying access-control problems can become even more important when autonomous systems begin interacting with business resources.

2. Poor Network Segmentation: AI Agents Reveal How Connected Your Network Really Is

Another common weakness is poor network segmentation.

Network segmentation means splitting a network into separate areas and limiting how they talk to each other. The goal is to stop someone who gets into one system from easily reaching everything else.

Think about a business office.

The reception area, accounting department, executive offices, and server room may all be inside the same building. But that does not mean everyone should be able to walk freely between them.

A well-designed building uses doors, keycards, and access rules to keep sensitive areas apart.

Network segmentation works the same way.

A properly segmented network may allow employees to access normal business applications while restricting their ability to communicate directly with servers, administrative systems, backups, or other sensitive resources.

Unfortunately, many networks have grown without a clear plan for segmentation. Systems that should be separate can talk to each other just because they share a network or have weak firewall rules.

An AI agent operating inside that environment may reveal these connections.

For example, an agent assigned to inventory internal systems might discover that a workstation can communicate with a server that stores sensitive financial information. It might also identify administrative interfaces or services that are reachable from areas of the network where they should not be available.

This doesn’t mean the agent has broken into those systems. It just means the network might allow more communication than the business intended.

The difference is important.

If a workstation gets infected with ransomware, poor segmentation can let the attacker move from that workstation to file servers, backups, and other business systems. If an AI agent finds those same paths during a security review, the company can fix them before an attacker uses them.

That’s one reason network security assessments and internal penetration tests matter, even as businesses adopt more advanced AI tools.

3. Exposed Service Accounts and Credentials: AI Agents Find the Keys People Forgot About

Many business applications communicate with other systems using service accounts, API keys, access tokens, and stored credentials.

These credentials let applications perform tasks without requiring an employee to sign in every time.

These are necessary for modern business, but they also create risks if companies don’t manage them carefully.

Consider a contractor who receives a key to a building to perform maintenance. Years later, the contractor is gone, but nobody remembers to collect the key. The key may still open doors that the contractor no longer needs to access.

This is similar to having an old service account or API credential that stays active even after it’s no longer needed.

AI agents can uncover these issues when they check application settings, development environments, scripts, shared folders, and connected services.

For example, an agent might find an API key saved in a configuration file or notice that several applications use the same service account. It could also spot credentials with too many permissions or ones that haven’t been changed in years.

These problems aren’t always brand new. Often, they come from weak credential management and not enough oversight.

The situation gets even riskier when agents have broad credentials of their own.

When several agents have the same API key, it’s hard to track which agent did what. If one agent is compromised or misused, an attacker could get all the permissions linked to that key.

Each AI agent should have its own identity, limited permissions, and secrets stored in secure vaults. Use short-lived credentials when possible. Also, log agent activity so security teams can see which agent accessed resources or took actions.

It helps to treat AI agents more like employees who access business systems, rather than just simple software features.

4. Misconfigured Applications and APIs: AI Agents Reveal Unintended Access Paths

Today, businesses rely on applications that communicate with each other through application programming interfaces, or APIs.

An API is like a controlled doorway, letting one application request information or perform actions in another.

For example, an accounting system might use an API to pull customer information from a CRM platform. An employee scheduling app could use an API to get payroll data. A customer portal might use an API to check account balances.

These connections make businesses run more efficiently, but they also create possible access paths.

Imagine a building with a service entrance for deliveries. This entrance should only let delivery staff reach the loading area, not the accounting office or executive suites.

If the entrance isn’t set up right, someone could get into areas they shouldn’t access.

APIs can face the same kinds of issues.

An application might check if a user is signed in, but not confirm if they’re allowed to see a specific record. Another API might let users access more data than necessary. Some APIs could even expose admin functions or sensitive information if their endpoints aren’t restricted enough.

AI agents working with these applications can help uncover unexpected access paths, especially if they’re allowed to test how the apps respond to different requests.

This becomes even more important as companies connect AI agents to their own systems and outside services.

Before giving an AI agent access to a business app, companies should know exactly what the agent can read, change, create, delete, and share. They should also make sure the APIs for those actions check authorization themselves, not just rely on the agent’s instructions.

Tanner discusses related application security concerns in its article about the importance of testing APIs during a penetration test. Businesses should not assume that an application is secure simply because its normal user interface appears to work as intended.

5. Weak Monitoring: AI Agents Reveal What Your Security Team Cannot See

A company might have strong security policies on paper but still miss things happening on its network.

For example, IT might know which employees are supposed to access sensitive systems, but not whether those employees or their apps actually do.

That’s why logging and monitoring are so important.

Think of a warehouse with security cameras. Installing cameras isn’t enough. Someone needs to watch the footage, check anything unusual, and act when needed.

Network monitoring works in a similar way.

Businesses need visibility into user activity, system connections, authentication events, app access, and other key security events.

AI agents add another layer since they can take actions without an employee starting each step.

If a company can’t tell what an employee does versus what an AI agent does, it may struggle to spot unusual behavior or investigate incidents.

For example, an agent might access hundreds of files, check several databases, or connect to different internal systems while doing a task. These actions might be legitimate, but the security team should know what happened, who did it, and whether it was within the agent’s allowed scope.

Businesses should give AI agents clear identities, centralize important logs, monitor for unusual behavior, and decide who reviews agent activity.

Visibility isn’t only about catching bad behavior. It’s also about understanding what your own systems are doing.

AI Agents Can Also Introduce New Risks

Even though AI agents can reveal existing weaknesses, that doesn’t mean they’re risk-free.

AI agents can create new attack paths because they combine software, credentials, data access, and independent decision-making.

For example, an agent could be tricked by harmful instructions hidden in a webpage, email, or document. This is called prompt injection.

A human assistant might notice the instruction is suspicious. An AI agent, depending on how it’s built and protected, might just follow it as part of its task.

This is one reason AI agents shouldn’t be trusted to set their own security boundaries.

Telling an agent not to delete files isn’t enough. You need technical controls that truly prevent the agent from deleting files.

The same principle applies to network access, credentials, and sensitive information.

Security controls should be outside the AI model. The agent should only have the permissions it needs, network connections should be limited, sensitive actions should require approval when needed, and the business should be able to stop or isolate an agent if something goes wrong.

Research from the Cloud Security Alliance on AI agents and enterprise security shows that autonomous agents can use persistent credentials and access many systems. This creates risks if permissions and oversight aren’t strong enough.

In short, AI agents can reveal old weaknesses and create new ones. A strong security program should address both.

One interesting opportunity with AI agents is using them for security testing.

Traditional penetration testing means security experts examine a company’s systems like an attacker would. They find weaknesses, check if they can be exploited, and explain what the impact could be.

AI agents could eventually help automate some parts of this process.

An agent could be instructed to map authorized systems, find accessible resources, review settings, and look for possible paths between systems. This helps our team find connections and weaknesses that would otherwise take a lot of time to uncover.

An AI agent might find a possible weakness without understanding its full business impact. It could also misunderstand the environment, make wrong assumptions, or do something that disrupts a business system.

That’s why AI-assisted security testing still needs a clear scope, technical safeguards, human oversight, and expert validation.

The best approach isn’t to replace experienced security professionals with AI agents. Instead, use AI to help them identify issues and improve efficiency, but keep human experts to validate findings, understand business risks, and recommend the right fixes.

What Business Leaders Should Do Before Deploying AI Agents

Businesses don’t have to avoid AI agents. They just need to know what those agents can access and how their actions are controlled.

Before rolling out AI agents across business networks, leaders should work with IT and security teams to understand the environment.

Start by identifying which AI agents are in use, who owns them, what business functions they support, and which systems they can access. Don’t forget to include agents set up by individual employees, development teams, business apps, and third-party vendors.

Next, check the permissions given to those agents. Don’t give an agent broad access just because it’s easier. Limit its access to only the systems and information it needs for its job.

Review how your network is segmented and how applications are accessed. Make sure systems with sensitive information are properly isolated.

Keep credentials and service accounts secure. Whenever possible, give each agent its own identity, limit its permissions, and verify you can turn off access when the agent is no longer needed.

Set up monitoring and accountability. Businesses need to know what their agents are doing, who is responsible for them, and how to stop them if something goes wrong.

These steps aren’t new ideas. They build on proven cybersecurity basics, just applied to a new kind of system.

How Tanner Security Helps Businesses Identify Hidden Security Risks

AI agents are changing how businesses use technology, but many of the weaknesses they reveal are long-standing cybersecurity problems.

Excessive permissions, poor network segmentation, exposed credentials, weak application controls, and inadequate monitoring have been problems for a long time.

What’s different now is how quickly and widely attackers can find and exploit these weaknesses.

Tanner Security helps businesses find and fix these risks by providing independent security assessments using the NIST AI Risk Management Framework and penetration testing.

An IT risk assessment reviews your company’s security controls, finds weaknesses in technology and business processes, and helps leaders identify which risks need attention.

Internal network penetration testing examines how an attacker could move through your network after gaining access through a compromised account. It can reveal too many privileges, unnecessary network access, weak segmentation, and other issues that AI agents might also find.

Web application and API penetration testing helps find weaknesses in the apps and interfaces that connect your business systems and could expose sensitive information.

If your company uses artificial intelligence, an AI risk assessment can show how your AI systems work, what information they can access, and whether the right security and governance controls are in place.

Learn more about Tanner’s related services:

• AI Risk Assessment Services
• NIST AI Risk Assessment
• Internal Network Penetration Testing
• Web Application Penetration Testing
• Red Team Penetration Testing

You can also read Tanner’s related articles:

• AI Is Finding Vulnerabilities Faster Than Companies Can Patch Them
• OpenAI and Hugging Face Security Incident: What It Teaches Businesses About AI Security
• A Guide to a NIST AI Risk Management Framework Risk Assessment
• How Do Attackers Exploit Active Directory?

The Bottom Line: AI Agents Are a Test of Your Existing Security

AI agents are more than just another software tool. They can connect with business systems, access sensitive data, and complete tasks much faster and more independently than traditional applications.

This creates new security challenges, but it also brings new opportunities.

If an AI agent discovers that an employee can access sensitive financial records, that an internal server is open to an unnecessary network segment, or that an application has an exposed API, it’s probably revealing a weakness that existed before the agent was used.

Businesses shouldn’t only ask if the AI agent is the problem. They should also consider why their environment allowed that kind of access in the first place.

A secure business network shouldn’t depend on employees missing excessive permissions, applications not exposing unnecessary data, or attackers not noticing a connection.

AI agents might reveal where the weaknesses are. Your security controls determine whether those weaknesses can actually be exploited.

If your business is starting to use AI agents or wants to know what an attacker could access after entering your network, contact Tanner Security to discuss an IT risk assessment or penetration test.