Generative AI is evolving from answering questions to AI Agents that can autonomously access systems, use applications, and execute workflows. As AI shifts from providing answers to taking action, it also introduces greater cybersecurity risks.

This makes AI Governance increasingly important. It establishes policies, responsibilities, and risk controls to ensure AI is used securely and in compliance with organizational and regulatory requirements, covering areas such as data, access permissions, security controls, monitoring, and auditing.

Before deploying AI Agents, enterprises should assess not only their capabilities, but also three key questions: What data can they access? What permissions do they have? Is their operating environment secure?



How Are AI Agents Different from Generative AI?

Traditional generative AI typically receives a prompt from a user and generates text, code, or other content in response. AI Agents, however, can go a step further by connecting to internal enterprise systems, browsers, APIs, and other tools, allowing them to autonomously plan and execute tasks based on defined objectives.

This means that AI Agent risks are not limited to the AI model itself. They also extend to account permissions, endpoint devices, data access, and integrations with other tools. If permissions are improperly configured or vulnerabilities exist in the endpoint environment, the automation capabilities of AI Agents may amplify existing security issues.

Five Common Cybersecurity Risks of AI Agents

1. Sensitive Data Leakage

AI Agents may access corporate documents, source code, customer data, account information, and other sensitive resources while performing tasks. If access privileges are too broad or appropriate controls are not in place, sensitive information could be unintentionally accessed, processed, or transmitted.

2. Prompt Injection and Malicious Instructions

AI Agents may retrieve information from external sources such as websites, documents, and emails. Attackers can embed malicious instructions within this content to manipulate an Agent into ignoring its original rules, accessing unauthorized information, or even performing unintended actions.

3. Excessive Permissions

To enable AI Agents to complete tasks autonomously, enterprises may grant them permissions to access files, operate systems, or call APIs. If the principle of least privilege is not followed, a manipulated or misused Agent could significantly expand the potential impact of a security incident.

4. Unpredictable Autonomous Behavior

AI Agents can independently plan multiple steps to accomplish a task, but their execution paths may not always align with user expectations. Without appropriate restrictions and human approval mechanisms, incorrect decisions can quickly propagate through automated workflows.

5. Endpoint Environment and Configuration Risks

AI Agents ultimately operate on enterprise computers, servers, or other endpoint environments. If these devices contain vulnerabilities, insecure configurations, excessive privileges, or insufficient security controls, they may introduce new attack surfaces even if the AI Agent itself has not been directly compromised.


What Risks Should Enterprises Assess Before Deploying AI Agents?

AI Agent security cannot be fully addressed by a single product or tool. Instead, it should be incorporated into the organization’s existing AI Governance, identity and access management, data security, and cybersecurity management processes.

Before deployment, enterprises can begin by assessing the following questions:

  • Which endpoints are currently using or preparing to deploy AI Agents?

  • What corporate data and systems can these AI Agents access?

  • Have they been granted permissions beyond what is required for their tasks?

  • Do the endpoints running AI Agents contain vulnerabilities or high-risk configurations?

  • Has a security baseline been established, and can endpoint compliance with that baseline be continuously verified?

  • Do high-risk actions require human approval or other control mechanisms?

Build an AI Agent Security Baseline Through Endpoint Risk Assessment

Comprehensive AI Governance covers policies, data, models, permissions, auditing, and risk management, with endpoint security assessment serving as an important technical control. Before deploying AI Agents at scale, enterprises should assess their usage, configurations, and vulnerabilities to establish a security baseline and prioritize remediation.

TeamT5 ThreatSonar Plus helps identify AI Agent-related risks through endpoint, vulnerability, and configuration assessments, giving security teams greater visibility before deployment.

AI Agents offer powerful automation, but the key question is not only “Can we use them?” but “Can we use them securely?” Understanding endpoint risks, permissions, and configurations provides a stronger technical foundation for broader AI Governance.