AI agents are changing how organizations interact with software. Instead of simply responding to a prompt, an agent can access information, call APIs, use external tools, and complete tasks on behalf of a user.
That capability makes AI agents useful, but it also introduces a new security challenge.
When an AI system can take actions, an incorrect or manipulated decision can have consequences beyond an incorrect answer.
Why AI Agents Create a New Attack Surface
An AI agent can sit between users, language models, applications, APIs, databases, and external services.
Every connection creates another area that needs to be protected.
Depending on its purpose, an agent may be able to:
- Read internal documents
- Access APIs
- Retrieve customer information
- Send messages
- Modify records
- Execute workflows
- Interact with third-party services
If an attacker can influence the agent, they may be able to influence the actions it takes.
Prompt Injection and Untrusted Content
Prompt injection is particularly important for agentic applications.
An agent may consume information from websites, documents, emails, or other external sources. That information may contain instructions designed to manipulate the agent.
The risk becomes greater when the agent is allowed to act on those instructions.
For example, an attacker could place malicious instructions inside a document that an agent is expected to process. If the agent treats those instructions as trusted commands, it could perform an action that the user never intended.
Excessive Permissions Increase Risk
An AI agent should have only the permissions it actually needs.
An agent that only needs to read information should not automatically receive permission to delete records, change account settings, or execute administrative operations.
Least privilege is therefore an important security principle for agentic applications.
Security teams should review every tool available to an agent and determine:
- What information can it access?
- What actions can it perform?
- What happens if the action is triggered incorrectly?
Protecting Sensitive Actions
Not every agent action should happen automatically.
Sensitive operations may require additional authorization or human approval.
Examples include:
- Deleting important data
- Changing financial information
- Accessing sensitive records
- Sending external communications
- Modifying security settings
Adding controls around these actions can reduce the impact of an incorrect or manipulated decision.
Runtime Monitoring
Traditional application logs may show that an API request occurred, but they may not explain why an AI agent decided to make that request.
Security teams should therefore consider monitoring:
- User prompts
- Agent decisions
- Tool calls
- Tool parameters
- Data sources
- Authorization decisions
- Final actions
This creates a clearer picture of what happened when an agent behaves unexpectedly.
Final Thoughts
AI agents are becoming more capable, but greater capability also creates greater responsibility.
Organizations should treat agents as software systems with access to real business resources rather than simply as AI chat interfaces.
Strong permissions, controlled tool access, runtime monitoring, and human approval for sensitive operations can help organizations gain the benefits of agentic AI without giving autonomous systems unnecessary authority.
Security teams should evaluate emerging threats, understand their attack surface, and combine preventive controls with continuous monitoring.
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