AI is entering a new phase. Businesses have been experimenting with AI with chatbots and copycats that answer questions or summarize information. Now, the shift is towards using AI agents that can think, plan, and take action across business processes on behalf of users or organizations.
Unlike traditional automation tools, AI agents pursue goals automatically. They interact with systems, gather information, and perform tasks. This shift, from answering questions to taking actions, presents a new security challenge.
For CISOs, the question is no longer whether AI will be used in business. It already is. The real challenge is understanding what types of AI agents exist in an organization and where their security risks lie.
Most business AI agents fall into three categories: agent chatbots, location agents, and production agents. Each presents different operational capabilities and very different risk profiles.
AI Agent Risk Driven by Access and Autonomy
Not all AI agents present the same level of risk. The real risk of an agent depends on two important factors: access and autonomy. Access means systems, data, and infrastructure that an agent can interact with, such as applications, databases, SaaS platforms, cloud services, APIs, or internal tools. Autonomy refers to how an agent can act independently without human consent.
Agents with limited access and human oversight generally pose less risk. But as access increases and autonomy increases, the risks and potential impact grow exponentially. A script-reading agent poses little threat.
An agent that cannot connect to critical business services, modify infrastructure, issue commands, or orchestrate workflows across multiple systems represents a significant security concern.
For CISOs, this creates a clear model of prioritization: the greater the access and independence, the greater the security priority.
AI agents create, deploy, and rotate identities at machine speed, bypassing traditional IAM controls.
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Agent Chatbots: The Gateway to Enterprise AI
The first category is the most familiar: agent chatbots. These AI assistants work within managed platforms such as productivity tools, information systems, or customer service applications. They are often activated by human interaction and help retrieve information, summarize documents, or perform simple compilations.
Businesses are increasingly using them for internal support, HR information retrieval, sales enablement, customer service, and other productivity functions. From a security perspective, chatbot agents appear to be relatively low risk.
Their independence is limited and most actions are initiated by the user. However, they present risks that organizations often ignore.
Many chatbot tools rely on embedded API connectors or static credentials to access business applications. If these details are overly permissive or widely shared, the chatbot becomes an exclusive gateway to valuable resources.
Similarly, databases connected to these systems may reveal sensitive data through conversational queries.
Chatbot agents may be the lowest risk category, but they still require strong identity governance and authentication management.
Local Agents: The Fastest Growing Security Gap
The second category, local agents, quickly becomes the most widespread and least governed. Local agents work directly on worker endpoints and are integrated with tools such as development environments, terminals, or production workflows.
They help users achieve efficiency by automating tasks such as writing code, analyzing logs, querying information, or planning workflows across multiple services.
What makes local agents different is their ownership model. Instead of operating under a dedicated system identity, they inherit the permissions and network access of the user you are running. This allows them to interact with business systems in the same way that a user would.
This design greatly accelerates discovery. Employees can quickly connect agents to tools like GitHub, Slack, internal APIs, and cloud environments without going through a centralized identity provisioning. But, this simplicity creates a major governance problem.
Security teams often have little visibility into what these agents can access, what programs they interact with, or how much autonomy they provide to users. Each worker becomes the administrator of their own automated AI.
Local agents can also introduce supply chain risk. Many rely on third-party plugins and tools downloaded from the community ecosystem. This compilation may contain malicious instructions that inherit user permissions.
For CISOs, local agents represent an AI attack surface that is growing rapidly and is increasingly invisible due to its reach and autonomy.
Productive Agents: A Fully Autonomous AI Infrastructure
The third category, production agents, represents the most powerful category of AI systems. These agents run as business services built using agent frameworks, orchestration platforms, or custom code.
Unlike chatbots or virtual assistants, they can operate continuously without human interaction, respond to system events, and orchestrate complex workflows across multiple systems.
Organizations deploy response automation, DevOps workflows, customer support systems, and internal business processes.
Because these agents operate as services, they rely on dedicated machine ownership and credentials to access infrastructure and SaaS platforms. This building creates a new place of ownership within the business premises.
The biggest risks come from three areas:
- First, these agents often operate with a high degree of autonomy, performing actions without human review.
- Second, they often process untrusted external input, such as customer requests or webhook data, which increases exposure to injection attacks quickly.
- Third, complex multi-agent architectures can create hidden trust chains and privilege escalation mechanisms as agents initiate other agents across systems.
AI Agents Present a Significant Identity Security Challenge
In all three categories, one fact is clear. AI agents are a new set of first-class identities operating within enterprise environments. They access data, trigger workflows, interact with infrastructure, and make decisions using ownership and permissions.
When those identities are poorly managed and access is allowed too much, agents become powerful entry points for attackers or sources of unintended damage.
For CISOs, the priority should not be controlling AI agents, but gaining visibility and control over agents to understand:
- what agents are there
- what identities do they use
- what programs they can access
- and that their permissions are consistent with their purpose.
Businesses have spent the last decade protecting people’s identities and services. AI agents represent the next wave of ownership and are arriving faster than most organizations realize.
Organizations that successfully defend AI will not be the ones that shy away from embracing it.
They will be the ones who understand their agents, manage their identities, and manage the permissions and intent of what those agents say. Because in the era of AI agents, ownership becomes the control plane of business AI security.
If you’d like to see how Token Security addresses agent AI ownership at scale, book a demo with our technical team.
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