AI is entering a new phase. Enterprises have been experimenting with AI through chatbots and copilots that answered questions or summarized information. Now, the shift is toward implementing AI agents that can reason, plan, and take actions across enterprise systems on behalf of users or organizations.

Unlike traditional automation tools, AI agents pursue goals autonomously. They interact with systems, collect information, and execute tasks. This shift, from answering questions to performing actions, introduces a fundamentally new security challenge.

For CISOs, the question is no longer whether AI will be deployed in the enterprise. It already is. The real challenge is understanding which types of AI agents exist in the organization and where their security risks lie.

Most enterprise AI agents fall into three categories: agentic chatbots, local agents, and production agents. Each introduces different operational capabilities and very different risk profiles.

AI Agent Risk Is Driven by Access and Autonomy

Not all AI agents present the same level of risk. The true risk of an agent depends on two key factors: access and autonomy. Access refers to the systems, data, and infrastructure an agent can interact with, such as applications, databases, SaaS platforms, cloud services, APIs, or internal tools. Autonomy refers to how independently the agent can act without human approval.

Agents with limited access and human oversight typically pose minimal risk. But as access expands and autonomy increases, risk and the potential impact grow dramatically. An agent that reads documentation poses little threat.

An agent that can connect to business-critical services, modify infrastructure, execute commands, or orchestrate workflows across multiple systems represents a far greater security concern.

For CISOs, this creates a clear prioritization model: the greater the access and autonomy, the higher the security priority.

Agentic Chatbots: The Entry Point for Enterprise AI

The first category is the most familiar: agentic chatbots. These AI assistants operate inside managed platforms such as productivity tools, knowledge systems, or customer service applications. They are typically triggered by human interaction and help retrieve information, summarize documents, or perform simple integrations.

Enterprises increasingly use them for internal support, HR knowledge retrieval, sales enablement, customer service, and more productivity tasks. From a security perspective, chatbot agents appear relatively low risk.

Their autonomy is limited and most actions begin with a user prompt. However, they introduce risks that organizations often overlook.

Many chatbot tools rely on embedded API connectors or static credentials to access enterprise systems. If these credentials are overly permissive or widely shared, the chatbot becomes a privileged gateway into critical resources.

Similarly, knowledge bases connected to these systems may expose sensitive data through conversational queries.

Chatbot agents may be the lowest-risk category, but they still require strong identity governance and credential management.

Local Agents: The Fastest-Growing Security Gap

The second category, local agents, is rapidly becoming the most widespread and the least governed. Local agents run directly on employee endpoints and integrate with tools like development environments, terminals, or productivity workflows.

They help users gain efficiencies by automating tasks such as writing code, analyzing logs, querying databases, or orchestrating workflows across multiple services.

What makes local agents unique is their identity model. Instead of operating under a dedicated system identity, they inherit the permissions and network access of the user running them. This allows them to interact with enterprise systems exactly as the user would.

This design dramatically accelerates adoption. Employees can instantly connect agents to tools such as GitHub, Slack, internal APIs, and cloud environments without going through centralized identity provisioning. But, this convenience creates a major governance problem.

Security teams often have little visibility into what these agents can access, which systems they interact with, or how much autonomy users grant them. Each employee effectively becomes the administrator of their own AI automation.

Local agents can also introduce supply chain risk. Many rely on third-party plugins and tools downloaded from public ecosystems. These integrations may contain malicious instructions that inherit the user’s permissions.

For CISOs, local agents represent one of the fastest-growing and least visible AI attack surfaces because of their access and autonomy.

Production Agents: Fully Autonomous AI Infrastructure

The third category, production agents, represents the most powerful class of AI systems. These agents run as enterprise services built using agent frameworks, orchestration platforms, or custom code.

Unlike chatbots or local assistants, they can operate continuously without human interaction, respond to system events, and orchestrate complex workflows across multiple systems.

Organizations are deploying them for incident response automation, DevOps workflows, customer support systems, and internal business processes.

Because these agents run as services, they rely on dedicated machine identities and credentials to access infrastructure and SaaS platforms. This architecture creates a new identity surface inside enterprise environments.

The biggest risks arise from three areas:

• First, these agents often operate with high autonomy, executing actions without human review.
• Second, they frequently process untrusted external inputs, such as customer requests or webhook data, increasing exposure to prompt injection attacks.
• Third, complex multi-agent architectures can create hidden trust chains and privilege escalation paths as agents trigger other agents across systems.

AI Agents Introduce a Significant Identity Security Challenge

Across all three categories, one reality is clear. AI agents are a new set of first-class identities operating inside enterprise environments. They access data, trigger workflows, interact with infrastructure, and make decisions using identities and permissions.

When those identities are poorly governed and access is over permissioned, agents become powerful entry points for attackers or sources of unintended damage.

For CISOs, the priority should not simply be controlling AI agents, but gaining visibility and control of agents to understand:

• what agents exist
• what identities they use
• what systems they can access
• and whether their permissions align with their intended purpose.

Enterprises have spent the past decade securing human and service identities. AI agents represent the next wave of identities and they are arriving faster than most organizations realize.

Organizations that secure AI successfully will not be the ones that avoid adopting it.

They will be the ones that understand their agents, govern their identities, and align permissions with the intent of what those agents are meant to do. Because in the era of AI agents, identity becomes the control plane of enterprise AI security.

If you’d like to see how Token security is tackling agentic AI identity at scale, book a demo with our technical team.

Sponsored and written by Token Security.