Prioritizing Risk in AI Agent Categorization

AI has now reached a new stage within enterprises. Initially, companies dabbled with AI through chatbots and copilots that could answer questions or provide summaries. However, the focus has shifted towards implementing AI agents capable of reasoning, planning, and taking actions across enterprise systems on behalf of users or organizations.

Unlike traditional automation tools, AI agents operate autonomously to pursue goals. They engage with systems, gather information, and execute tasks. This transition from merely answering questions to performing actions presents a new security challenge.

For Chief Information Security Officers (CISOs), the concern is not whether AI will be integrated into the enterprise, as it already has. The real challenge lies in identifying the types of AI agents present within the organization and understanding their associated security risks.

Enterprise AI agents generally fall into three categories: agentic chatbots, local agents, and production agents. Each category offers distinct operational capabilities and varying risk profiles.

The risk posed by AI agents is determined by their level of access and autonomy. The risk factor of an agent is influenced by two main factors: access, which refers to the systems, data, and infrastructure the agent can interact with, and autonomy, which pertains to the agent’s ability to act independently without human intervention.

AI agents that have limited access and require human oversight typically pose minimal risk. However, as access expands and autonomy increases, the potential impact and risk also grow significantly. An agent that simply reads documentation poses little threat, whereas an agent capable of modifying infrastructure, executing commands, or orchestrating workflows across multiple systems presents a substantial security concern.

In response to these challenges, Token Security offers a solution to manage the complete lifecycle of AI agent identities, reduce risk, and maintain governance and audit readiness without compromising speed.

Agentic Chatbots: The Entry Point for Enterprise AI

Agentic chatbots, the first category of AI agents, operate within managed platforms like productivity tools, knowledge systems, or customer service applications. These AI assistants are typically triggered by human interaction and assist in retrieving information, summarizing documents, or performing simple integrations.

While chatbot agents appear to pose relatively low security risks due to their limited autonomy and user-prompted actions, they introduce risks that organizations often overlook. Many chatbot tools rely on embedded API connectors or static credentials to access enterprise systems, potentially creating a privileged gateway into critical resources if not properly secured.

Local Agents: The Fastest-Growing Security Gap

Local agents, the second category, are rapidly becoming the most prevalent and least regulated type of AI agent. These agents run directly on employee endpoints and integrate with development environments, terminals, or productivity workflows to automate tasks such as code writing, log analysis, database queries, and workflow orchestration.

Unlike other agents, local agents inherit the permissions and network access of the user running them, allowing them to interact with enterprise systems as the user would. This model accelerates adoption but creates governance challenges as security teams often lack visibility into the actions and access levels of these agents.

Production Agents: Fully Autonomous AI Infrastructure

The third category, production agents, represent the most powerful AI systems that run continuously without human interaction. These agents are used for incident response automation, DevOps workflows, customer support systems, and internal business processes.

Production agents operate as enterprise services and rely on dedicated machine identities and credentials to access infrastructure and SaaS platforms. While they offer advanced capabilities, they also introduce security risks related to their high autonomy, processing of external inputs, and complex multi-agent architectures.

AI Agents Introduce a Significant Identity Security Challenge

In conclusion, AI agents represent a new set of first-class identities within enterprise environments, accessing data, triggering workflows, and making decisions using identities and permissions. Proper governance of AI agent identities is crucial to prevent security breaches and ensure that permissions align with the intended purpose of the agents.

Enterprises must adapt to the era of AI agents by understanding, governing, and aligning permissions with the identities of these agents. Token Security offers a solution to tackle agentic AI identity at scale, providing organizations with the tools needed to secure their AI agents effectively.

If you are interested in learning more about how Token Security can help with AI identity management, book a demo with our technical team today.