Beyond the hype: The hidden security risks of AI agents and MCP

As AI rapidly evolves from a novelty to a necessity, businesses across every industry are feeling the pressure to integrate it into their operations, products, and services. What was once a forward-looking initiative has now become a critical component of staying competitive in a fast-changing market. 

AI experimentation is no longer driven by enthusiastic technologists or curious stakeholders—it’s now a strategic imperative. A significant component of this transformation is the use of AI agents: intelligent systems designed to autonomously perform tasks, make decisions, and adapt to changes in information.

Here, we’ll define what AI agents are, introduce MCP (Model Context Protocol), and dive into the security risks that come with these emerging technologies.

AI agents, defined: The brains behind autonomous applications

AI agents are applications where a Large Language Model (LLM) drives decisions, coordinates tasks, and adapts to changing inputs in real time.

A true shift occurs when AI agents are equipped with the tools and services they need to interact with the digital world. Whether it’s querying a database, sending a message, updating records, or triggering entire workflows, this tool access transforms AI into an autonomous process.

One of the most promising enablers of this evolution is the MCP. Introduced by Anthropic in November 2024, MCP is an open, emerging standard that simplifies how AI agents connect to tools and data sources. It’s earning widespread attention for doing what the USB standard did for hardware peripherals: replacing complex, one-off integrations with a universal interface.

By standardizing tool access, MCP empowers AI agents to execute dynamic, context-aware tasks across platforms. 

How does MCP work?

MCP uses a familiar client-server architecture to standardize how AI agents interact with external tools and data sources. This protocol ensures consistent and reliable communication between the agent and the resources it needs to function effectively.

This setup places MCP clients within the host application, whether that’s an AI assistant, a coding environment, or any other AI-enabled application. It performs the function of managing communication with an MCP server. As part of this process, the application and connected tools must negotiate protocol versions, discover available capabilities, and transmit requests and responses between them.

What makes MCP unique is that these capabilities are described in natural language, thereby allowing them to be directly accessed by the LLM driving the AI agent. This enables the model to understand which tools are available and how to use them effectively.

The server uses URI-based patterns to manage access to its resources and supports concurrent connections, enabling multiple clients to interact with it simultaneously. This makes the MCP highly scalable, flexible, and well-suited for complex agentic environments.

Autonomous vs. delegated identity: A crucial distinction

As AI systems become more embedded in business and everyday life, defining and managing AI identities is becoming increasingly important. Two key models are emerging: autonomous AI identity and delegated (on-behalf-of, or OBO) identity.

Autonomous AI identity refers to an agent that operates independently, making decisions and taking action without the intervention of a human in real time.

In contrast, a delegated identity represents an AI that performs tasks under the direction of a human. Understanding this distinction is crucial for maintaining proper accountability and security in AI-powered systems.

It is important to note that both models influence how authorization is managed by systems. A failure to differentiate these roles can result in over-permissioned systems, security risks, or misattribution of actions.

Visibility & control: The missing pieces

Real-time monitoring is essential for detecting and responding to anomalous behaviors in AI agents, especially as they operate autonomously and make decisions without human oversight. Just as important is robust identity management that clearly distinguishes between non-human identities (NHIs), which represent fully autonomous agents, and delegated identities, in which an agent acts on behalf of a human user.

Tagging each action with the correct identity context allows security teams to enforce least-privilege access, audit agent behavior against user delegations, and maintain clear accountability. Furthermore, tool-specific audit trails provide detailed records of every API call, data access, and action performed by an AI agent. As a result, these logs are essential for forensic investigations and compliance audits and should be integrated with existing SIEM systems to correlate agent activity across environments and detect suspicious activity.

As protocols like MCP expand tool integration capabilities, security frameworks must evolve in parallel, introducing dynamic authorization, continuous monitoring, and adaptive policy enforcement to manage increasingly capable agents. The combination of detailed audit trails and identity-aware monitoring will be critical to maintaining control, visibility, and trust as AI agents become more embedded in core operations.

Prepare now for secure AI adoption

As MCP rapidly gains traction as a standardized framework for integrating AI models with external tools and data sources, it’s reshaping how AI systems interact with applications. This unlocks more dynamic and context-aware capabilities. The rapid adoption of this technology, however, has outpaced the development of mature security controls, exposing potential risks such as unauthorized access, data leakage, and compromised tool integrity.

To address these concerns, organizations are encouraged to take proactive steps:

• Audit current MCP usage or plans: Assess how MCP is currently implemented within your systems or how it’s planned to be integrated.
• Enhance visibility and standardize authentication: Implement standardized authentication protocols and ensure comprehensive identity tracking to monitor interactions between AI models and external tools.
• Foster collaboration between engineering and security teams: Encourage cross-functional teams to work together in developing and enforcing security policies tailored to MCP implementations.

Securing the future of AI agents

The use of AI agents is unlocking unprecedented efficiency and intelligence across applications – automating tasks, streamlining workflows, and enabling real-time decision making. But with any rapid advancement comes risk. 

To stay ahead of emerging threats, prioritize auditing MCP deployments and implement standardized authentication protocols to establish a secure baseline. Then build a comprehensive AI identity security strategy by leveraging third-party security tools to protect your systems as agents grow more autonomous and deeply integrated into core business operations.

Remember that security isn’t static—it must evolve with your AI stack.

 

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