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AI agents, capable of performing multi-step tasks and autonomously writing and deploying code, are rapidly gaining traction within enterprise workflows. However, their autonomy raises critical concerns about security and control. To address these concerns, the Model Context Protocol (MCP) has emerged as a solution.
More than two years ago, OWASP warned about the potential for AI models to act like free agents with too much autonomy, a phenomenon known as Excessive Agency. This could lead to AI agents scheduling meetings, deleting files, or provisioning excessive, expensive cloud infrastructure.
MCP is designed to hold an agent's context together across tools and time, telling the coding assistant what it has done, what it's allowed to do, and what it should remember. By implementing strict permission scopes, memory boundaries, short-lived credentials, user consent checkpoints, and comprehensive audit trails, organizations can ensure security and control when using AI agents.
Critical concerns for secure AI agent usage include data sensitivity and leakage, authentication and authorization complexity, MCP server security risks, context poisoning and drift, rule violations and lack of visibility, and cross-boundary interactions. To mitigate these risks, organizations can employ data classification, redaction, DLP tools, sandboxing AI agents, and restricting access to unauthorized AI tools. They can also apply zero-trust principles, detailed permission scopes distinguishing agents, tasks, users, and temporal states, and use permission scopes and tool constraints encoded in MCP message formats.
In addition to these measures, best practices include training human users on safe and responsible AI use, establishing explicit data handling rules, real-time monitoring and observability tools, and sandboxing and access controls to restrict AI agent capabilities.
MCP-style architectures are vulnerable to prompt injection, command misuse, and memory poisoning, especially when shared memory is not adequately scoped or encrypted. As AI agents increasingly operate autonomously, with minimal human oversight, and interface with sensitive data and execute cross-functional workflows, governance, risk management, and strategic planning become crucial considerations.
In recent real-world examples, agents from software products like Microsoft Copilot and Salesforce's Slack product were vulnerable to being tricked into exfiltrating sensitive data. It's essential to establish agent privilege boundaries, govern shared memory, simulate attacks, and train employees for safe and effective use of AI agents.
The future of AI agents is here, and businesses that treat them as core infrastructure will thrive, while those that don't may be left dealing with messes or watching from the sidelines. To integrate AI agents securely, launching pilot programs, limiting autonomy, auditing shared memory and tool calls, simulating attacks, and training developers are key steps.
MCP is not just limited to coding tasks. It is being integrated into tools beyond coding, encompassing tasks like email triage, meeting preparation, sales planning, and document summarization. As AI agents make decisions and their actions in the world require serious consideration for safety and control, MCP is raising new questions, particularly about prompt injection resistance, command scoping, and token abuse.
In conclusion, ensuring security and control in AI agents via MCP involves embedding secure design principles, layering technical controls, and maintaining organizational policies and training. By addressing critical risks such as data leakage, agent misbehavior, context poisoning, and dynamic authorization challenges, businesses can harness the power of AI agents while minimizing potential risks.
An artificial-intelligence (AI) agent's ability to schedule meetings, delete files, or provision excessive cloud infrastructure, as warned by OWASP two years ago, highlights the need for the Model Context Protocol (MCP) to maintain security and control. MCP, designed to hold an agent's context together across tools and time, implements strict permission scopes, memory boundaries, user consent checkpoints, and comprehensive audit trails to minimize risks associated with AI agent usage, such as data sensitivity and leakage, authentication and authorization complexity, and cross-boundary interactions.
