The Model Context Protocol (MCP) introduces a shift from static API integrations to dynamic, user-driven systems for large language models (LLMs). This evolution allows LLMs to interact more flexibly with external applications and data sources. However, this flexibility also exposes new security risks not adequately addressed by existing governance frameworks like NIST AI RMF or ISO/IEC 42001. Key threats include content-injection attacks, where malicious instructions are embedded in legitimate data; supply-chain attacks from compromised servers; and unintentional adversaries who misuse their roles. The article identifies these vulnerabilities and highlights how MCP can widen the attack surface through issues like data exfiltration and privilege escalation. To combat these risks, the authors propose practical security controls. Recommendations include implementing per-user authentication with scoped authorization, tracking the provenance of data across workflows, and using containerized sandboxing to prevent unvetted code from running unchecked. Inline policy enforcement, such as data loss prevention (DLP) and anomaly detection, is also suggested as a means to detect and mitigate potential threats. Centralized governance through private registries or gateway layers is essential for auditing actions and ensuring that tools are used within their intended scope. Adoption of MCP has surged among leading AI companies, with reports indicating a 50-70% reduction in time spent on routine tasks. Despite this rapid integration, many organizations struggle with inconsistent security practices. The article emphasizes a paradigm shift in security, noting that traditional methods like static code analysis fall short in the dynamic environment of AI agents, which adapt their behavior based on real-time data. This adaptability, while beneficial, also increases vulnerability to manipulation through malicious instructions embedded in user-generated content.