The AI supply chain includes more than application code. It includes models, embeddings, APIs, and context providers. Each component can affect the behavior and security of the final system.
Before a component is integrated, its source should be verified. Secure deployment pipelines should be used, components should be scanned for vulnerabilities, and changes should be monitored continuously.
GitHub Advanced Security, Azure DevOps, and CodeQL are identified as tools that can support this work.
MCP also inherits the security posture of the environment in which it runs. A secure implementation therefore includes secure coding practices, hardened servers, multifactor authentication, regular patching, logging, and monitoring.
The OWASP Top 10 and OWASP guidance for large-language-model applications provide relevant security practices. Zero-trust architecture is also recommended so access is not assumed merely because a component is inside an enterprise environment.
Input validation remains essential. Empty strings, excessive lengths, invalid formats, and injection attempts should be rejected. Users should be authorized before protected resources are exposed.
Sensitive data should be redacted by default. It should be returned only when it is explicitly needed and the user is authorized to receive it.