AI tools, particularly with Model Context Protocol (MCP) Servers, highlight the critical need for data context and user identity management to ensure security and compliance. MCPs facilitate data storage and retrieval, but they expose vulnerabilities when AI systems access information across diverse domains, such as finance, government, and healthcare. Organizations risk significant breaches if data is mishandled or accessed improperly. Effective strategies like ethical walls in financial firms and strict data classification in governments protect sensitive information but become complex when intertwined with AI. Continuous authorization and verification are essential throughout the AI tool chain to maintain data integrity and privacy. Each user must only access data within their authorized scope to prevent unauthorized exposure and misinformed outputs. To navigate these challenges, organizations need coordinated efforts across identity, infrastructure, and AI teams, emphasizing robust security measures and transparency regarding data completeness and freshness.
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