Vector databases, commonly used for enterprise AI search, can inadvertently expose privileged legal data. This occurs because semantic similarity search, a core function of these databases, can retrieve confidential information if its vector embedding is close to a user's query, regardless of traditional access controls. Furthermore, the ingestion process often strips security metadata from data chunks, and prompt-level authorization is insufficient to prevent data leakage. To address this, the paper proposes architecting identity-bound pre-retrieval filters and control towers to enforce security boundaries before data is accessed by the AI. AI
IMPACT Highlights critical security vulnerabilities in enterprise AI search, necessitating new architectural controls to prevent data leakage.
RANK_REASON The item is a technical paper discussing a security flaw in a specific type of AI system. [lever_c_demoted from research: ic=1 ai=1.0]
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