AI-Powered Vector Databases: Security Vulnerabilities, Privacy Preservation Mechanisms, and Governance Frameworks

This paper has presented the first comprehensive security and privacy analysis of AI-powered vector database systems, identifying a set of novel vulnerabilities — embedding inversion, membership inference, adversarial query injection, and index reconstruction — that are specific to the vector data paradigm and are not addressed by conventional database security frameworks. Our evaluation of platform security capabilities revealed significant gaps between the current state of the art and the requirements imposed by emerging data privacy regulations. The governance framework proposed in this paper provides a practical starting point for organizations seeking to deploy vector databases in regulated environments while managing their compliance obligations responsibly. As AI systems increasingly rely on vector databases as their primary knowledge retrieval infrastructure, the security and privacy properties of these systems will have far-reaching implications for the trustworthiness of AI applications broadly. Addressing the challenges identified in this paper requires coordinated progress across cryptography, machine learning security, database engineering, and regulatory policy — a multidisciplinary research agenda of growing importance.

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