This datasheet explains how higher education institutions can securely adopt AI while managing new risks like shadow AI, model poisoning, and data leaks. It emphasizes the need for full visibility across AI pipelines using AI-BOM capabilities to inventory models, services, and dependencies without agents. The solution focuses on identifying misconfigurations, prioritizing risks, and removing attack paths across data, identities, and infrastructure. It also highlights real-time threat detection and compliance monitoring to protect sensitive research data. The key takeaway is that institutions must embed security into AI development from the start—enabling innovation without compromising visibility, control, or governance.

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