Topic

Vendor Management

Vendor management in AI governance refers to the processes and controls organizations implement to evaluate, monitor, and manage third-party AI providers and suppliers throughout their lifecycle. This is critical for enterprises because external AI vendors introduce risks around data security, model reliability, regulatory compliance, and intellectual property that must be actively overseen through contractual obligations, audits, and performance assessments. Strong vendor management ensures organizations maintain visibility into how their AI systems are developed and maintained while protecting against supply chain vulnerabilities and vendor lock-in situations.

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ResearchGlobal2025-05-30

Shadow AI and Client-Side Widgets Create an Inventory Gap That Existing Controls Do Not Cover, Kiteworks Analysis Finds

Kiteworks published a research piece on May 30, 2025, framing the central AI governance challenge as an architecture and visibility problem rather than a policy problem. The analysis identifies shadow AI deployments, embedded client-side scripts, third-party AI widgets, and fragmented controls as the primary blind spots undermining enterprise AI oversight. It recommends continuous inventory, Content Security Policy and script allowlists, third-party AI monitoring programs, joint incident response planning, and treating AI widgets as data processors under applicable privacy frameworks.

shadow AI third-party risk data privacy AI inventory vendor management