NIST AI Risk Management Framework

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   AI governance is nearing the turning point data governance hit a decade ago

   A TechNews commentary argues that enterprise AI governance now mirrors the data governance phase of the early 2010s: departments deploy models without central review, SaaS vendors embed AI features, and agentic AI can reach unapproved data, so visibility is the first problem. It recommends building a continuously updated AI inventory, similar to a data catalog, that records every production model, agent and pipeline along with risk tier, data classification, owner and compliance status, and embedding controls into operations such as prompt-injection detection, pre-output sensitive-data detection, sandboxing agents and tamper-evident audit trails, then mapping those controls to NIST's AI risk framework, the EU AI Act and ISO 42001. The piece cites IDC projections that Asia-Pacific AI and generative AI spending will grow nearly fivefold from $73 billion in 2024 at a 38.4% compound annual rate, and a PwC 2026 survey finding that 31% of Taiwanese companies say their most-used AI tool can access all files and data while only 28% have responsible-AI and risk-management governance, below 51% globally and 54% in Asia-Pacific.

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