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The article focuses on the critical need for AI governance frameworks to address autonomous agents performing consequential work in regulated sectors like finance. It emphasizes that mere plausibility of output is insufficient; instead, systems must provide full traceability of data origins, decision approvals, and agent actions to maintain accountability. This shift highlights how fragmented data infrastructure exacerbates risks when AI autonomously executes high-stakes tasks.
AI governance moves closer to the workflow: theCUBE Insights at Amplify
AI governance now has to account for systems that perform work rather than merely assist employees. That shift is especially relevant for Workiva Inc., which operates in reporting, audit and compliance workflows where errors carry serious consequences. As companies introduce AI agents into finance and other regulated functions, plausible output is no longer enough. Businesses […] The post AI governance moves closer to the workflow: theCUBE Insights at Amplify appeared first on SiliconANGLE.
AI governance now has to account for systems that perform work rather than merely assist employees. That shift is especially relevant for Workiva Inc., which operates in reporting, audit and compliance workflows where errors carry serious consequences. As companies introduce AI agents into finance and other regulated functions, plausible output is no longer enough. Businesses must be able to trace information, identify approvals and reconstruct the actions…
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