The Next AI Governance Problem Is Identity, Not Intelligence
•InnovationThe Next AI Governance Problem Is Identity, Not IntelligenceByRishi Katdare,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated u...
•Opinions expressed are those of the author.
•| Membership (fee-based)May 29, 2026, 10:45am EDTRishi Katdare, Senior Leader in Networking and Edge for Global Financial Services at Amazon Web Services.
هذا الخبر من Forbes. خبر يقدم أدوات ذكاء اصطناعي للتلخيص والترجمة والاستماع.
InnovationThe Next AI Governance Problem Is Identity, Not IntelligenceByRishi Katdare,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 29, 2026, 10:45am EDTRishi Katdare, Senior Leader in Networking and Edge for Global Financial Services at Amazon Web Services. gettyWhen I talk with leaders about AI governance, the conversation still centers on model behavior. Hallucinations, bias, data leakage, safety testing and regulatory exposure all matter. Yet as AI systems act across the enterprise, another question becomes harder to avoid. Who or what acted, under whose authority, against which system, and how long will the proof remain defensible?The next consequential AI failure may not come from a model producing an unreliable answer. It may come from an enterprise being unable to prove that an AI-enabled action was authorized, attributable, properly constrained and auditable.The Trust Layer Leaders MissWhen a system routes a request to an approved model, the organization assumes the artifact in production is the one teams reviewed. When a model uses enterprise data, leaders assume the lineage is understood, licenses are respected and sensitive information stays where it belongs. When an incident occurs, they assume logs, access records and deployment history can reconstruct what happened.In my experience, governance reviews often examine model behavior while treating service identities, agent permissions and evidence retention as implementation details. That is where the exposure begins. Those assumptions were easier to manage when enterprise systems were built around human users and static applications. They become fragile when nonhuman actors can initiate transactions, update records, change configuration, recommend approvals and operate across system boundaries. If you give an AI agent authority to act, you have not mer...المصدر: Forbes | Source: Forbes
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This article was originally published by Forbes. Khabr is a licensed Jordanian AI-powered news platform (Registration #82086). We add editorial value through: AI-powered news analysis, automated summaries, AI audio narration, multi-language translation (Arabic, English, French, Turkish), and AI fact-checking. Our mission is to make news more accessible and understandable for Arabic-speaking audiences worldwide.



