Generative AI and machine‑learning systems are rapidly being embedded into core business processes, yet most enterprise AI initiatives fail to deliver sustained value. The problem is not model capability, but governance: organizations deploy opaque, high‑impact systems without clear policy guardrails, risk accountability, or an understanding of how AI decisions reshape human workflows. This gap creates systemic security, legal, and operational risks that cannot be addressed through technical controls alone.
This talk presents a structured, policy‑centric framework for operationalizing AI in business processes. Drawing on enterprise case experience and aligned with emerging standards such as the NIST AI Risk Management Framework, ISO/IEC 42001, and the OWASP Top 10 for LLM Applications, the framework evaluates AI proposals across four dimensions: business value, technical feasibility, risk management, and implementation sustainability. The approach explicitly integrates human decision‑making, organizational incentives, and governance responsibilities, highlighting where misplaced trust, automation bias, and unmanaged organizational change introduce new attack surfaces or failure modes.
Key findings show that AI risk concentrates at organizational boundaries rather than in models themselves: unclear accountability, insufficient change management, and misaligned incentives consistently undermine security and value. The framework reveals measurable indicators that distinguish high‑risk, low‑return deployments from sustainable, defensible AI adoption.
The significance of this work lies in reframing AI security as a policy and coordination challenge at scale. Rather than treating AI as a tooling problem, this talk provides security leaders and policymakers with practical criteria to govern AI deployments that affect people, institutions, and critical decisions—before failures become systemic.