FinOps Strategies for AI-Enabled Real-Time Compliance Platforms in Cloud Native Environments

Abstract

This study investigates FinOps strategies for AI-enabled real-time compliance platforms in cloud-native environments. It begins with an analysis of FinOps foundations, covering essential principles, and continues by examining AI-enabled compliance concepts and requirements. Next it identifies architectural requirements, focusing on data mesh, observability, policy as code, and continuous compliance. Cost management and optimization strategies follow, addressing resource profiling for real-time AI workloads, billing-aware scheduling and autoscaling, and payment schemes for third-party compliance services. Finally, security, privacy, and regulatory aspects are explored, covering data residency and encryption, compliance-driven access controls and auditing.

FinOps is an essential discipline in cloud-native development and operations. Business value is generated and consumed with every transaction, making transparent and geared operations vital. Most development and operations activity is transferring, managing, and operating workloads on external services. To continue to use cloud services and avoid the risk of unpredictable costs, business leaders and boards are demanding that FinOps engineers implement appropriate controls, shaping FinOps into a platform function that works closely with product development teams. At the same time, cloud computing fosters business speed and agility; as services mature and the platform becomes a business enabler rather than a source of technical bondage, engineering teams need to align with their FinOps operations, shifting the controls toward the product teams.