In Part 1, we explored the foundational strategy, including data classification frameworks and tagging approaches. In this post, we examine the technical implementation approach and key architectural patterns for building a governance framework. We explore governance controls across four implementation areas, building from foundational monitoring to advanced automation. Each …

Generative AI and machine learning workloads create massive amounts of data. Organizations need data governance to manage this growth and stay compliant. While data governance isn’t a new concept, recent studies highlight a concerning gap: a Gartner study of 300 IT executives revealed that only 60% of organizations …

In Part 1 of this series, we explored the fundamental risks and governance considerations. In this part, we examine practical strategies for adapting your enterprise risk management framework (ERMF) to harness generative AI’s power while maintaining robust controls. This part covers: Adapting your ERMF for the cloud Adapting …

According to BCG research, 84% of executives view responsible AI as a top management responsibility, yet only 25% of them have programs that fully address it. Responsible AI can be achieved through effective governance, and with the rapid adoption of generative AI, this governance has become a business imperative …

Security and governance teams across all environments face a common challenge: translating abstract security and governance requirements into a concrete, integrated control framework. AWS services provide capabilities that organizations can use to implement controls across multiple layers of their architecture—from infrastructure provisioning to runtime monitoring. Many organizations deploy …