Data and AI practices share a deep connection. Every component of an AI system can be understood as a combination of data and IT assets, making AI systems inherently subject to data management and governance principles. However, this does not imply that AI and data governance and management must be fully integrated. Instead, it highlights the need to ensure their alignment is clearly defined and effectively established.
This series of articles aims to:
- Analyze and compare various regulations related to data and AI
- Explore existing industry frameworks for data, AI, and associated risk management and governance
- Present a method to harmonize frameworks for data, AI, and risk management
- Evaluate real-world use cases for data and AI strategies
By addressing these objectives, this series provides actionable insights to help organizations navigate the interdependencies between data and AI governance and management.
Making Sense of AI Regulations and Frameworks: Principle-Based Regulations
This article discusses the challenges associated with AI principle-based regulations. In Part 1, we outlined which institutions issue AI-related regulations and how different legislative instruments interact across jurisdictions. Part 2 examined risk-based regulations, showing how [...]
Making Sense of AI Regulations and Frameworks: Understanding Risk-Based AI Regulations
This article provides a review of the risk-based AI regulations. In Part 1 of this series, we examined which institutions issue AI-related regulations and how various regulatory instruments interact. In this second part, we focus [...]
Making Sense of AI Regulations and Frameworks: Who Regulates AI—and How It’s Defined
This article provides the review of the AI-regulating bodies. Artificial intelligence is evolving faster than the rules meant to keep it in check. Around the world, governments, regulators, and industry bodies are setting standards for [...]
Tailoring AI Industry Frameworks for an Organization’s Needs
This article discusses the approach to tailoring AI industry frameworks to an organization's needs. Recently, I conducted a LinkedIn poll to identify the biggest challenges in implementing an AI governance framework. Figure 1 presents the [...]
AI Systems and Data & IT Products: Finding Common Ground
This article discuss the common grounds of AI systems and data products & assets. In the previous article of this series, I demonstrated that various legislations worldwide take significantly different approaches to defining an AI [...]
AI Regulations Globally: Same Goal, Different Paths
This article discusses the differences in AI regulations. In the previous article, “Harmonizing Data and AI Governance: To Do or Not To Do,” I discussed five key factors that influence the decision on how to [...]