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SUMMARY: Here is something you may not have thought of: all of your data controls were probably built prior to the commercial emergence of AI. AI represents data in different forms, contexts, and use models. So, are you keeping up with the changes?

SUMMARY:

David W. Schropfer and Anthony Woodward discussed the upcoming podcast episode, emphasizing the importance of an unscripted format and a compelling cold opener to engage listeners. Sarah Thorson joined the conversation, expressing appreciation for both David’s insights and Anthony’s contributions. The discussion highlighted the need for clarity in podcast content, ensuring that the audience understands the relevance of each episode.

The conversation shifted to the challenges of data governance in the context of artificial intelligence and large language models. David raised concerns about organizations’ compliance with new data types generated by AI, while Anthony pointed out the persistent issue of data silos that hinder effective governance. They acknowledged Record Point’s leadership in utilizing AI for data governance and discussed the complexities of managing unstructured data, emphasizing the necessity for robust frameworks to ensure data traceability and integrity, particularly in regulated industries.

The dialogue also addressed the implications of AI on data privacy and security. Anthony stressed the importance of implementing AI principles that protect personally identifiable information, while David raised concerns about AI potentially circumventing security measures. They discussed the need for auditing AI behavior and establishing controls to manage its actions effectively. The conversation concluded with a focus on the potential for future collaborations and the importance of navigating regulatory challenges in the evolving landscape of data governance.

Chapters & Topics:

Podcast Preparation Discussion
David W. Schropfer outlined the structure of his podcast, emphasizing a cold opener to provide context for listeners. Anthony Woodward shared insights about his own podcast, which features a rotating set of guests focused on data compliance. Sarah Thorson joined the discussion, thanking David for connecting with Anthony and contributing to the podcast preparation.

Discussion on Data Governance and AI
David W. Schropfer presented an article that emphasizes the governance shortcomings associated with new data types and locations arising from AI usage. He pointed out that organizations may overlook compliance issues related to LLMs and training data. Anthony Woodward contributed by mentioning the ongoing problem of data silos, which AI is bringing to light.

Preparation for Recording and Introduction
Anthony Woodward highlighted the dual aspects of data management in modeling, focusing on the data input and model management. David W. Schropfer checked the recording status and asked Sarah to mute her camera for the video. He also prepared to introduce Anthony, detailing his background and expertise in data governance and privacy.

Data Governance in the Age of AI
David W. Schropfer raised concerns about the adequacy of current data governance practices in the context of artificial intelligence, noting that many organizations may not be aware of the new data types and locations that AI introduces. He referenced an article that discusses the challenges of identifying and governing AI-related data. Anthony Woodward contributed to the conversation by explaining that AI data does not exist in silos and is often embedded across various platforms, complicating governance efforts.

Data Management and AI Compliance Challenges
Anthony Woodward emphasized the importance of integrating various data sources, such as Workday and OSHA data, to enhance AI capabilities within organizations. He noted that heavily regulated industries, including financial services and public sector entities, must pay particular attention to data management practices. The conversation also touched on the NIST AI standard as a framework for ensuring data integrity and governance.

• Compliance Standards for AI

AI Governance and Data Security Concerns
Anthony Woodward highlighted the challenge of categorizing data to apply appropriate policy controls, particularly regarding personally identifiable information (PII). David W. Schropfer expressed concerns about AI potentially exploiting vulnerabilities within an organization, likening its behavior to that of a threat actor. The discussion emphasized the necessity of establishing rules to limit AI access to sensitive data while allowing it to interact with other relevant information.

Data Privacy and AI Challenges
David W. Schropfer raised questions about the protection of personal data as AI technology advances. Anthony Woodward highlighted the fear surrounding privacy, emphasizing that individuals should have control over who accesses their information and how it is used. He pointed out the need for better ownership rights regarding personal data, which is currently treated as a commodity rather than an individual’s property.

Discussion on Collaboration and Future Engagements
David W. Schropfer and Anthony Woodward explored the complexities of defining what organizations can do rather than what they cannot. Anthony highlighted his work at RecordPoint, emphasizing their clientele in the public sector and financial services. They also touched on the challenges of organizing discussions around regulatory frameworks.

Key Questions:

• How does AI-related data interact with existing data governance frameworks?
• What are the implications of AI-related data governance for organizations?

Notepad:

• No notes

SHOW NOTES:

An recent article In Database Trends and Applications entitled, “Reimagining Data Governance and Security in the Era of AI and Fast-Moving Data“ they raised an interesting point about Artificial Intelligence and Data:

The rise of AI is also “giving way to new data types and locations” that may exist under the IT department’s radar, said Rick Vanover, senior director of product strategy at Veeam. “Vector databases, training data, the large language models, and more are components of an AI infrastructure and exist somewhere… “The biggest shortcoming for governance and security lies in the identification and governance of data.”

How is this data governed? Is it compliant with the regulations you follow? Is it susceptible to data leakage? Does your data governance system even see it

Here with me to talk about this today is Anthony Woodward.

Anthony Woodward is the co-founder and CEO of RecordPoint (a leading AI-based Data governance SaaS company). In addition to his technology background, be also has a background in the law. He is a fellow podcaster, and is regarded as one of the leading thinkers on the intersection of data and privacy.

Welcome Anthony

Q: Does AI- related data live in it’s own silo, or do typical data governance products capture it?