Enhancing Data Privacy and Compliance with AI | Patricia Thaine, Private AI

Jun 20, 2024

39:51

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In today’s episode, we sit with Patricia Thaine, the Co-founder and CEO of Private AI. Patricia shares her journey into the world of AI, the challenges companies face in adhering to data protection regulations like GDPR, and the vital role of privacy-enhancing technologies. She elaborates on how Private AI leverages artificial intelligence to help companies identify and safeguard personal information within their data, ensuring compliance and protecting customer privacy. Additionally, Patricia emphasizes the importance of data minimization and discusses the real-world applications of AI in addressing privacy and data protection challenges.

Private AI is addressing the pain point of data privacy by leveraging AI to help companies identify and protect personal information in unstructured data. Patricia explained that AI is crucial in understanding the context of the data and making accurate predictions about what constitutes personal information. While traditional methods like regular expressions can be used, they often fall short due to their inability to handle the complexity and variety of unstructured data.

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Founder Bio:

Patricia Thaine is the Co-Founder & CEO of Private AI, a Microsoft-backed startup who raised their Series A led by the BDC in November 2022. Private AI was named a 2023 Technology Pioneer by the World Economic Forum and a Gartner Cool Vendor. She is also a Computer Science PhD Candidate at the University of Toronto (on leave) and a Vector Institute alumna. Her R&D work is focused on privacy-preserving natural language processing, with a focus on applied cryptography and re-identification risk. She also does research on computational methods for lost language decipherment. Patricia is a recipient of the NSERC Postgraduate Scholarship, the RBC Graduate Fellowship, the Beatrice “Trixie” Worsley Graduate Scholarship in Computer Science, and the Ontario Graduate Scholarship. She is the co-inventor of one U.S. patent and has ten years of research and software development experience, including at the McGill Language Development Lab, the University of Toronto’s Computational Linguistics Lab, the University of Toronto’s Department of Linguistics, and the Public Health Agency of Canada.

Time Stamps:

02:10 Patricia's background and journey into AI

03:54 Challenges and solutions in data privacy

06:15 Consequences of noncompliance in data protection

09:43 Enhancing data privacy in AI startups

11:34 Unraveling data privacy in AI for multinational compliance

15:11 Demystifying AI’s role in data analysis and application

17:10 AI, privacy, and internal risks in large language models

21:09 Building the private AI platform: Team and technology insights

23:35 Efficiency and expertise in building custom machine learning models

28:33 Catalysts and compliance in conversational AI adoption

30:35 Evaluating AI’s role over regular expressions in customer solutions