Using LLMs to create, review & improve privacy documents

Today's world runs on data. We offer practical, educational sessions on modern de-identification to enable responsible data sharing and use. Learn how to operationalize recognized standards and the latest regulatory guidance, automate risk assessments and produce auditable evidence to know when data can be treated as non-personal information.

Woodway Assurance. Trust in Data.

Are your privacy documents strong enough to stand up to review, audit or approval?

Across data access and data-sharing initiatives, teams rely on a growing set of documents to demonstrate good privacy practices — from de-identification methodologies to data-sharing agreements and risk assessment reports. Reviewing these documents thoroughly and consistently to ensure they meet regulatory expectations takes time and expertise, and can slow projects down.This webinar focuses on what a good de-identification process looks like, and how large language models (LLMs) can support the review and analysis of privacy documents in the context of data access and sharing. Drawing on real project experience, we will show how LLMs can strengthen the development and review of these documents, improving quality, consistency and completeness, all while accelerating the process. We will conclude with a practical demonstration of new technology that enables a fully automated re-identification risk assessment.You’ll learn:<ul><li>About contemporary guidance and standards, as well as prompts, checklists and criteria, for applying and evaluating de-identification methodologies, and how an LLM can be used to evaluate, critique and provide feedback on a de-identification methodology document</li><li>From concrete experiences using LLMs and automation to support the privacy documentation process</li><li>How to review data-sharing agreements to assess whether they reflect good practices for sharing de-identified data</li><li>How new solutions, such as the companion functionality in EviData, can support this type of workflow</li></ul>Who this is for:Leaders and practitioners working in privacy, data governance, legal, compliance, risk, research, analytics, IM/IT and information security, across public and private sector organizations. This session will be particularly useful for teams responsible for de-identifying data for secondary purposes, as well as those reviewing, approving or producing privacy-related documentation in environments involving sensitive or regulated data.Speaker bios:Dr. Khaled El Emam is Canada’s Research Chair in Medical AI at the University of Ottawa and leads the university’s new medical AI institute. He is a Professor in the School of Epidemiology and Public Health and Director of the Electronic Health Information Laboratory at the CHEO Research Institute. He was recently the Scholar-in-Residence with the Ontario Information and Privacy Commissioner (IPC), where his main mandate was to support the IPC's modernization and expansion of their de-identification guidance. An internationally recognized expert in anonymization, re-identification risk assessment and privacy-preserving data sharing who has founded or co-founded several spin-off companies, he has decades of experience conducting and applying rigorous research to practical solutions used globally.Michael Pascu is a Senior Manager at INQ Consulting focused on AI and data governance. He has provided advisory and consulting services on data strategy, data ethics, and AI and data governance across a diverse set of industries, including the public, financial services and insurance sectors. Michael focuses on supporting organizations in operationalizing effective AI governance and tailoring their data management solutions to enable advanced analytics and AI opportunities. Prior to joining INQ, he was at Deloitte Canada where he was part of their AI Strategy practice, providing advisory services to Canada’s largest banks, insurance providers, and public sector organizations. He also helped grow the company’s Trustworthy AI Offering where he assisted clients in maturing their AI governance capabilities, adhere to emerging global AI regulations, and stand up their data ethics communities of practice.

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Using LLMs to create, review & improve privacy documents

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Using LLMs to create, review & improve privacy documents

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