Are Large Language Models Like GPT Secure? A Thomson/Reuters Webinar
A Look at the Technology and the Law
The legal industry is increasingly thinking about using AI and Large Language Models (LLMs) like GPT for document review, legal research, and even writing legal briefs. Yet, in our discussions, legal professionals regularly express concern about LLM security.
In this webinar, we discussed the following questions. Take a look at the recorded video above to hear more.
- Do we risk a waiver of attorney-client or work-product privileges by sending our data to OpenAI?
- What if that data includes confidential client information?
- Can LLMs learn from and share the information I send?
- How do large language model providers like Microsoft assure data security and confidentiality?
- What is the law governing these questions and how will it be applied?
Check out our article, “Are LLMs Like GPT Secure? Or Do I Risk Waiving Attorney-Client or Work-Product Privileges?”, that inspired this webinar.
Download a PDF of the slides here.
John Tredennick (JT@Merlin.Tech) is the CEO and founder of Merlin Search Technologies, a software company leveraging generative AI and cloud technologies to make investigation and discovery workflow faster, easier and less expensive. Prior to founding Merlin, Tredennick had a distinguished career as a trial lawyer and litigation partner at a national law firm.
With his expertise in legal technology, he founded Catalyst in 2000, an international ediscovery technology company that was acquired in 2019 by a large public company. Tredennick regularly speaks and writes on legal technology and AI topics, and has authored eight books and dozens of articles. He has also served as Chair of the ABA’s Law Practice Management Section.
Dr. William Webber is the Chief Data Scientist of Merlin Search Technologies. He completed his PhD in Measurement in Information Retrieval Evaluation at the University of Melbourne under Professors Alistair Moffat and Justin Zobel, and his post-doctoral research at the E-Discovery Lab of the University of Maryland under Professor Doug Oard.
With over 30 peer-reviewed scientific publications in the areas of information retrieval, statistical evaluation, and machine learning, he is a world expert in AI and statistical measurement for information retrieval and ediscovery. He has almost a decade of industry experience as a consulting data scientist to ediscovery software vendors, service providers, and law firms.
Bill Hamilton is the Senior Legal Skills Professor at the University of Florida Levin College of Law, where he teaches electronic discovery, complex litigation and civil procedure. He has taught electronic discovery for the past 10 years and is an author of the LexisNexis Practice Guide Florida e-Discovery and Evidence and A Student Electronic Discovery Primer: An Essential Companion for Civil Procedure Courses. He is also the General Editor of the LexisNexis Practice Guide: Florida Contract Litigation.
Hamilton is a neutral arbitrator and mediator for the World Intellectual Property Organization and the author of more than 100 domain name dispute decisions. Prior to academia, Hamilton served as the electronic discovery partner for a national law firm. During his 30-year litigation career, he has been recognized in Chambers USA, Florida Legal Elite, Best Lawyers in America and Florida Super Lawyers.
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