In an age of digital transformation, 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. Are we risking a waiver of attorney-client or work-product privileges by sending our data to OpenAI? What if that data includes confidential client information?
If these questions resonate with you, you cannot afford to miss our upcoming webinar: "Are Large Language Models Like GPT Secure? A Look at the Technology and the Law."
We’ll delve into the key issues that every legal professional should consider:
- Can large language models learn from and share the information I send?
- Does a commercial license provide reasonable protection for my communications?
- Will OpenAI or Microsoft review the information I send such that it might waive attorney-client privilege?
- 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?
Our experts will unpack these questions and help you better understand how these new LLMs work, how commercial providers provide a “reasonable expectations of privacy” for your communications and what you should expect from your LLM vendor to protect against waiver of privilege.
Join us as we tackle the elephant in the room: Are LLMs like GPT secure and are we risking confidentiality and privilege when we send client data to these AI platforms for analysis?
Mary Mack is the CEO and Chief Legal Technologist of the EDRM [Electronic Discovery Reference Model]. She has more than two decades of strong credibility and global leadership within the e-discovery community and is known for her relationship and community building skills and for the depth of her technical and e-discovery knowledge.
She is the author of what is considered the first book on e-discovery, "A Process of Illumination: The Practical Guide to Electronic Discovery." She was the co-editor of "eDiscovery for Corporate Counsel" (Thomas Reuters) for nearly a decade.
John Tredennick is the CEO and founder of Merlin Search Technologies, a cloud technology company that has developed a revolutionary new machine learning search algorithm called Sherlock® to help people find information in large document sets–without having to master keyword search.
Tredennick began his career as a trial lawyer and litigation partner at a national law firm. In 2000, he founded and served as CEO of Catalyst, an international e-discovery search technology company that was sold to a large public company in 2019. Over the past four decades he has written or edited eight books and countless articles on legal technology topics, spoken on five continents and 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.