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By Jon Fowler, Managing Director, Data Solutions at SecretariatThe ongoing Post Office Horizon Inquiry stands as a landmark case that showcases the power of modern digital forensics in answering a critical question: Who knew what and when? At the heart of the inquiry lies the fundamental need to understand if and at what point senior Post Office officials became aware of the flaws within the Horizon IT system.AdvertisementTraditionally, digital forensics has focused on proving digital events – a website visit, a file modification, or a device connection. While it excels at establishing “what happened,” it does not definitively answer “who did it?” This is where eDiscovery and communication analysis comes in, bridging the gap between digital activity and human involvement. Techniques like communications mapping visualize communication networks, a cornerstone of modern eDiscovery. Creating a diagram where nodes represent individuals and connections signify email exchanges. This means investigators can track conversation threads, identify key participants, and potentially reveal who knew what and when.Another crucial aspect in the Horizon case is digital forensics’ ability to recover deleted communications. Deleted emails may not be truly gone—their remnants might linger within an organization’s storage systems

Jon Fowler, managing director of data solutions at SecretariatThe eDiscovery ecosystem, once dominated by manual document reviews and endless paperwork, has undergone a dramatic transformation due to artificial intelligence (AI). Generative AI, a particularly innovative branch that creates entirely new text, emails, or code based on existing data, offers immense potential yet faces complex challenges. By reflecting on the adoption journey of predictive coding, the first major AI tool in document review, we can glean valuable lessons for integrating generative AI responsibly and effectively.AdvertisementPredictive Coding: A Catalyst for ChangeBefore predictive coding, sifting through mountains of documents for relevant information was a labour-intensive, time-consuming, and costly process. Predictive coding introduced a paradigm shift, empowering users to train AI models to filter and rank documents based on predefined relevance criteria. This resulted in:AdvertisementIncreased Efficiency: Reviewers focused on a smaller, pre-classified pool of documents, significantly reducing time and cost.Improved Accuracy: Over time, trained models learned to identify relevant documents more accurately than manual methods.Enhanced Scalability: Large and complex data sets became manageable through AI-powered triage.However, the adoption of predictive coding had its challenges. Many lawyers expressed concerns that AI would replace human judgment and the nuanced decision-making skills they had honed through years of legal training

eDiscovery is a significant part of the legal tech world. How will GenAI change it? Artificial Lawyer caught up with Jon Fowler, Managing Director of litigation consulting group Secretariat, to hear what the new wave of AI will mean for eDiscovery, whether deep fakes will become an issue, how this field has evolved over the years, and about what Secretariat does for law firms and inhouse teams.–How has eDiscovery changed since you came into the field?The eDiscovery landscape has dramatically transformed since I started in 2007. This is due to several factors:Increasingly complex types of disputesTechnological advancementsThe sheer volume of data involvedThe emergence of entirely new data typesData processing used to be a significant cost driver, but prices have plummeted as the industry matured. So, differentiation now comes from expertise in analytics and emerging data types.In those days, eDiscovery primarily dealt with emails, documents, and occasionally audio recordings in complex financial cases. While audio solutions existed, they weren’t particularly sophisticated and heavily relied on manual intervention.Today, the rise of collaboration platforms and chat applications has blurred the lines between structured and unstructured data. Thankfully, technology has caught up, offering seamless collection and processing for these diverse data types.Predictive coding emerged as a game-changer, fundamentally altering how large-scale document reviews are conducted