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93 I L T A N E T . O R G These pillars work together to create a framework that enables organizations to build reliable AI systems while maintaining forensic data integrity. CHALLENGES AND CONSIDERATIONS Data collection enhances AI, but the opposite is true - AI enhances data collection efficiency. An AI feedback loop is where AI can further add value by optimizing the processes of collecting data in and of itself. A prime example is predictive coding in ediscovery, where an AI-driven process streamlines document review by prioritizing the most relevant data, creating a more efficient collection process. However, while this convergence of digital forensics, ediscovery, and AI presents opportunities, several critical considerations demand attention. The success of AI implementations hinges entirely on data quality. As industry experts emphasize, AI models follow the principle of "garbage in, garbage out" without exception. This reality makes the creation of forensically sound AI datasets particularly challenging in three key areas: • Accurate Data: AI's foundational element is ensuring data is solid, correct, and represents what is trying to be studied. It's about being thorough and meticulous in how data is collected and verified. • Playing by the Rules: With all the privacy laws and regulations out there, organizations are expected to adhere more and more to data requirements and legal frameworks. It is critical to balance using valid data and respecting people's privacy. • Keeping Secrets Safe: Protecting sensitive information while maintaining valuable data for AI training is a top priority. Think of it as redacting a document - you want to hide the sensitive bits while keeping the vital context intact. CONCLUSION The most fundamental challenge underlying digital forensics, ediscovery, and AI is the issue of data collection. Moving forward, centralizing data architectures of various technology landscapes on forensically sound data collection will lead to an ease in innovation. Making data compliant and secure while attaching to it the principles of integrity and accountability that are the mainstays of digital forensics and ediscovery should be the norm when thinking about the changing landscape of artificial intelligence. ILTA THOMAS YOHANNAN is Co-Founder of Digital DNA, creators of Rocket - the industry's first cloud-native remote forensic collection platform for Windows & MacOS that operates without installed software, hardware, or on-site personnel. As an attorney merging legal expertise with technical acumen, he specializes in security, data forensics, and cyberinsurance. Thomas excels at bringing innovative solutions to market through strategic analysis of risk and regulatory frameworks in high-touch verticals. His multidisciplinary approach helps enterprises navigate complex digital challenges. thomas@digitaldnagroup.com FEATURES

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