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92 P E E R T O P E E R : I L T A ' S Q U A R T E R L Y M A G A Z I N E | W I N T E R 2 0 2 4 to the conclusion of the process, AI activation, without ensuring their data meets the necessary quality standards, will only harm the usage of transformational technologies. All successful companies do it: constantly collect data. Data holds exceptional importance in fueling AI, as its strength lies in analyzing large amounts of data and making predictions based on its inputs. Data accuracy directly correlates with AI's ability to be intelligent. The data truly is the differentiator. Organizations must realize that foundational data is the first and most crucial step in creating accurate artificial intelligence, not jumping straight to activation. Organizations must prioritize accurate data from the start to maximize AI model performance. INCREASING AI INTELLIGENCE USING FORENSIC AND EDISCOVERY DATA Building on this foundation of clean, forensically sound data, organizations can leverage digital forensics and ediscovery principles to provide a rich training ground for AI algorithms. "Generative AI in ediscovery isn't just a tool; it's a force multiplier. Picture this: mountains of data that would take human teams months to review, tackled in hours. And it doesn't stop there— this tech learns and evolves, anticipating needs and uncovering connections you didn't even know to look for. It's not replacing humans; it's unleashing their potential by cutting through the noise and delivering actionable insights faster than you can say data overload," says Cat Casey, Chief Growth Officer, Reveal. Digital forensics and ediscovery data can offer a rich training ground for AI algorithms. For example, the AI can be presented with recurring incident patterns of cybercrime to predict or identify various occurrences of cybercrime to further assist in their cybersecurity measures. Similarly, AI will use information from an ediscovery process to automate and improve identifying relevant documents in legal cases, saving time and costs. HOW TO CREATE AI-READY FORENSIC DATA Creating AI-ready forensic data requires four essential pillars that ensure effective utilization in artificial intelligence and machine learning applications: • Data Quality: The foundation of reliable AI systems demands accurate, complete, and consistent data. This fundamental requirement ensures trustworthy model outputs and dependable results. • Governance: In today's regulatory landscape, data must be trusted, consented adequately to, and fully auditable to maintain compliance with privacy regulations and AI guidelines while protecting organizational interests. • Understandability: Data becomes more valuable when enriched with contextual intelligence, comprehensive metadata, and accurate labels, enabling AI systems to interpret and utilize the information better. • Availability: Ensuring the correct data is accessible at the right time through robust interoperability and real-time delivery capabilities is crucial for practical AI training and activation.

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