P2P

90 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 dataset for a specific business purpose. This particular purpose can include decision-making, answering research questions, or strategic planning. It's the first and essential stage of data-related activities and projects. Yet, the integrity and reliability of AI systems depend entirely on data that remains untouched and unaltered from its original state (i.e., forensically sound data). A few critical aspects must be in place when gathering training data for AI, similar to digital forensics. CRITICAL ASPECTS OF FORENSIC DATA INTEGRITY Chain of Custody: Tracks every interaction with the data through detailed chronological records of collection, storage, and access, including timestamps and user details for complete accountability. Cryptographic Hashing: Generates unique digital fingerprints of data files, enabling immediate detection of any modifications or tampering through hash value verification. Data Acquisition Methods: Utilizes specialized forensic tools to capture data while preserving original file structures and metadata, ensuring authenticity from the point of collection. Documentation: Maintains transparent records of collection processes, methodologies, transformations, and limitations, establishing clear data provenance. Metadata Preservation: Retains all contextual information about data sources, providing crucial context for forensic investigations. Additionally, just as traditional digital forensics requires meticulous documentation and validated tools, organizations using AI need strict protocols to preserve training data, model parameters, and system logs in their original form. This forensic approach to data handling does more than just feed algorithms—it creates an auditable trail that proves your system's decisions are based on reliable, untampered information, building trust and meeting compliance standards. "For many companies, building a forensically sound data approach feels overwhelming," notes Christian J. Ward, Chief Data Officer of Yext, a corporate knowledge graph and search company. "Here's the reality: your already structured data can integrate seamlessly with AI solutions. Whether custom or off-the-shelf, today 's AI models have massive training datasets beyond any single organization. You can merge this AI with forensically

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