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91 I L T A N E T . O R G FEATURES sound data structures through RAG solutions or similar protocols - combining broad language understanding with verified, trusted information. This isn't just about feeding data to machines. It's about ensuring every AI response draws from forensically verified knowledge." Forensic data collection in AI serves several critical functions. First, it ensures data integrity by implementing strict protocols for gathering and preserving training datasets, similar to evidence handling in criminal investigations. This process includes maintaining detailed documentation of data sources, collection methods, and preprocessing steps. For instance, when collecting employee emails from a corporate server using Rocket, each email is preserved with its complete metadata, including sender, timestamp, and routing information, creating exact copies. It also includes detailed documentation of data sources (whether emails came from Exchange servers or local backups), collection methods (whether extracted using Rocket or Outlook exports), and preprocessing steps (how emails were filtered and redacted). For AI systems, this forensic approach helps track potential biases, data quality issues, or manipulations that could affect model behavior. The rigorous protocols extend beyond data collection - they encompass recording model parameters, system logs, and decision-making processes to ensure data remains valid and uncorrupted throughout its lifecycle. For example, when an AI system analyzes employee behavior patterns for security threats, forensic documentation would allow investigators to trace the exact sequence of events, from the initial log files captured through the AI's analysis steps to the final alert generation. This level of detail becomes crucial for auditing AI behavior for accuracy and verifying that the underlying data hasn't been tampered with or degraded. By maintaining this detailed chain of custody for data and model decisions, organizations can demonstrate compliance with AI regulations while building trust through transparency - much like how a bank must prove its transaction records are authentic and unaltered for regulatory audits. BRIDGING TO ARTIFICIAL INTELLIGENCE Data is the fuel that powers artificial intelligence and machine learning systems. If AI works with premium and structured data, it creates more meaningful and accurate insights. Forensically sound data collection becomes crucial when looking for meaningful and accurate insights. Just as a high-performance engine requires clean fuel to run efficiently, AI systems need pristine data to produce reliable outcomes. When organizations feed their AI models with forensically sound data collected through rigorous digital forensics and ediscovery processes, they create a foundation for success. However, using poor-quality data is like putting cheap fuel in your engine, leading to unreliable performance and questionable results. As Zach Warren, Technology & Innovation Insights, Thomson Reuters Institute notes, "The idea of 'garbage in, garbage out' might be something that every lawyer has heard at this point, but being repeated so often doesn't make it any less true. In fact, the availability of Gen AI may make this maxim even more pressing: If law firm leaders see technology as a key firm differentiator in the near future, that makes clean data to run these tools not just a nice-to- have tech issue, but a key business problem that has to be solved." With the surge of digital transformation, organizations may need to establish a solid data foundation before implementing AI. Jumping Just as a high-performance engine requires clean fuel to run efficiently, AI systems need pristine data to produce reliable outcomes.

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