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In the summer of 1956, when John McCarthy gathered researchers at Dartmouth College, he did not just coin the term “artificial intelligence” — he sparked a digital revolution that would span generations. While the world is enamored with headlines of the changing face of society due to Large Language Models (LLMs), artificial intelligence (AI) extends into nearly every corner of innovation: self-driving vehicles navigate our streets, computer vision systems diagnose diseases, and neural networks unlock patterns in vast seas of data. Yet beneath the complexity of these systems lies a fundamental truth: the quality of AI is only as reliable as its foundational data. The emphasis on data quality is why data collection from a legal and investigative perspective is crucial for moving forward.
In its core definition, digital forensics deals with recovering and investigating data residing in digital devices and cloud-native storage, generally in the context of cybercrime. Similarly, in the context of the EDRM model, ediscovery manages data as evidence from initial collection to presentation for use in both civil and criminal legal cases. Yet, for both disciplines, the point of origination is through data collection, which is this crucial process of gathering digital evidence or information that is accurate and legally allowable.
Like digital forensics and ediscovery, AI’s effectiveness hinges on forensically sound data collection. Why? Forensically sound data is information collected, preserved, and handled in a way that maintains its integrity and authenticity so it can be reliably used as evidence or for analysis. For AI, gathering and validating data isn’t just a “best practice,” but it’s essential for building trustworthy AI systems that can identify patterns, form patterns, and produce insights. Illumination of the need for forensically sound data as the foundation of AI occurs when considering the fact that when LLMs memorize errors and biases and create incomplete analyses, there is an audit trail to see where such misgivings originated. The critical role of forensically sound data collection and its parallels with established practices in digital forensics and ediscovery must be examined to seize the opportunity of AI technologies.
The Data Collection Challenge
According to research by AI Multiple Research, training data collection has been identified as one of the main barriers to AI adoption. Their analysis highlights six significant data collection challenges: availability issues, bias problems, quality concerns, protection and legal requirements, cost constraints, and data drift prevention. Three challenges — quality concerns, protection and legal requirements, and bias problems — can be effectively addressed through forensic data collection methods because forensically sound data is collected to ensure integrity using legally prescribed standards. When talking about clean data in the context of AI, we generally mean valid, consistent, and uncorrupted data. The large volume, complexity, and rapid data evolution of data within an organization make the task difficult. However, these challenges present an opportunity to leverage established forensic methodologies to ensure data quality.
The Role of Forensically Sound Data Collection
Artificial intelligence begins with data collection. Every technology starts with data collection. Data collection is not just the first step in the decision-making process; it is the driver of machine learning. The integrity and reliability of AI systems hinge on acquiring meaningful information to build a consistent and complete 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 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 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.
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 ensures 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.
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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 cyber-insurance. Thomas excels at bringing innovative solutions to the market through strategic analysis of risk and regulatory frameworks in high-touch verticals. His multidisciplinary approach helps enterprises navigate complex digital challenges.