How Artificial Intelligence Is Transforming Digital Forensics & Investigations (Part 1 of 3)

A decade ago, most litigation driven digital forensics centered around user-created email and documents stored locally on a computer, corporate servers, and cloud-based repositories. The new modern data universe is layered, fragmented, complex and often duplicative. Critical evidence may be buried within millions of records, hidden inside metadata, or masked by seemingly irrelevant data points that only reveal significance when viewed in context. Today, relevant evidence may live across mobile devices, encrypted messaging apps, collaboration platforms, cloud repositories, wearable devices, connected vehicles, and increasingly, AI-assisted workflows. 

The challenges are no longer simply finding evidence. It is identifying the right evidence quickly, defensibly, and at scale. This is where artificial intelligence is a game changer. AI is rapidly transforming digital forensics, E-discovery, internal investigations, and litigation support not by replacing human expertise, but by helping e-discovery professionals and investigators identify, cull and process complex data at a speed and scale that legacysystems could not compete with.  

This article is the first in a three-part series exploring AI’s expanding role in modern investigations: 

  • AI as an investigative tool 
  • AI-generated evidence 
  • AI as part of the misconduct itself 

In this first article, we focus on using AI to accelerate digital investigations. 

The Data Explosion Problem 

Data sets of early 2000’s consisted of email, documents, SMS messages and rudimentary media files. Now, an Advanced Logical Cellebrite extraction can hold 15-25 major data categories and potentially hundreds of application data exports, and store up to 2TBs of data! That is just one device. Today, an “average” user stores and uses 1.4TB. The modern investigations rarely involve a single source of truth. Instead, relevant evidence often exists across multiple systems and data types, including: 

  • Email 
  • Text messages and chat platforms 
  • Mobile devices 
  • Cloud repositories 
  • Application data 
  • Images and video 
  • IoT and connected devices 
  • Collaboration tools like Teams, Slack, and Zoom 
  • Chatbot platforms including, ChatGPT, Claude, Gemini, Copilot and Perplexity 

For litigation support teams and digital forensics investigators, the volume alone can be overwhelming. A single custodian may now possess hundreds of gigabytes or even terabytes of potentially relevant data. Multiplied across multiple custodians, devices, and communication channels, and the universe becomes significant and complicated very quickly. Additionally, modern data may not be reviewable on the same hosted platform, aka collaborative platform metadata, enterprise LLM, native files, embedded media, and hyperlinks.  

Traditional human review remains essential, but human review alone does not scale efficiently against modern data volumes. Investigative teams face growing pressure to move faster, control costs, and surface critical evidence earlier in the case lifecycle. This is where AI assisted tools begin to change the equation. 

Historically, attorneys focused on traditional sources such as emails, documents, text messages, and internal chat platforms. In recent cases, AI-generated content and even the prompts used to generate that content is now representing a new category of discoverable information. New preservation letters must list it, and litigators must ask for it.  

For litigation teams and forensic investigators, this raises important questions: 

  • Are AI prompts being preserved? 
  • Where does that data reside? 
  • Who has access to it? 
  • What metadata exists? 
  • How should AI-assisted workflows be documented? 

AI as a Force Multiplier  

At its best, AI acts as a force multiplier for investigators. 

It helps teams: 

  • Get to the facts faster 
  • Reduce investigative noise 
  • Prioritize high-value evidence 
  • Detect hidden relationships 
  • Surface subtle patterns across large datasets 

AI does not solve the case. It does, however, help investigators and legal teams see what may have otherwise remained buried. A useful way to think about AI in investigations is as a tool that gives practitioners enhanced visibility, speed, and focus. In many ways, it gives investigators “superpowers” not because it replaces expertise, but because it amplifies human capability.  

  • Visibility 
  • Speed 
  • Focus 
  • Strategic direction 
  • Ability to understand and interpret motive 

Instead of spending weeks manually reviewing low-value data, investigators can use AI-assisted workflows to quickly identify areas that warrant deeper human analysis. That shift can materially improve the efficiency and strategic direction of an investigation. 

AI is already improving how investigators analyze complex digital evidence. 

Behavioral pattern recognition: 

AI can help surface: 

  • Tone shifts 
  • Emotional escalation 
  • Concealment indicators 
  • Changes in communication patterns 
  • Unusual access behavior 

For example, in an internal investigation involving potential employee misconduct, AI may reveal a sudden increase in off-hours communications, unusual file access, and a shift in messaging tone all before a key resignation or suspicious event. This can be especially valuable in matters involving workplace misconduct, collusion, trade secret theft, or internal fraud. These signals do not prove misconduct, but they can help investigators ask better questions earlier. 

Multimedia Intelligence

AI is also proving valuable in multimedia investigations. 

Modern forensic tools increasingly incorporate AI-assisted capabilities for: 

  • Image classification 
  • Media origin analysis 
  • Deepfake detection 
  • Hidden image detection 
  • Visual anomaly detection 

These tools can help identify manipulated media, sensitive imagery, or suspicious visual content far faster than manual review alone. As multimedia evidence continues to grow and AI generated imagery gets more sophisticated, authenticity has become increasingly important. 

“The goal isn’t to let AI think for us. The goal is to let AI help us get to the right questions faster.”
— Melinda Redenius 

Quick Internal Investigations 

Speed matters in many internal investigations as concise actions need to be made quickly by multiple teams both inside and outside the company. Whether responding to allegations of fraud, embezzlement, IP theft, policy violations, antitrust, or regulatory concerns, early insight can significantly influence outcomes. 

AI helps investigative teams: 

  • Review the right material first 
  • Interview the right people sooner 
  • Identify or rule out misconduct earlier 
  • Reduce review burden 
  • Focus investigative resources strategically 
  • That can lead to meaningful business benefits: 
  • Lower costs 
  • Faster resolution 
  • Reduced operational disruption 
  • More defensible outcomes 

In many cases, the earlier an organization can identify risk, the better positioned it is to respond effectively. 

Human Judgment Still Matters 

Despite its power, AI has limits. This is where caution becomes essential. AI can surface patterns, anomalies, and signals, but it does not understand context the way experienced investigators, attorneys, and forensic experts do.AI may identify suspicious activity that is entirely benign. It may miss nuances. It may produce false positives. That creates real risk in legal matters where defensibility matters. Using AI effectively requires human oversight, validation, and documentation. 

Investigative teams must still ask: 

  • Why did the model flag this? 
  • Is the output reliable? 
  • Can the findings be validated? 
  • Can the workflow be explained and defended? 

These are not purely academic questions; they evoke the fundamentals of defensibility. At Veracity Forensics, we believe AI should enhance, not replace, expert judgment. Artificial intelligence helps us work smarter, and human expertise makes the work defensible. 

“AI can accelerate insight, but defensibility still depends on transparency, validation, and human judgment.” — Chip Koons 

That statement is reshaping how courts, counsel, and investigators think about authenticity, admissibility, and trust. In Part Two of this series, we examine the growing challenge of AI-generated evidence and what happens when seeing is no longer believing. 

Questions about defensible AI workflows, AI-assisted investigations, or emerging discovery risks? Veracity Forensics helps legal teams navigate complex digital evidence with defensibility at the forefront.