Artificial Intelligence in Digital Investigations:A leadership imperative for modern policingBy Brandon Epstein, Technical Forensics Specialist, Magnet ForensicsToday's police leaders are facing a common reality: the volume and complexity of digital evidence is growing faster than the investigative capacity of police services.Smartphones and other digital elements now play a part in the overwhelming majority of all criminal investigations. A single mobile device can contain years of communications, images, videos, location data and application records, often spread across local storage and the cloud.At the same time, police services are under pressure to do more with constrained budgets, manage backlogs, protect officer well-being and maintain public confidence in investigative outcomes. Artificial intelligence (AI) has entered digital investigations against this backdrop – not as a speculative innovation, but as a pragmatic response to scale.For police professionals, the most important question is not whether AI will be used in digital investigations – it already is – but how it is understood, governed and operationalized by those ultimately accountable for outcomes.WHAT AI IS AND IS NOT DOING Much of the anxiety around AI in policing stems from misunderstanding its role in investigations. Today’s AI enabled digital forensic tools, like those used to acquire and analyze cellphone and computer evidence, should not be used to determine guilt, draw conclusions or make investigative decisions.Their core function today is straightforward: to quickly help surface potentially relevant information – identifying patterns, grouping similar material, summarizing large datasets or highlighting items that may warrant closer human attention so investigators can use their expertise to focus on analysis rather than exhaustive manual review.For example, an investigator queries the AI to ask if there is evidence of narcotics distribution found on a cellphone extraction. The AI tool may return a series of images or communications it believes to be about narcotics distribution. The investigator then examines the highlighted images and chats, interpreting the data to further the investigation.Investigators still review the underlying source evidence. They read the messages. They view the images. They assess relevance and context. The AI tool understands the context of data, but it does not interpret meaning – it points investigators toward material that may matter.Before AI, the investigator would have to pore over hundreds of thousands of images and conversations to arrive at the same output. In both cases, the investigative work remains the same. The difference is time, efficiency and consistency. In other words, AI is being used to shrink the haystack, not decide what the evidence means.From a leadership perspective, this is not a technology issue – it is an operational one.EFFICIENCY AND BUDGETS Efficiency in this context is not about convenience; it is about public safety and stewardship of limited resources. When digital evidence reviews take weeks or months longer than necessary, the result is predictable: stalled investigations, increased strain on investigators, growing pressure on already limited budgets and delayed justice for victims. Digital evidence backlogs grow not because investigators lack skill, but because the volume of data outpaces human capacity.AI-assisted review changes this equation by accelerating initial evidence triage, allowing investigators to move more quickly from data collection to analysis, prioritizing cases more effectively and reducing unnecessary delays. For police leaders managing budgets and staffing pressures, this matters. AI is not a cost cutting shortcut – it is a way to align investigative capability with modern evidentiary reality without proportionally increasing headcount.PUBLIC TRUST DEPENDS ON TRANSPARENCY Community trust in policing is shaped not only by what decisions are made, but how they are made. Poorly governed AI raises legitimate concerns: opacity, over reliance on automation or technology that appears to replace human judgment. However, avoiding AI altogether does not eliminate risk – it creates new risks, which could include missed evidence, investigative inconsistency or investigator burnout.An important leadership responsibility is to provide investigators and frontline officers the tools to effectively do their work. In modern times, this encompasses AI tools purposely built for investigations, as well as ensuring they are deployed transparently and conservatively, with clear boundaries and human accountability.This includes being able to explain – at a high level – what AI is doing in investigations – and just as importantly, what it is not doing. AI does not replace investigative judgment. It does not operate autonomously. It does not remove human responsibility.Clear messaging around this distinction is essential for maintaining public confidence.RETHINKING “EXPLAINABILITY” Concerns about “explainability” frequently dominate AI discussions in policing, but they are often framed inaccurately. In practice, most officers and investigators cannot explain the internal mechanics of many technologies they already rely on in court– from breath analysis tools to radar systems to complex forensic software.What matters to policing – and to the justice system as a whole – is not that every investigator can explain how an algorithm functions internally, but that:
  • investigators verify and validate the underlying evidence; 
  • the reliability of the AI tools has been tested and evaluated; and 
  • the outputs are contextualized and weighed by humans.
The emphasis on testing and documented performance mirrors guidance recently published by international policing and justice bodies, including the IACP, which consistently identify human oversight and empirical validation – not technical transparency – as the foundation of defensible AI use in law enforcement.The more meaningful leadership question is not “Can my investigators explain how AI works internally?” but rather:
  • Has this technology been rigorously tested? 
  • Do we understand its limitations? 
  • Are we using it appropriately and conservatively?
TESTING, VALIDATION, ERROR RATES One of the strongest arguments for the responsible use of AI in digital forensics is that it can be empirically evaluated. AI features can be tested against known datasets, producing measurable performance metrics and documented error rates. This allows leaders to contextualize outputs and make informed decisions about when and how results should be relied upon.By contrast, there is currently no empirical data measuring how accur- ate human investigators are when reviewing massive digital datasets. Variability, fatigue and inconsistency have always existed in manual review – but have largely gone unmeasured.From a leadership standpoint, this creates an opportunity: AI, when properly tested and governed, may ultimately provide more transparency around results than traditional processes.For any organization facing the challenges of adopting AI, one point is clear: leadership decisions determine whether AI improves investigations or undermines trust. For police leaders, this means policy, governance and oversight – not just procurement.Investigators must be trained to understand where AI assists and where human judgment must dominate. Supervisors must ensure AI outputs are validated, contextualized and appropriately weighted alongside other evidence. There is no scenario in which AI replaces the human role in investigations or the courtroom. In practice, effective oversight of AI in digital investigations rests on three leadership levers:
  1. Define approved use cases
    AI should be authorized for specific, defined investigative tasks – such as evidence triage or data organization – rather than open-ended analysis. Clear boundaries reduce risk and increase defensibility. 

  2. Require human validation of source evidence
    Across multiple international studies, public confidence in AI-supported policing is highest when humans retain clear deci- sion-making control and review original evidence directly. This is already standard practice in digital investigations and should remain non negotiable. 

  3. Treat AI as an efficiency strategy, not an automation strategy
    AI adoption in policing is being driven primarily by staffing constraints, workload growth and data proliferation, not by a desire to reduce personnel.
AI is not a panacea, and it is not risk-free. But neither is the status quo. Ignoring AI in digital investigations does not protect public trust or investigative integrity. It exacerbates challenges already facing police services: data overload, investigative delay and strained resources.This presents leadership with the opportunity to adopt AI deliberately, as a means to:
  • improve efficiency without sacrificing accountability; 
  • manage budget pressures responsibly; 
  • support investigator well-being; and 
  • maintain defensible, transparent investigative processes.
When governed properly, AI strengthens modern policing by allowing investigators to do what they do best: apply professional judgment, experience and context to the evidence in front of them.The question for police leaders is not whether AI belongs in investigations, but whether it will be shaped intentionally or allowed to evolve by default.Brandon Epstein is a Technical Forensics Specialist at Magnet Forensics, a former police detective and co-founder of Medex Forensics, which Magnet acquired in 2024. Brandon specializes in AI and media authentication and is active in many digital forensic community organizations.

Learn more about AI at Magnet Forensics and watch Brandon’s AI Unpacked webinar series on how responsible AI is shaping the future of digital forensics. www.magnetforensics.com/ magnet-ai