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Digital evidence overload is now the starting condition of serious casework, not the exception. In one California fentanyl homicide, the scene produced no pills, no powder and no packaging. It produced a phone. By the time investigators finished serving legal process, they were facing roughly 1.2 terabytes of data.
The Placer County District Attorney’s Office has a name for this: death by data. The office has secured five of the seven successful murder prosecutions of fentanyl dealers in California. In a recent webinar, a senior prosecutor and a crime analyst from that office described what that volume looks like from the inside. Their experience points to a conclusion the profession needs to state plainly. Volume has outgrown human review. Agencies that still treat AI-assisted analysis as optional are making a choice, whether they say so or not. They are accepting that some evidence will never be read.
The case drew on returns from media, rideshare, payment, cloud and device sources. Each return arrived in its own format, with its own structure and its own noise. Together they added up to more data than any team could open file by file.
The office’s crime analyst put the number in human terms. 1.2 terabytes is about 500 million pages of plain text, or more than 10 million PDF pages. Her conclusion was blunt. No one person can go through it and not miss something.
This was not an unusual case. Overdose deaths often leave a clean physical scene, so the digital record carries the investigation. Placer County went from three confirmed fentanyl deaths in 2019 to 32 in 2021. Furthermore, each one is potentially a data case of the same scale.
Therefore, the real question for agency leadership has changed. It is no longer whether a case will be data-heavy. It is whether the team has the capacity to read what it collects.
In an environment of digital evidence overload, data that sits unread has the same value in court as data that was never collected. In other words, both count for nothing. However, only one of them shows up on the evidence log and creates the impression that the work is done.
The Placer County case turned on a small number of records. A message thread, a rideshare drop-off, a short video and a few minutes of location data. Those records were a tiny fraction of the total. As a result, finding them by hand depends on luck, instinct and overtime.
Manual review also carries a quieter cost. Analysts spend their hours formatting, cleaning and filtering instead of analyzing. Additionally, every new warrant return adds to the backlog, and cases wait in line.
Agencies facing digital evidence overload should treat data capacity the way they treat staffing. A unit that cannot process its returns is understaffed, whatever the headcount says.
AI and agentic workflows belong in the sifting layer. Normalizing returns from different providers, aligning timestamps, filtering noise and flagging links between accounts, places and times are problems of scale. Machines handle scale well because they do not get tired at hour eleven.
Agentic AI takes this a step further. It can carry a single investigative question across several sources at once, checking one return against another before an analyst opens a file. As a result, analysts start their day with candidate connections instead of raw exports.
Similarly, open-source information belongs in the same picture. Fentanyl is marketed openly on social platforms, with menus and availability posted for anyone to see. An open-source intelligence workflow that sits apart from warrant returns leaves gaps that investigators then have to close by hand.
However, the goal is not speed for its own sake. It is getting people to the handful of records that matter while there is still time to act on them.
AI should not decide what a record means, whether it supports a charge or how it reaches a jury. Instead, those decisions belong to investigators, analysts and prosecutors. They are the people who put their names on the work and defend it under oath.
The Placer County analyst testified from a presentation in which every data point referenced its original return file. That choice was deliberate. On cross-examination, “it’s in the software” is not an answer. The answer has to name the return, the file and how it entered the analysis.
The same standard applies to AI. If an AI-surfaced finding cannot be traced back to its raw source, it is a lead, not evidence. Agencies evaluating any data analytics capability should ask that question before any other.
Sourcing also paid off in the jury room. Because the exhibits were admitted into evidence, jurors followed the references back into the underlying records themselves. Human-owned, sourced work holds up even when the jury checks it.
Start by planning for digital evidence overload at intake. Every death investigation should be treated as a data case from the first hour, with collection and review capacity planned together.
Next, measure capacity honestly. Leadership should know how much of what the agency collects actually gets reviewed. If the answer is unclear, that is the answer.
Finally, divide the work deliberately. Put AI on sifting and correlation, keep people on judgment and testimony, and hold every tool to the provenance standard a defense attorney will apply anyway.
Collecting the data was never the hard part. Reading all of it is.
See the full story, from the first warrant return to the verdict, in the on-demand webinar with the Placer County District Attorney’s Office, or Request a demo to see how PLX and CoAnalyst help analysts work through high-volume returns.
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