Magnet Forensics Pairs AI Launch With Forensic Risk Study

Magnet Forensics recently unveiled Magnet AI, an engine that surfaces evidence artifacts in seconds and generates investigative summaries. To guide the deployment of this technology, the company is partnering with digital forensics veteran Brett Shavers on an independent study of where public safety investigations break down. The research will survey law enforcement leaders, examiners, investigators and prosecutors.

For corporate buyers, AI in forensic software presents a straightforward appeal: managing ever-increasing data volumes while reducing turnaround times and budget lines. However, deploying these tools introduces acute liability risks. Digital forensics differs from traditional forensic science because the evidence—phones, platforms, and file formats—constantly evolves. Injecting probabilistic AI models into a discipline that requires strict repeatability creates potential vulnerabilities if courts challenge the findings.

Shavers, who spent a decade in law enforcement and authored Placing the Suspect Behind the Keyboard: DFIR Investigative Mindset, has long warned against "button pushing," where examiners blindly accept tool outputs. That risk escalates with AI. As Shavers noted, "Digital investigations do not succeed or fail on an administrative spreadsheet. They succeed or fail at the scene, in the lab, during the investigation, and in the courtroom." An AI model that answers the same question differently on two runs cannot meet the standard required for legal evidence.

The consequences of unverified AI outputs are already documented. In Utah, an AI report-writing tool falsely claimed an officer had transformed into a frog based on movie audio. In Tennessee, an AI facial recognition error led to a wrongful five-month incarceration. Magnet Forensics is attempting to navigate this by designing its AI to produce "investigative leads" backed by citations, leaving verification and decision making "firmly in the hands of human judgment."

The partnership with Shavers signals an effort to align product development with practitioner realities rather than purely administrative metrics. Shavers stated the goal is to ensure decisions are guided "based on what happens in the field, not what someone thinks is happening from a spreadsheet." As vendors across the sector race to embed AI, the companies that succeed will be those that treat machine output as the starting point of an investigation, not the conclusion.