Glenn Devitt’s expertise in digital forensics began with life-and-death intelligence operations but found its most profound application in analyzing the final digital footprints of deceased veterans. His revolutionary Black Box Project at Stop Soldier Suicide transformed how researchers understand suicide prevention by applying forensic techniques typically used against criminals to save lives instead.
“I have a background in digital forensics, I was teaching cybersecurity hacking for open-source intelligence information,” Devitt explained during his Ed Clay Show appearance. “So I understood the security world, and then data science, where you bring it together. I would start dabbling in data science with machine learning.” This unique combination of skills positioned him to pioneer an entirely new approach to suicide prevention that would earnthe Department of Veterans Affairs recognition and $250,000 in funding.
The concept emerged from personal tragedy and professional insight. After losing eight friends to suicide, including his best friend Andrew Tallman who died three months after Devitt returned from deployment, he recognized that traditional intervention methods failed to identify at-risk individuals before crisis points. “I wrote my first eulogy when I was 21,” Devitt recalled, describing how loss shaped his determination to prevent future tragedies through systematic analysis.
From Criminal Investigation to Life-Saving Innovation
Devitt’s transition from analyzing digital evidence in criminal cases to examining suicide patterns required applying the same forensic rigor to an entirely different purpose. His experience conducting digital forensics “against pedophiles” revealed how comprehensive digital analysis could reconstruct individual behavior patterns and predict future actions. “It was like I could live your life. I could see everything you were doing,” he explained, describing the depth of insight available through systematic examination of digital footprints.
The Black Box Project name reflects this investigative approach. “Nobody knew how a plane crashed until they put a black box in the plane and figured out how they were crashing,” Devitt noted. “So I’m sitting on all these black boxes basically. All these plane crashes that happen, the cellphone is that individual’s.” This analogy captures how he transformed cellphones left by suicide victims into data sources for preventing future deaths.
Glenn Devitt established partnerships with industry leaders, including Cellebrite, whose CEO supported the project with a full government-level suite of forensic software. “Cellebrite has been amazing. We have a government-level suite of forensic software in North Carolina,” he noted, describing the technical infrastructure that enabled comprehensive analysis of donated devices from families seeking answers and hoping to prevent similar tragedies.
Machine Learning Algorithms Predict Suicide Risk Patterns
The Black Box Project’s breakthrough came from combining Devitt’s digital forensics expertise with machine learning algorithms capable of identifying behavioral patterns that precede suicide. Parents donated their deceased children’s phones, providing unprecedented access to complete digital histories that revealed warning signs invisible to traditional screening methods.
“I created a project called the Black Box Project where I did digital forensics on soldiers’ cell phones that killed themselves,” Devitt explained during content review meetings. “Their parents donated their phone to me and I did digital forensics on their phone, and build out machine learning algorithms to identify suicide trends and patterns.” This systematic approach enabled analysis of complete behavioral timelines leading to suicide events.
The project’s methodology focused on the critical period before death. “So I could see the last second, to last year of life to figure out what went wrong, why did it go wrong, and can we prevent and predict it in the future,” Devitt described. This comprehensive timeline analysis revealed patterns that traditional psychological assessments missed, including subtle changes in communication frequency, social media engagement, and digital behavior that preceded suicide attempts.
Devitt’s forensic background enabled the extraction of data that families and traditional researchers couldn’t access. Forensic techniques revealed deleted messages, browsing history, and communication patterns that painted complete pictures of mental health deterioration. The analysis extended beyond obvious warning signs to identify subtle indicators that could trigger early intervention.
Expanding Beyond Veterans to Broader At-Risk Populations
Recognition of the Black Box Project’s potential led to its expansion beyond veteran suicide prevention. “So if you can do it for veterans, you can do it for teens, firefighters, and police,” Devitt noted, describing how the same analytical framework could address suicide epidemics across multiple high-risk populations. The machine learning algorithms developed for veteran data proved adaptable to civilian contexts.
The project’s success drew attention from the Department of Veterans Affairs through the Mission Daybreak challenge, a $20 million competition seeking innovative solutions to prevent veteran suicide. Out of over a thousand entries, the Black Box Project advanced to finalist status and received $250,000 in funding, validating Devitt’s approach to data-driven intervention strategies.
This recognition transformed digital forensics from a purely investigative tool into a predictive life-saving technology. The same techniques Devitt had used to track criminal networks now identified individuals at risk of self-harm, enabling intervention before crisis points. The methodology’s expansion to teens, firefighters, and police officers demonstrated its universal applicability across demographics facing elevated suicide risk.
Glenn Devitt’s transformation of forensic investigation into suicide prevention illustrates how specialized military skills can address civilian humanitarian challenges through innovative application of existing technologies.
Digital Legacy AI: From Death Analysis to Life Preservation
The transition from analyzing death to preserving life crystallized when Devitt recognized how his forensic work revealed a fundamental problem with digital inheritance. Parents accessing their deceased children’s phones through his Black Box Project discovered intimate digital lives never intended for family consumption, creating additional trauma during the grieving process.
“The parents are still using their cell phone as their phone,” Devitt described during content review sessions. “They’re reading every message.” This revelation exposed how current digital inheritance methods violated privacy while failing to preserve meaningful legacies for future generations, creating additional emotional trauma when intimate personal content was never intended for family viewing.
The experience sparked Devitt’s vision for Digital Legacy AI, where he applies his forensic expertise to create secure systems that preserve digital memories without compromising privacy. “My legacy is scattered across the world right now, and if I die, I only have my wife to tell who I am,” he explained. “Really, your legacy is not what you did. It’s what you learned.” This insight drove development of patent-protected technology that secures personal knowledge and memories for controlled posthumous access.
His U.S. patent for “securing and facilitating access to a digital legacy” addresses the $84 trillion intergenerational wealth transfer by creating automated inheritance processes that verify death certificates, validate legal authority, and execute predetermined access instructions. The system combines blockchain technology, multi-factor authentication, and air-gapped storage to protect digital assets while ensuring verified beneficiaries can access intended inheritances.
From Forensic Investigation to Systematic Life Protection
Glenn Devitt’s career arc demonstrates how digital forensics expertise can evolve from investigating criminal behavior to protecting civilian populations through predictive analytics and secure inheritance systems. His work analyzing suicide patterns through the Black Box Project established methodologies for identifying at-risk individuals before crisis points, while his Digital Legacy AI venture applies similar systematic thinking to preserve family histories across generations.
The same analytical rigor that enabled him to reconstruct criminal networks now drives the development of inheritance technologies that anticipate tomorrow’s challenges from decentralized finance protocols to AI-generated intellectual property. “I was really good at working open source intelligence back then or creative ways of getting data,” Devitt noted, describing capabilities that now inform his approach to digital legacy preservation.
His evolution from a four-time Black Hat instructor teaching cybersecurity to a humanitarian technology entrepreneur illustrates how specialized technical skills can address broader societal needs. The forensic techniques that once tracked human traffickers now enable families to preserve memories and knowledge for future generations without compromising security or privacy.
This transformation from analyzing death to preserving life represents a fundamental shift in how digital forensics expertise can serve humanitarian purposes, positioning Glenn Devitt as a pioneer who applies combat-tested security principles to protect what matters most to families during the largest wealth transfer in American history.