Executive summary
For decades, solving crimes meant investigators had to physically comb through every inch of a scene, meticulously collecting evidence, conducting face-to-face interviews with witnesses and suspects, then spending countless hours analysing their findings in labs and conference rooms. But AI is now revolutionising crime scene investigation, transforming it from an art into a science – a precise, data-driven science.
- AI is streamlining every aspect of investigative work, matching fingerprints, recreating crime scenes in 3D, and sifting through mountains of digital evidence.
- According to a 2025 survey, 51% of law enforcement agencies worldwide plan to implement AI in investigations within the next two years.
- Nearly 90% of law enforcement officers are supportive of their agencies using AI, while 87% believe AI is improving public safety.
- One study found that some facial recognition algorithms are 10 to 100 times more likely to misidentify Black and Asian individuals.
- “As an expert in digital forensics, I know that AI cannot be the final decision maker, because that responsibility lies with me,” says Lars Daniel, digital forensics lead at Envista Forensics.
The next decade will likely see AI become as common in forensics as DNA testing is today. Young investigators are already learning to work alongside algorithms, treating them as partners rather than threats. The technology will only get more sophisticated – imagine AI that can reconstruct entire crime scenarios in virtual reality or predict criminal behaviour before it happens. But the fundamental question remains: can we harness this incredible power while preserving the human judgment and civil liberties that define justice?
Think back to your favourite crime show from a decade ago. Remember those scenes where detectives hunched over evidence boards, connecting red strings between photos and scribbled notes? That’s pretty much exactly how real forensic science worked for a long, long time. Investigators would arrive at a crime scene and painstakingly catalogue every detail by hand – collecting physical evidence, dusting for fingerprints, taking photographs from every angle, and then spending countless hours interviewing witnesses whose memories might already be fading. Back at the lab, forensic experts would analyse blood spatter patterns, compare fingerprints by eye, and make educated guesses about timelines and sequences of events.
This traditional approach, while often effective, carried inherent flaws. Human investigators, no matter how skilled, often bring their own biases to a scene. They might focus on obvious evidence while overlooking subtle clues. Fatigue can set in during long investigations. Memory can fail when trying to connect the dots across multiple complex cases. Even the most experienced detective can sometimes miss a crucial piece of evidence hidden in plain sight and steer an investigation down the wrong path.
This rather antiquated approach is finally receiving a major overhaul thanks to AI. In recent years, AI tools have been quietly revolutionising how we approach crime scene analysis, enabling investigators to shift from subjective interpretation toward an objective, data-driven approach. Where human eyes might scan a room and see chaos, AI systems can identify patterns, anomalies, and connections that would take an entire team of investigators weeks to uncover – or would be missed altogether. The result? More criminals brought to justice, fewer cold cases gathering dust, and a level of investigative precision that seemed unimaginable just a generation ago.
The role of AI in crime scene analysis
From AI-powered fingerprint analysis to 3D crime scene reconstructions, AI is revolutionising crime scene analysis.
So, how exactly can AI transform the way investigators piece together what happened at a crime scene? Take fingerprint analysis, for instance. For over a century, fingerprints have been the gold standard of forensic identification, but the process has traditionally been painstakingly slow and prone to human error. It typically involved a detective squinting at smudged prints under a magnifying glass, trying to match ridge patterns by eye. Now, AI algorithms can scan through millions of fingerprints in seconds, identifying matches with remarkable precision. They can also identify partial prints, degraded samples, and even connect seemingly unrelated crime scenes through subtle pattern similarities that human analysts might otherwise overlook.
The way investigators document crime scenes has also undergone something of a dramatic transformation. Instead of relying on 2D photographs and hand-drawn sketches, investigators can now use high-resolution 3D laser scanners to capture every millimetre of a scene from multiple angles, ensuring that nothing gets missed, and then use computer vision algorithms to stitch the recordings together into what’s essentially a perfect digital copy of the crime scene. Detectives can then slip on VR goggles and walk through the scene months later, testing different theories about how the event might have unfolded. What if the shooter stood here instead? Could the victim have seen their attacker from this position? They can change their viewpoint, zoom in on evidence, or simulate possible bullet trajectories – all without contaminating the actual scene.
Streamlining investigative work
But modern crimes aren’t just about physical evidence anymore. With our lives increasingly dependent on all things digital, investigators often face mountains of electronic data that’s nearly impossible to sort through manually. This is another area where AI can offer a helping hand by sifting through terabytes of information from smartphones, laptops, and cloud storage, automatically flagging relevant messages, photos, or files. While an investigator might spend weeks scrolling through thousands of text messages, AI can instantly identify conversations mentioning specific locations, people, or timeframes. And unlike humans who might zone out after hours of monotonous searching, AI maintains laser focus whether it’s analysing the first file or the ten-thousandth.
A growing number of law enforcement agencies are also starting to use AI to tackle cold cases. AI systems can quickly process disparate data streams from multiple sources: crime scene photos, hours of CCTV footage, financial records, phone logs, social media content, emails, and more. By cross-correlating people, places, and events across these various data sources, AI can quickly flag potential connections or surface new suspects, essentially condensing years of investigative work into a single night. This could potentially breathe new life into cases that have long been considered unsolvable. Just imagine – wouldn’t it be crazy if AI figured out the identity of DB Cooper?
No room for bias
One of the biggest advantages of AI in forensic analysis is its potential to eliminate human biases. For all its scientific rigour, traditional crime scene investigation remains surprisingly vulnerable to cognitive biases. An examiner might subconsciously interpret evidence in a way that fits neatly into their working theory of the case. Or they’ve already zeroed in on a prime suspect, and suddenly everything seems to point in their direction. It’s not necessarily malicious, it’s just how our brains work. We sometimes see patterns where we expect to see them, even if those patterns aren’t really there.
AI algorithms operate differently. They don’t make their decisions based on hunches or gut feelings. They process data according to preset parameters, applying the same criteria whether they’re analysing evidence from a high-profile murder or a routine burglary. This consistency helps standardise outcomes across different investigators and jurisdictions. Where one human examiner might see a partial fingerprint match, and another might disagree, an AI system provides the same assessment every time it encounters similar data patterns. This shift toward objective, reproducible approaches has fundamentally changed how forensics operates.
At the same time, it’s impossible to ignore the glaring reality that AI systems can often introduce algorithmic biases of their own, typically reflecting the prejudices embedded in their training data or the assumptions of their creators. A facial recognition system trained primarily on light-skinned faces will struggle with darker complexions. In fact, a National Institute of Standards and Technology study found some facial recognition algorithms misidentify Black and Asian individuals 10 to 100 times more often than they do white individuals. Recognising these limitations, researchers and practitioners emphasise the importance of rigorous validation for forensic AI. Models need to be tested and validated thoroughly across diverse conditions and populations to ensure they perform equitably for everyone – not just certain groups. Otherwise, we risk trading one set of biases for another.
The accountability dilemma
The use of AI in crime scene analysis raises profound ethical and legal questions. Privacy and surveillance top the list of concerns – and for good reason. AI-powered tools like facial recognition systems, license plate readers, and camera-equipped drones dramatically expand law enforcement’s surveillance capabilities, leaving many people worried about the erosion of civil liberties and the creation of a police state. From an ethical standpoint, accountability becomes crucial. Who takes responsibility when an algorithm flags the wrong person as a suspect? Agencies must develop robust protocols for review and correction, treating AI errors with the same seriousness they’d apply to human mistakes. “As an expert in digital forensics, I know that AI cannot be the final decision maker, because that responsibility lies with me. Experts must ultimately verify all AI findings and remain accountable for the analysis,” says Lars Daniel, digital forensics lead at Envista Forensics.
While AI is gradually making its way into policing and forensic labs, we’re still in the early days of adoption. A 2024 University of Michigan survey found that about one-third of local law enforcement departments have adopted or plan to adopt AI or predictive policing tools. However, only 3% were actually using these tools at the time of the survey. Still, the momentum seems to be building, with 51% of law enforcement agencies worldwide planning to implement AI in investigations within the next two years, according to a 2025 global survey. Law enforcement and forensic professionals themselves are generally optimistic about AI’s potential. In a US national survey, nearly 90% of law enforcement officers were supportive of their agencies using AI, while 87% believe AI is improving public safety. Similarly, a 2024 survey spanning 97 countries found that 61% of law enforcement professionals see AI as a valuable tool in digital investigations, while 79% agree that AI makes investigations more effective and their jobs easier.
“As an expert in digital forensics, I know that AI cannot be the final decision maker, because that responsibility lies with me.”
Lars Daniel, digital forensics lead at Envista Forensics
Solving crime with AI
Law enforcement departments worldwide are using AI to help solve crimes and bring perpetrators to justice.
Now let’s take a look at some of the real-world cases where the police have used AI to solve crimes. In early January 2024, an unidentified male body was found under a flyover in Delhi, India, with no ID or personal clues, making it difficult for investigators to even determine who the victim was. An autopsy confirmed the man had been strangled, but with no leads on his identity, detectives were at an impasse. The Delhi Police then turned to an AI-based facial reconstruction tool. They digitally ‘resurrected’ the victim’s image by restoring facial features: opening closed eyes, adding natural skin tone, and replacing the morgue background with a neutral one. This lifelike reconstructed photo was then uploaded to a national criminal tracking network and printed on over 400 posters that police distributed across the city and shared via messaging apps.
The police soon got the breakthrough they needed. Within days, a man saw one of the posters outside a police station and recognised the victim as his missing brother. With the victim identified, investigators uncovered that the man had been embroiled in a personal dispute weeks before. Following this lead, police discovered he’d quarrelled with two men over a woman and was lured to a meeting where those individuals, aided by a female accomplice and a cab driver, strangled him to death. Based on the AI-assisted identification and subsequent detective work, authorities arrested four suspects (three men and one woman) for the murder.
The all-seeing eye of justice
In April 2024, a fight at a city park in Blue Springs, Missouri, escalated into a shootout that left two men dead and a third injured. Witnesses reported a vehicle fleeing the scene, but initial city surveillance cameras only provided a blurry image of the car with no visible license plate. To hunt down this crucial piece of evidence, the Blue Springs police department turned to its network of AI-enhanced license plate reader cameras made by security technology company Flock Safety. Unlike traditional patrol car-mounted plate readers, the fixed Flock cameras continuously monitor traffic at key locations and use machine learning to recognise vehicles by make, model, colour, and other unique features – even when a plate number is unknown.
Investigators queried the Flock system with the suspect vehicle’s description (a particular colour and model of sedan seen on footage) and were able to narrow down candidates across camera sightings. Within 48 hours, the system hit on an exact match: the same make and colour sedan, now identified with a specific license plate, had passed a nearby intersection. Officers were immediately alerted and moved in to detain the driver at a local car wash.
The truth uncovered
After a 2021 high school mass shooting in Oakland County, Michigan, investigators faced an overwhelming amount of digital evidence – from cellphone texts to social media posts – involving the shooter’s parents. In preparation for the 2024 trial, the Oakland County Prosecutor’s Office used Cellebrite’s Pathfinder, an AI-driven data analysis platform, to rapidly sift through multiple smartphones and reconstruct the timeline of events. The tool cross-referenced messages, photos, and location pings across seven different devices, revealing critical patterns that manual analysis might have missed.
Most notably, Pathfinder helped implicate the shooter’s parents by showing what they knew and when – for example, linking a photo of a weapon sent by the son to evidence of the parents receiving and responding to it – thus helping prosecutors build a stronger case. This was pivotal in prosecuting the parents for involuntary manslaughter (an unprecedented charge for a school shooter’s parents) by establishing their knowledge and negligence leading up to the tragedy. Instead of investigators laboriously opening each device one by one, the AI tool instantly synchronised the data into a coherent timeline, a task that “would’ve taken forever” otherwise, according to David Williams, Oakland County’s chief assistant prosecuting attorney. In the end, the parents were convicted, and the case set a historic precedent.
Learnings
So, where do we go from here? Are we really about to hand over the reins of criminal investigations to algorithms and let machines determine guilt or innocence? The promise is tantalising. Cold cases finally cracked open. Innocent people exonerated by evidence that humans missed. Dangerous criminals caught before they strike again. Yet there’s something unsettling about machines making connections that human intuition can’t grasp. When an AI flags someone as suspicious based on patterns invisible to us, we’re essentially taking a leap of faith in code we might not fully understand.
Perhaps the real challenge isn’t choosing between human and machine investigation, but finding the sweet spot where silicon precision meets human wisdom. Because while AI can process terabytes of data without breaking a sweat, it can’t sit across from a grieving family and understand what justice means to them. It can’t weigh the moral complexities of a case or consider mercy alongside evidence. In the end, maybe what matters most isn’t whether truth comes from a detective’s hunch or an algorithm’s calculation. What matters is that we get to the truth and that we handle it with humanity and compassion.
