The application of artificial intelligence (AI) in law enforcement technology for investigations is revolutionizing how evidence is analyzed and managed. AI-driven tools and technologies are streamlining the process, enabling law enforcement agencies to handle evidence with greater speed, accuracy, and efficiency. With digital evidence present in up to 90% of criminal investigations, traditional manual methods are increasingly overwhelmed by the volume and complexity of data.
A recent survey of law enforcement professionals found that 79% agree AI improves investigative effectiveness, and 86% report that AI can quickly analyze vast amounts of data to surface relevant evidence faster than manual review. From digital evidence management to forensic data analysis, AI is transforming traditional methodologies, making it possible to process terabytes of information in hours rather than days, automatically prioritize critical leads, and reduce human error in evidence interpretation. AI automation in forensic labs has been shown to achieve classification accuracy exceeding 90%, significantly improving reliability and investigative outcomes.
In this blog, we’ll explore the benefits of AI in digital evidence management, key AI technological capabilities, and applications in investigations.
Benefits of AI in digital evidence management
One of the most significant advantages of AI in managing digital evidence is its ability to process large volumes of data quickly and accurately. Investigations often involve reviewing an overwhelming amount of digital evidence from various sources such as social media, surveillance footage, and emails. In fact, 69% of law enforcement professionals report that they do not have enough time to manually review all available digital evidence, highlighting the need for automated solutions.
AI’s capacity to sift through these files and surface data allows investigators to focus on the most critical elements of the case without being bogged down by manual data review processes. By leveraging machine learning, convolutional neural networks (CNNs), and natural language processing (NLP), AI can dramatically accelerate evidence processing while maintaining high analytical accuracy. In a digital forensic study, an integrated AI framework processed roughly 1 TB of image and text evidence in about 4.2 hours compared with 15–20 days using traditional forensic methods, representing approximately an 85× improvement in processing time. In the same study, CNNs achieved about 92% precision in pattern recognition and NLP achieved 88% precision in text‑based extraction, highlighting both speed and reliability gains.
In doing so, AI can add another layer of accountability to reduce human error, which can happen more often in manual evidence analysis processes. AI mitigates this risk by automating repetitive tasks and analyzing evidence with consistent precision. Machine learning models trained on large datasets can surface and identify things that may go unnoticed by human analysts. This enhances the accuracy of data analysis, accelerates the investigation timeline, and supports more reliable case outcomes.
AI also enables predictive insights, helping investigators anticipate connections between pieces of evidence, identify anomalies, and prioritize high-risk leads, which was not possible in traditional evidence workflows.
Key AI technologies in investigations
Several AI technologies are at the forefront of automated evidence processing, including machine transcription and translation, automated redaction, person-of-interest tracking across videos, and AI-powered metadata tagging. Emerging technologies such as generative AI, predictive analytics, and anomaly detection are now complementing these tools. Here’s how each one plays a role in investigations:
- Machine transcription and translation: evidence comes in various formats and might of other languages outside of an investigation team’s native tongue. Transcription and translation can quickly bog down an investigation. Finding a service to transcribe and translate is a task in itself, but once you do, it can take days to get the results. Furthermore, if a large batch of evidence needs this type of processing, it can take even longer. AI can accelerate that process significantly, helping investigators surface evidence that might have otherwise been missed.
- Automated redaction: in many cases, some people might appear within evidence files that need to be redacted. Previously, this would require a time-consuming manual workflow that took valuable resources away from other important tasks. AI can largely automate the redaction process, greatly reducing the time it takes.
- Person-of-interest and vehicle tracking: facial recognition technology falls short in some situations where footage involves crowds of people. There’s a time and place to use it, but at the same time, some hesitate due to concerns about protecting one’s personally identifiable information (PII). Now, new AI technology that defines humans as objects can help investigators look for key aspects of a potential suspect (such as a logo on a shirt, a type of hat or backpack they are wearing, etc.) to track that person across video files at scale, greatly reducing the time it takes to analyze video evidence. Investigators can also use this same technology to track vehicles across footage.
- AI-powered metadata tagging: when processed with AI, digital evidence creates a ton of data that can be leveraged to help investigators understand what they have on hand. From surfacing only relevant files to finding moments within files with potential case-changing evidence, AI provides automated evidence processing to provide investigators the insights to find that needle in a haystack.
These capabilities are more than just theoretical. A lot of agencies are already using AI in real-world applications.
Applications of AI in Investigations
AI in forensics and investigations is already being successfully integrated into technology stacks, transforming how cases are handled. For instance, TransPerfect Legal Services, an industry leader in multilingual legal support services, adopted AI capabilities from Veritone to help accelerate eDiscovery, transcription, and translation. In doing so, they were able to help two of their clients reduce their document review size, containing over 500K of multilingual files, by 68%.
We’ve also worked with state, local, and federal agencies to help improve how they hand media, whether it is evidence for investigations or simply internal files. For instance, the Oregon Police Department, as well as the Escondido and Lake Stevens Police Departments, have adopted automated redaction capabilities from Veritone. In doing so, they’ve been able to cut redaction times in half by improving efficiency. In the case of the Lake Stevens Police Department, they also adopted AI transcription to make it easier to analyze what’s spoken in video footage.
At the federal level, the U.S. Congress adopted AI to better store, manage, and distribute photos, audio, and video files, transforming its digital asset management. The same principle is applied to digital evidence management practices, which help investigators consolidate and process their digital evidence to better find and share only the most relevant information in a secure environment.
Ethical considerations in responsible AI deployment
As AI plays a larger role in investigations and law enforcement, ethical considerations must remain at the forefront of its deployment. Issues such as bias in AI algorithms, data privacy, and more make it critical for AI leaders to lead with standards that protect people from harm. As legislation continues to play catch up, there are things companies should be doing now. Veritone has created a set of AI for Good principles that guide everything we do to ensure our technology is transparent, trustworthy, secure, and compliant. It also empowers humans, such as investigators, to do their jobs even better.
Embracing AI to improve the investigative process
AI holds transformative potential for the future of evidence analysis and law enforcement technology. By enhancing accuracy, reducing human error, and streamlining the investigative process, AI empowers professionals to solve cases more efficiently and effectively. As AI technology continues to evolve, its integration into law enforcement will undoubtedly play a key role in improving public safety while maintaining ethical standards.
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Sources
https://link.springer.com/article/10.1007/s44163-025-00729-4
