How is the public safety sector managing digital evidence?
Digital evidence plays a critical role in solving high-profile cases and enhancing public safety. Over recent decades, the impact of video technology on public safety operations has grown significantly, with evidence types ranging from body-worn cameras and dashcams to drones and mobile phone videos.
In both the United Kingdom and the United States, several laws and regulations govern the handling of video evidence to ensure privacy, security, and admissibility. In the UK, these include the UK General Data Protection Regulation (GDPR), the Data Protection Act 2018 (DPA), the Criminal Procedure and Investigations Act 1996, the Police and Criminal Evidence Act 1984 (PACE), and the Forensic Science Regulator Act 2021.
Meanwhile, in the US, the key regulations are the Federal Rules of Evidence, the Freedom of Information Act (FOIA), the Electronic Communications Privacy Act (ECPA) of 1986, the Fourth Amendment, and various state-specific laws.
Despite these frameworks, the public safety sector faces numerous operational challenges in managing digital evidence - and this only grows as the volume of video evidence expands.
The challenges in managing video evidence
Digital evidence is subject to rigorous scrutiny to ensure its authenticity and to maintain a secure chain of custody - which is critical for its admissibility in court.
The immense volume of video evidence from body-worn, CCTV and other types of camera footage often requires advanced management systems for efficient cataloguing and retrieval. Timely processing is also crucial, particularly in high-stakes investigations, where delays can significantly impact case outcomes - a task that is easier said than done.
For example, in 2022, San Francisco police came under scrutiny for large-scale delays in releasing public records from incident records. A 2024 investigation also found that district attorneys are struggling with reviewing and processing the volume of evidence generated by body-worn cameras, and are having to pay tens of thousands to have them transcribed to track and catalogue officer interactions with the public.
The Crown Court backlog in England and Wales has also reached unprecedented levels, which has caused major delays in trials. This backlog has placed a heavy burden on the criminal justice system, particularly affecting serious criminal cases such as murder, rape, and robbery. For instance, in December 2020, there were approximately 56,000 outstanding cases, with this number reaching over 67,000 in 2024.
Mismanagement of digital evidence can have severe consequences, as illustrated in recent high-profile cases. For instance, the recent UK Post Office Scandal saw over 900 postmasters wrongfully convicted due to faults in an accounting system, which were misunderstood and inadequately disclosed.
Enhancing digital evidence processing with AI
The integration of artificial intelligence (AI) into surveillance and data analysis can significantly enhance the capabilities of digital evidence processing.
For instance, facial recognition can quickly identify individuals in video footage, speeding up investigations. AI-driven object recognition can detect and categorize items, such as weapons or vehicles, in surveillance videos, providing critical insights in real time. AI-based transcription services can also convert audio evidence into searchable text, making it easier to sift through hours of recordings. These technologies not only improve efficiency but also help maintain the integrity of digital evidence by ensuring accurate and consistent analysis.
However, the rise of AI also introduces concerns about the authenticity and potential manipulation of digital evidence, especially in the era of deepfakes. Identifying AI-influenced video evidence requires specialized training and tools to detect anomalies such as inconsistencies in lighting, unnatural movements, or other signs that may indicate tampering. Advanced forensic techniques, including AI-based detection algorithms, can help discern authentic footage from manipulated content, and digital forensic experts are developing AI-driven tools that can detect manipulation in digital evidence and trace its digital footprint.
Prioritizing privacy and security when managing digital evidence
Digital evidence in the public safety sector requires continuous adaptation to keep up with evolving technology and data landscapes. Data security and integrity remain essential, and established protocols for evidence collection and sharing are only a part of it.
But with the rise of AI in the public safety sector, data privacy is becoming even more of a crucial factor. Handling digital evidence must adhere to various privacy laws and regulations to protect the personal information of individuals involved. Sensitive information must be carefully redacted to meet legal standards, especially when videos are shared with the media or used outside the courtroom.
Moreover, there should be proactive moves to implement relevant privacy-enhancing technologies (PETs) to manage this data while securing it efficiently. For example, AI technologies such as automated redaction tools can help anonymize sensitive information in video footage. The Department of Justice's (DOJ) best practice recommendations for video redaction highlight the importance of proactive planning and training to ensure video records are processed efficiently and within legal standards.
Continuous legal review and promoting ethical standards are essential to adapt to technological advancements and ensure the fair handling of digital evidence. By addressing these considerations, the sector can improve its digital evidence management, ensuring justice and upholding high standards of integrity and privacy.
As the use of video evidence continues to grow, so does the importance of effective management solutions. By leveraging AI and privacy-enhancing technologies, the sector can enhance the efficiency of evidence processing, maintain compliance with privacy laws, and protect individual rights.