Responsible AI in law enforcement
With the right tools in place, agencies can uphold a clear chain of custody and maintain a complete audit trail – ensuring AI-driven processes are transparent, traceable and accountable, says Simon Randall.
From body-worn cameras and CCTV to drone footage and citizen-captured clips, law enforcement agencies today operate in a near-constant stream of digital video evidence – far more than any team can manually manage. According to Microsoft, digital evidence now factors into nearly 90% of criminal cases, placing enormous pressure on departments to process and protect that data with speed, precision, and integrity.
To manage this content, agencies are turning to artificial intelligence) AI to automate time-consuming tasks like evidence tagging, content classification, and redaction of personally identifiable information (PII). These advancements bring tremendous potential, but speed and scale must be matched by systems that reinforce accountability and preserve evidentiary integrity.
Because in law enforcement, the admissibility of evidence hinges on the chain of custody. If there’s any ambiguity about who accessed, modified, or reviewed a piece of evidence, the entire case could be called into question. That’s why transparency must be foundational to every AI deployment. When implemented responsibly, AI can enhance both due process and public trust. Anything less risks eroding them.
Redaction is the front line of responsible AI
Among the most consequential uses of AI in law enforcement is video redaction. With increasing Freedom of Information Act (FOIA) requests, transparency mandates, and public scrutiny, agencies are often under tight deadlines to release footage, while still protecting the privacy of bystanders, victims, and witnesses.
Today’s AI can redact faces, license plates, screens, and other PII across hundreds of hours of footage in a fraction of the time it would take a human alone. But if those edits aren’t tracked, and it’s unclear what was redacted, by whom, and when, the technology can shift from asset to liability.
Responsible AI redaction protects the privacy of community members, along with the agency handling the footage. It ensures there’s a clear, auditable record of every action taken, both by the system and by the individual users overseeing it. And it ensures that AI is always in service of transparency, not a shield from it.
Auditability can’t be an afterthought
Auditability is a structural requirement for responsible AI, especially when it comes to redaction. If a system offers the ability to redact sensitive sections of audio and video then it needs to have a strong audit trail that can detail who uploaded a file, what the unique identifier was for that original file, what was done to it, and then what was created, who viewed it, accessed it and downloaded.
This level of traceability ensures redacted evidence stands up in court, supports FOIA compliance, and maintains integrity in internal reviews. And as investment in digital evidence management accelerates – with the market expected to reach over $14.4 billion by 2030 – there will be growing attention to how AI tools are implemented and governed – transparency is key to ongoing trust in these systems.
For agencies under pressure to do more with less, AI offers real value – streamlining hours of manual redaction, surfacing relevant footage faster, and simplifying collaboration with prosecutors or oversight bodies. But efficiency must go hand in hand with responsibility. A redaction engine that removes PII in seconds means little if it can’t document those actions or be reviewed by a human decision-maker.
The conversation, then, can’t stop at what AI can do. Agencies also need to ask: “Can I prove what a user did with this tool, when, and who was overseeing it?”
The good news is that responsible AI tools already exist. They are being used today by agencies that understand the stakes of digital evidence handling. These systems support multi-user collaboration, offer robust access control, and generate transparent edit histories, all while enabling faster and more accurate redaction and review.
A clear record is the strongest defence
Law enforcement leaders are right to be thoughtful about how AI is deployed – especially in areas as sensitive as evidence management and video redaction. These tools are powerful, but in law enforcement, power must be matched with precision, accountability, and public trust. When questions surface about what was removed, altered, or accessed, a clear, tamper-proof record is the best protection an agency can have.
Even as law enforcement becomes more digital, its core values remain the same. Fairness, accuracy, and due process still define the strength of a case. AI that upholds those principles – by making every action visible, reviewable, and attributable – can help departments meet rising demands without compromising quality.
AI in law enforcement doesn’t have to come at the cost of accountability. With tools that prioritise transparency and traceability, agencies can move faster, build stronger cases, and earn greater public trust. A clear chain of custody and audit trail ensures that technology enhances justice without ever compromising it.
Simon Randall is CEO and co-founder of Pimloc.
