Generative artificial intelligence (AI) is rapidly transforming industries, and law enforcement is no exception. Police leaders now face a pivotal moment as the Fifth Industrial Revolution introduces new tools and challenges to public safety.
The Fifth Industrial Revolution is defined by the collaboration between humans and intelligent machines. Where the Fourth focused on automation and data, the Fifth emphasizes human-centered innovation — using AI, robotics and advanced analytics to enhance, not replace, human decision-making. In policing, this means technology that supports officers’ judgment, transparency and community trust rather than automating them away.
Understanding generative AI is essential — not only to harness its potential but to safeguard the integrity and trust that communities place in their police departments. This article offers a practical guide for chiefs and command staff navigating generative AI’s opportunities and risks.
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What generative AI is — and isn’t
Generative AI refers to systems that create new content — text, images, audio, or video — based on patterns learned from vast datasets. Unlike traditional AI, which classifies data or makes predictions, generative AI produces original outputs. For example, a generative AI can draft a police report from bullet points or generate a composite image based on a description.
This distinction can be confusing in practice, especially when using tools like Copilot. For example, if you ask Copilot to “find emails in Outlook 365” it is leveraging AI-powered search that understands your language and retrieves existing emails, but it isn’t generating new content — just locating what’s already there. However, if you use an agent in Copilot to summarize unread emails, highlight urgent tasks, or draft responses, that’s generative AI at work. In these scenarios, the system creates new summaries or text based on the content it finds, reflecting the core capability of generative AI.
It’s important to distinguish generative AI from other types of AI. Predictive AI, for instance, forecasts crime hotspots based on historical data, while analytical AI helps identify patterns in evidence. Generative AI, by contrast, creates something new — making it a powerful but sometimes unpredictable partner. Limitations include a tendency to “hallucinate” (produce plausible but false information, also called blind spots), sensitivity to biases in training data and challenges in verifying outputs.
What officers are already trying
Across the country, officers are experimenting with generative AI in several ways:
- Drafting reports: AI tools can convert field notes into full incident reports, or tools that automatically draft incident reports from body cam transcription data, streamlining documentation and reducing manual entry for officers.
- Chatbots: Officers and staff use AI-powered chatbots for answering routine questions, such as a statute or ordinance, case law, or supporting dispatch operations.
- Image generation: Generative AI creates composite sketches, reconstructs scenes from descriptions for investigations and victim support, and is also used in training and presentations.
Chief David Norris of the Menlo Park (California) Police Department notes, “Like many agencies, we have experienced early concerns with officers ‘freelancing’ with AI tools for functions like report writing. We have engaged in some education with our Team on the dangers of utilizing open source AI tools for such tasks. We are now exploring options to pilot some more secure industry-specific vendor tools to pull data from body worn camera video in an effort to assist and facilitate report writing.”
The risks: Accuracy, hallucination, bias, discovery and public perception
Generative AI introduces new risks that every police leader must address:
- Accuracy: AI-generated content may contain errors or omissions. Human review is essential.
- Hallucination: Generative AI can create plausible but false information, which may compromise investigations or court proceedings.
- Bias: If the AI’s training data reflects biases, outputs may perpetuate stereotypes or unfair conclusions.
- Discovery issues: AI-generated materials may complicate the discovery process, raising questions about data retention and disclosure.
- Public perception: Transparency is vital. Misuse or misunderstanding of AI can erode community trust.
Chief Rachel Tolber of the Redlands (California) Police Department states, “What we have done is developed a department policy to address the ways in which AI can and should be used. Additionally, our City has developed and implemented an overall City policy on this as well.”
Policy essentials: What chiefs must establish first
Before deploying generative AI, police leaders must set clear policies. Start by defining who can use these tools and for what tasks — with supervisor review required for any sensitive use. Data retention and disclosure policies must also be airtight: specify how AI-generated content is stored, for how long, and under what conditions, and ensure public records requests cover AI-generated materials with clear redaction and release procedures.
Departments should require that all AI-generated materials are clearly identified in reports, case files and court submissions. Audit trails and usage logs are essential to track prompts, outputs and user actions. Finally, oversight should not be an afterthought — appoint a dedicated officer or committee to review AI use and address emerging issues.
“The policies indicate that confidential information cannot be implemented into open AI systems, that there is a need for human oversight and verification (to address hallucinations) and transparency comes from open policies, communication with our community and Council Reports (purchase or adoption of new technology),” Tolber said.
Together, these steps ensure your department’s use of AI reduces legal exposure, preserves evidence integrity and reinforces public trust.
Chief’s briefing sheet: Key questions before adoption
- ⚖️ Is our use of generative AI legally compliant and ethically sound?
- 🗣️ How will we communicate AI usage to the public and courts?
- 🕵️♂️ What oversight mechanisms are in place?
- 🎓 Are staff trained to recognize and manage AI risks?
- 🗂️ How will we document and audit all AI interactions?
- 🚨 What is our process for responding to errors, bias, or public concerns?
Staying informed: Questions chiefs should ask
When evaluating generative AI solutions, chiefs should take a structured, skeptical approach. Begin with data and security: ask vendors what data sources and safeguards they provide, and ensure IT verifies secure deployment and privacy protections. Clarify who owns the data, how it’s stored, and whether any third-party access exists.
Traceability and bias mitigation should come next. Can analysts trace outputs back to their original prompts? Does the system integrate with a transparent data dashboard for algorithm auditing? How are errors corrected, bias detected, and performance verified over time?
Norris advises, “One of the most prominent questions is ‘Who owns the data?’ This is followed quickly by ‘How might data be shared with the vendor, and who is accessing it?’”
Finally, address compliance. Tolber recommends asking, “Are they CJIS-compliant? What systems are they pulling from and using? Are they open systems or internal? Are there guardrails built into your systems and if so, what are they?”
By making these questions part of early procurement and pilot discussions, chiefs can anticipate ethical, legal and operational risks before they become headlines.
Policy starter checklist
- ☑️ Define permitted uses of generative AI
- ☑️ Require supervisor approval for sensitive tasks
- ☑️ Establish retention and deletion schedules
- ☑️ Mandate disclosure of AI-generated content
- ☑️ Implement audit trails and usage logs
- ☑️ Regularly review bias and accuracy
- ☑️ Train staff on risks and oversight
- ☑️ Appoint an AI policy lead
Transparency is non-negotiable
Transparency is the cornerstone of trust. Police departments must document how generative AI is used, disclose its outputs, and invite community oversight. Every officer should be trained to recognize AI-generated content and to explain its role in police work. Oversight protects both the department and the public. I urge leaders to adopt the mindset of if you can’t explain it, you shouldn’t adopt it.
Officer do/don’t quick card
Do
- ✔ Use AI to assist, not replace judgment
- ✔ Flag AI-generated content in reports
- ✔ Ask for help if unsure about outputs
Don’t
- ✘ Assume AI is always accurate
- ✘ Use AI without departmental approval
- ✘ Ignore community concerns about AI use
Conclusion
Generative AI offers powerful new capabilities for law enforcement, but its adoption must be guided by clear policies, robust oversight, and unwavering transparency. Police leaders have a duty to ensure that technology serves the mission of public safety—and the values of justice and trust.
Tactical takeaway
Audit your department’s tech policies today — if they don’t mention generative AI, they’re already behind the curve.
Training discussion points
- How can your department safely test AI tools while ensuring legal compliance?
- What oversight and disclosure steps can protect against AI misuse?
- How should chiefs communicate AI adoption to maintain public trust?
How is your department balancing innovation and accountability when it comes to AI use? Share below.
