Efforts are underway to strengthen critical AI infrastructure and improve data sharing across law enforcement agencies nationwide. A major priority is to promote open, interoperable AI platforms that help break down data silos, reduce duplication of effort, and modernize workflows for police and public safety organizations. These initiatives aim to cut crime, speed up investigations, enhance coordination, and enable more effective use of resources by leveraging advanced AI capabilities alongside robust, integrated data systems. As law enforcement agencies today face an ever-expanding landscape of digital evidence, increasing demands for transparency, and the urgent need to respond to public safety threats in real time, the importance of open and interoperable AI platforms has never been more critical.
While “interoperability” may sound like technical jargon, its implications for law enforcement are profound. Open standards enable agencies, regardless of size or location, to securely share information, deploy proven analytics, and respond more swiftly to emergencies. Rather than each agency investing in redundant, proprietary software, an open AI framework lays the groundwork for centralized, scalable data-sharing solutions – from small rural sheriff’s offices to major urban departments like the LAPD.
As governments and AI solution providers across the country digest and begin to implement the Trump administration’s new AI framework, there is a clear opportunity for LEAs to operate more effectively and efficiently. However, law enforcement’s ability to realize the benefits of AI will hinge on systems that are open, interoperable, and transparent.
From Data-Sharing to Real-Time Response
AI is already making a measurable difference for several of law enforcement’s most pressing challenges:
- Modern AI systems can ingest terabytes of multimedia evidence (CCTV, social media, phone records) and surface actionable leads in a fraction of the time.
- As synthetic media and deepfakes become more sophisticated, AI-powered forensics can identify manipulated video evidence or fabricated voices, protecting the integrity of investigations.
- Open AI systems equipped with automated redaction remove personally identifiable information (PII) before footage is released, balancing transparency and privacy.
Meeting Growing Public Demands for Transparency
Open access unlocks a new world of advantages for LEAs, but privacy laws require careful handling of victims, bystanders, and minors’ data. Old, manual review processes are no longer tenable. AI-driven redaction and evidence management make it feasible to respond at speed and scale, reducing backlog while ensuring privacy is preserved.
Furthermore, open AI frameworks can be peer-reviewed, audited, and adapted to new guidelines as standards evolve, making transparency a built-in feature rather than an afterthought.
Turning Data Overload into Investigative Insights
Open collaboration platforms designed with interoperability at their core are revolutionizing digital evidence management for LEAs across the country. Rather than operating as walled gardens, these systems collect, index, and analyze data from across the technology spectrum, including body-worn cameras, ALPRs, drones, social media, and more. This enables agencies to see the full picture, link disparate events, and accelerate case closures.
During a recent webinar presented by the Digital Government Institute, a panel of former law enforcement officials shared actionable use cases, demonstrating the force multiplier that AI has become for law enforcement:
- Rob Gerber, former Homicide Investigator with the Antioch Police Department: Leveraging AI helps vastly accelerate investigation and FOIA workflows.
- Eddie Wagner, former Assistant Chief, Border Patrol HQ, Department of Homeland Security: 1,000s of hours of videos that could never be manually reviewed in full, but completely doable with AI-powered tools.
- Paul Haag, former Unit Chief, Strategic Vehicle Technologies Unit, FBI: Previous methods of combing through audio/video data were incredibly inefficient. AI is allowing agents to be out in the field doing actual investigative work instead.
For AI to truly move the needle in public safety, it must address core operational and legal requirements, while running on secure, compliant cloud infrastructure – a legal and operational necessity. Only then can agencies ensure both scalability and robust data protection.
The Risks of Proprietary, Closed AI
Not all AI frameworks are created equal. Historically, proprietary “black box” systems have dominated public sector contracts, leading to a number of issues:
- Siloed platforms slow down investigations when evidence cannot be shared across jurisdictions or requires custom conversion. Critical leads are lost in translation.
- Closed systems often lack transparency in how models are trained, raising concerns of baked-in algorithmic bias—disproportionately affecting marginalized communities.
- Agencies locked into one provider have less flexibility to innovate or adapt to new policy requirements, resulting in higher costs and less accountability.
By contrast, open and ethical frameworks allow agencies to benefit from shared innovation while maintaining community trust. These frameworks foster robust third-party oversight, transparent model documentation, and adaptable interfaces to accommodate changing laws and needs.
Responsible Innovation is Non-Negotiable
The pace of technological change often outstrips policy, but that is no excuse for treating transparency, privacy, and ethics as afterthoughts. The new generation of AI providers must reject the false choice between security and openness; public safety challenges require solutions that are both powerful and accountable. Open, scalable, and ethical AI architecture isn’t a nice-to-have – it’s a public mandate.
Transforming Justice Through Ethical AI
From faster evidence processing and predictive policing to streamlined public records and stronger community trust, the opportunity for AI to enhance public safety is clear. But realizing that potential requires a commitment to open and interoperable systems, rigorous privacy protection, and transparency.
As the federal government shapes America’s AI future, public safety leaders, technologists, and the public itself must insist on systems that prioritize collaboration over silos, and ethics over expediency. Only then can we ensure AI serves both justice and democracy in an age of digital transformation.
