Sighthound Publishes Its Privacy-and-Evidence Approach to Responsible Vehicle Recognition
A practical model for keeping the public-safety value of ALPR (Automated License Plate Recognition) while addressing privacy, data-sharing, retention, and evidence concerns, built on edge processing, local control, and readily available redaction.
LONGWOOD, Fla. — August 26, 2026 — Sighthound Inc., an American computer-vision company specializing in vehicle recognition, video redaction, and edge AI hardware, today published its approach to responsible vehicle recognition: a set of architectural principles designed to preserve the public-safety value of automated license plate recognition (ALPR) while addressing the privacy concerns now driving public debate.
Over the past decade, ALPR and vehicle recognition have become important tools for public safety, helping agencies recover stolen vehicles, support Amber and Silver Alerts, and move time-sensitive investigations forward. Alongside that value, a national backlash has grown that agencies can no longer afford to ignore. The technology itself is rarely the flashpoint. What erodes public trust is uncertainty about how the data is governed once it exists — and that uncertainty is now surfacing in city council meetings, lawsuits, and cancelled contracts.
Sighthound's position is that these concerns have architectural answers, and that the future of vehicle recognition does not have to be an all-or-nothing fight between public safety and privacy.
“License plate recognition has earned its place in public safety — it closes cases that would otherwise go cold,” said Ryan Campbell, President of Sighthound. “What people worry about now isn’t the camera. It’s what happens to the data after it’s collected: where it goes, how long it lives, and who can reach it. Those are fair questions, and we built our products so an agency gets the public-safety value without a company like ours holding a national record of where ordinary people drive.”
Sighthound's approach rests on four principles
1. Process at the edge. Don't aggregate by default. Sighthound Retriever, the company's browser-based ALPR and vehicle recognition application, runs on the agency's own hardware. It works on-premises and offline, with no required cloud connection. A local agency gets what it needs for its own cases, and there is no central database of vehicle movements being automatically pooled as a byproduct for anyone to query, share, or sell. Agencies that need to share across jurisdictions can choose to — a deliberate decision they control, not something that happens to their data by default.
2. Keep the data with the customer, under their policies. Because processing happens locally, the footage and the license plate reads stay in the agency's own environment, governed by the agency's retention and access policies. Sighthound does not hold that data. A company cannot share or sell what it never collects.
3. Let privacy and evidence coexist through redaction.Sighthound Redactor gives agencies a way to protect bystanders and non-relevant people in footage before public release, across video, image, and audio, without losing the context that makes the footage useful as evidence. Redactor lets an agency meet a public-records obligation without exposing people who were never part of a case.
4. Make release defensible. Evidence handling should carry a record. The most recent Redactor release strengthened audit logging so that a redacted release can show what was done, reviewed, and approved, which is what holds up when a release is later challenged.
Underpinning all four, Sighthound's Compute hardware is built in the United States and runs at the edge, keeping sensitive data in the customer's environment and under United States jurisdiction.
“Regulation is coming for this category, and we think that is fine,” Campbell added. “Rules that call for local control, minimal retention, redaction, and auditable access describe the way we already build. We would rather help set that standard than fight it. Agencies still need capable vehicle recognition. They also need systems that can stand up to public scrutiny and a courtroom, and that is a question of how the data is handled, not whether the technology works.”
Sighthound is making its approach available as a discussion starting point for agencies, policymakers, and community stakeholders weighing how vehicle recognition should operate in a more regulated environment.
To discuss how this approach fits a specific deployment, request a technical fit review at sighthound.com/products/alpr.
About Sighthound
Sighthound Inc. is an American computer-vision company that has spent over a decade turning video into actionable data. Its products include Sighthound Retriever, a browser-based ALPR and vehicle recognition application powered by ALPR+; Sighthound Redactor, AI-powered video, image, and audio redaction software for privacy and public-records workflows; and Sighthound Compute, edge AI hardware built in the USA. Sighthound's software is designed to run on the customer's own hardware, on-premises and offline, keeping data under the customer's control. Learn more at sighthound.com.
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