Alpharetta, Georgia, cops share data with thousands of Flock users, ranging from federal agencies to a fish and wildlife commission. The reasons why show how vast--and invasive--the network has become. Alpharetta, a prosperous suburb of Atlanta, Georgia, is home to about 67,000 people, served by about 120 local police officers. It also hosts several dozen cameras sold by Flock Safety, the increasingly controversial surveillance company that collects license plate data and other information and makes it searchable by police. WIRED has made this article free for all to read because it is primarily based on reporting from public records requests. Please consider subscribing to support our journalism.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Residents confront ICE agents on Atlantic Boulevard in the city of Bell on June 20, 2025. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search.

Safety Net Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Creator Hub Versus Say More Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Flock just announced new default privacy settings. Chase joined Mashable's Social Good team in 2020, covering online stories about digital activism, climate justice, accessibility, and media representation. Civil rights groups question the new policy shift. Controversial surveillance technology company Flock Safety has unveiled sweeping new privacy guardrails, seemingly intended to quell growing unrest over its devices' presence across the country. Days prior, CEO Garrett Langley went on a media run to convince the public that the company was not the latest cog in a dystopian surveillance state, but rather a purveyor of public safety.

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'We Ain't Seen Nothing Yet'--Trump's Mass Deportations Will Only Grow From Here Militias and far-right extremists believed they would be central to Trump's mass deportation plans. When Donald Trump won a second term as US president a year ago, members of violent militias and far-right extremist groups who had spent years boosting the lie that the 2020 election was rigged were ready to assist the president with delivering on one of his main campaign promises: mass deportations. "I'm willing to help," Richard Mack, a former sheriff who founded the far-right Constitutional Sheriffs and Peace Officers Association, told WIRED at the time, claiming he was in touch with Tom Homan, the man Trump installed as his "border czar." Tim Foley, head of the Arizona Border Recon, which describes itself as a "non-government organization," also told WIRED he was in contact with administration officials. William Teer, then head of the far-right Texas Three Percenters militia, wrote a letter to Trump offering his help.

A searchable database of public records concerning use of force and misconduct by California law enforcement officers -- some 1.5 million pages from nearly 700 law enforcement agencies -- is now available to the public. The Police Records Access Project, a database built by UC Berkeley and Stanford University, is being published by the Los Angeles Times, San Francisco Chronicle, KQED and CalMatters. It will vastly expand public access to internal affairs records that show how law enforcement agencies throughout the state handle misconduct allegations and uses of police force that result in death or serious injury. The database currently includes records from nearly 12,000 cases. The database is the product of years of work by a multidisciplinary team of journalists, data scientists, lawyers and civil liberties advocates, led by the Berkeley Institute for Data Science (BIDS), UC Berkeley Journalism's Investigative Reporting Program (IRP) and Stanford University's Big Local News.

State Sen. John Bramnick joins'Fox & Friends' to discuss the upcoming meeting with Gov. Phil Murphy and officials over mysterious drone sightings in their state. Two years ago, a Chinese balloon the size of three school buses hovered 60,000 feet in the air, drifting across the continental U.S. for seven days. It passed over sensitive security areas, including Malmstrom Air Force Base in Great Falls, Montana, that's home to stockpiles of missiles and nuclear defense infrastructure. Only after it was shot down did we learn this "civilian research airship" that President Biden claimed "was not a major security breach" was communicating with China through an American internet service provider and equipped with thousands of pounds of equipment, including a "massive surveillance payload." One would think the President of the United States and our nation's federal law enforcement agencies would have learned a lesson from this blatant security breach.

In recent years, urban safety has become a paramount concern for city planners and law enforcement agencies. Accurate prediction of likely crime occurrences can significantly enhance preventive measures and resource allocation. However, many law enforcement departments lack the tools to analyze and apply advanced AI and ML techniques that can support city planners, watch programs, and safety leaders to take proactive steps towards overall community safety. This paper explores the effectiveness of ML techniques to predict spatial and temporal patterns of crimes in urban areas. Leveraging police dispatch call data from San Jose, CA, the research goal is to achieve a high degree of accuracy in categorizing calls into priority levels particularly for more dangerous situations that require an immediate law enforcement response. This categorization is informed by the time, place, and nature of the call. The research steps include data extraction, preprocessing, feature engineering, exploratory data analysis, implementation, optimization and tuning of different supervised machine learning models and neural networks. The accuracy and precision are examined for different models and features at varying granularity of crime categories and location precision. The results demonstrate that when compared to a variety of other models, Random Forest classification models are most effective in identifying dangerous situations and their corresponding priority levels with high accuracy (Accuracy = 85%, AUC = 0.92) at a local level while ensuring a minimum amount of false negatives. While further research and data gathering is needed to include other social and economic factors, these results provide valuable insights for law enforcement agencies to optimize resources, develop proactive deployment approaches, and adjust response patterns to enhance overall public safety outcomes in an unbiased way.