Flock mandates surveillance audit tool amid transparency concerns
The company claims its new 'Audit Assistance' feature has identified abuse, but privacy advocates and legal experts are calling for independent verification of the technology’s efficacy and transparency.
Surveillance technology company Flock has announced a series of policy changes designed to curb the misuse of its automated license plate reader systems by law enforcement. The measures include making its 'Audit Assistance' tool mandatory for all customers by the end of the year, reducing standard data retention periods, and introducing stricter protocols for extended data storage.
The 'Audit Assistance' tool, originally launched in April, is now required for all users. Flock states that the tool detects abnormal activity and flags it for administrator review, potentially locking out users identified as problematic until an intervention occurs. Since its introduction, the company reports that more than one-third of its customers have enabled the feature. Flock clarified that the tool is not based on machine learning or artificial intelligence, but rather functions as a data-based system that flags atypical search patterns, such as querying the same license plate with multiple case codes.
In addition to the audit mandate, Flock has reduced its standard data retention period from 30 days to seven days. The company introduced an 'Evidence Mode' that allows for extended storage only in exceptional cases, requiring a specific case number to activate. Customers can also now set limits on the data shared with other users based on the type of offence under investigation.
These announcements follow a history of reported misconduct involving Flock’s technology. Earlier this year, three former Georgia sheriff’s deputies were arrested for allegedly using the cameras to stalk individuals with whom they had personal relationships. Bibb County Sheriff David Davis credited the new Audit Assistance tool with uncovering the misuse in that specific instance.
Despite these measures, privacy advocates have criticised the lack of technical transparency. The American Civil Liberties Union, the Electronic Frontier Foundation, and the Brennan Center have called for independent audits and stronger legal constraints. Critics argue that without disclosing the algorithm’s training data or effectiveness metrics, it is impossible to verify whether the tool is a genuine security measure or merely a superficial response to public pressure.
