AI in the Squad Car: Cognyte's Quiet Expansion in US Policing

WASHINGTON, D.C. – October 29, 2026 – The modern detective's most formidable adversary is no longer the criminal mastermind, but the sheer volume of fragmented data left in their wake. From burner phones and encrypted messaging apps to sprawling financial records and social media footprints, the digital breadcrumbs of organized crime are growing exponentially faster than the human capacity to analyze them. This operational reality is driving a quiet but massive procurement shift across the American public safety sector—a shift that is increasingly relying on artificial intelligence to connect the dots.

Cognyte Software Ltd. (NASDAQ: CGNT), an Israeli intelligence software vendor spun off from Verint Systems in 2021, announced today that it has secured contracts with two new U.S. law enforcement agencies, one at the federal level and one at the state level. The deployments are designed to support field teams and analysts in high-stakes missions, ranging from locating missing persons to dismantling violent criminal networks and preventing terrorism.

These two new contracts bring Cognyte’s total number of new North American law enforcement clients to nine since the beginning of fiscal year 2027. According to the company, this recent surge means they have already achieved more than 80 percent of their regional target to close $20 million in deals this year, signaling a robust expansion into a highly competitive domestic market.

The Data Fusion Arms Race

Cognyte’s recent wins highlight an accelerating adoption of foreign surveillance and investigative analytics platforms by U.S. public sector entities. The financial turnaround for the Herzliya-based firm underscores this momentum. After navigating a complex spin-off and a turbulent market, the company has found its footing. It recently reported second-quarter fiscal 2027 revenue of $109.2 million—a 12 percent year-over-year increase—with software revenue jumping nearly 21 percent to account for the vast majority of its income. Adjusted EBITDA has also seen significant growth, rising over 35 percent to $14.9 million in the same quarter.

To capture this market share, the vendor is going head-to-head with entrenched domestic defense tech giants like Palantir Technologies, as well as specialized AI vendors like Voyager Labs. What sets this competition apart is the specialized nature of the deliverables. Law enforcement agencies are no longer just looking for data storage; they require sophisticated fusion engines capable of cross-referencing disparate datasets in real-time.

"Expanding our presence in the U.S. is one of our strategic priorities, and the concrete investments we have made in the region are paying off," said Elad Sharon, Chief Executive Officer at Cognyte, in the press release. "The addition of nine new customers, alongside expanding relationships with existing customers, reflects the strength of our technology and the demand across the region."

From the Battlefield to the Beat

To understand why American agencies are increasingly choosing these platforms, one must look at the practical benefits of the technology. While the specific federal and state agencies in today's announcement remain undisclosed—a standard practice in sensitive national security contracts—the firm's footprint in the U.S. is already well-documented. Previous public records reveal that the Texas Department of Public Safety spent $4.5 million to equip vehicles with FalcoNet mobile surveillance equipment. Other documented customers have included the Albuquerque Police Department, the New York State Police, and the Florida Department of Law Enforcement.

One of the most notable offerings deployed in the U.S. is FalcoNet, an IMSI-catcher or cell-site simulator. Often referred to colloquially as "Stingrays," these devices mimic legitimate cell towers, tricking nearby mobile phones into connecting to them. This allows authorities to monitor calls, intercept text messages, track internet traffic, and pinpoint a user's exact location, frequently without needing the real-time cooperation of telecommunications providers.

When combined with a broader AI-powered analytics platform, the data harvested from the field is fused with open-source intelligence, public records, and existing police databases. For a detective trying to disrupt a drug trafficking ring or locate a kidnapping victim in the critical first 48 hours, this automated synthesis of information is invaluable. It transforms weeks of manual cross-referencing into instantaneous, actionable insights.

The Accountability Paradox

Yet, for all the operational efficiencies these AI platforms offer, they bring a host of hidden pitfalls that public sector leaders must navigate. The Watson Perspective has consistently highlighted that the rapid deployment of AI often outpaces the regulatory frameworks designed to govern it, and law enforcement is no exception.

The deployment of cell-site simulators is particularly contentious among civil liberties advocates. Because these devices indiscriminately collect data from all mobile devices within a certain radius, they inherently capture the digital footprints of innocent bystanders not under criminal suspicion. This dragnet approach to data collection creates significant friction with Fourth Amendment protections against unreasonable searches and seizures.

Furthermore, the integration of artificial intelligence into the investigative process introduces the risk of algorithmic bias. If the historical data fed into these analytics platforms is flawed or biased, the AI's predictive policing models and network analyses risk amplifying those biases, potentially leading to the disproportionate surveillance of specific communities.

The press release emphasizes that the platform ensures "data sovereignty, including built-in governance capabilities so that they gain insights that are secure, trusted and audited." The company asserts that performance, accountability, and auditability are inherent to their solutions. However, independent privacy researchers and legal scholars frequently point out that the proprietary algorithms powering these platforms function as "black boxes." Without independent, third-party auditing, it is incredibly difficult for defense attorneys, judges, or the public to verify how a piece of digital evidence was synthesized or whether the AI's conclusions are fundamentally sound.

Auditing the Black Box

The stakes of deploying advanced surveillance technology were starkly highlighted by past controversies surrounding the broader intelligence software industry. In early 2023, investigative reports revealed that the company had won a tender to supply intercept spyware to a state-backed telecommunications firm in Myanmar shortly before a military coup. While the geopolitical context of Southeast Asia differs vastly from domestic U.S. policing, such incidents underscore the immense power—and potential for misuse—inherent in dual-use surveillance technologies.

For U.S. agencies, deploying these systems requires strict adherence to the FBI’s Criminal Justice Information Services (CJIS) security policy, which mandates rigorous data encryption, access controls, and personnel security. As a foreign vendor, the firm must continuously prove that its architecture is impenetrable to outside breaches and strictly siloed to protect American citizens' data. Meeting CJIS compliance is a high technical bar, but satisfying the constitutional demands of the American justice system is an even higher ethical one.

As the future of work in law enforcement becomes inextricably linked to artificial intelligence, police chiefs and federal directors find themselves in a precarious position. They are tasked with leveraging cutting-edge technology to combat increasingly sophisticated criminal networks, while simultaneously safeguarding the civil liberties of the communities they serve. The agencies that successfully navigate this transition will be those that view data governance not merely as a compliance hurdle, but as a core component of their operational strategy, ensuring that the pursuit of actionable intelligence never comes at the expense of public trust.