Global law enforcement operation takes First VPN offline


Global law enforcement operation takes First VPN offline

Pierluigi Paganini
May 21, 2026

Police seized First VPN in a global crackdown, exposed its cybercrime users, and shut down infrastructure tied to ransomware and data theft.

A major international law enforcement operation has taken First VPN offline, a service that had become a quiet staple for ransomware crews, data thieves, and other cybercriminals trying to hide in plain sight.

“The coordinated action took place between 19 and 20 May and targeted the infrastructure behind one of the most widely used VPN services in the cybercrime underground.” reads the press release published by Europol. “The gathered intelligence exposed thousands of users linked to the cybercrime ecosystem and generated operational leads connected to ransomware attacks, fraud schemes, and other serious offences worldwide.”

Authorities seized dozens of servers across 27 countries, arrested the administrator, and carried out a search in Ukraine, cutting off an infrastructure that had been used in a wide range of serious investigations.

The service marketed itself as a privacy-first VPN with no logging and no cooperation with law enforcement, which made it appealing not just to ordinary users but also to threat actors looking to mask their activity. That’s the uncomfortable part of the VPN story: the same tools that help people protect privacy on public Wi-Fi or work securely from home are also useful for criminals who want to conceal their origin, route traffic through different regions, and make attribution harder.

“For years, the service, known as ‘First VPN’, was promoted on Russian-speaking cybercrime forums as a trusted tool for remaining beyond the reach of law enforcement. It offered users anonymous payments, hidden infrastructure, and services designed specifically for criminal use.” continues the press release. “‘First VPN’ had become deeply embedded in the cybercrime ecosystem, appearing in almost every major cybercrime investigation supported by Europol in recent years. Criminals used it to conceal their identities and infrastructure while carrying out ransomware attacks, large-scale fraud, data theft, and other serious offences.”

Europol said the service name kept resurfacing in major cybercrime cases, and Eurojust confirmed that investigators had been building the case for years through a joint effort led by French and Dutch authorities. 

What seems to have made this case especially valuable for investigators is that they didn’t just shut the service down, they also got inside its infrastructure before it disappeared. That likely gave them access to user records, connection data, and other evidence that can be used to map criminal activity back to real people and devices.

Authorities dismantled cybercrime infrastructure, including 33 servers and a service based in Ukraine, and seized domains linked to the operation: 1vpns.com, 1vpns.net, 1vpns.org, plus associated onion sites. They also notified users directly and shared information on hundreds of accounts with international partners, which suggests this may lead to follow-on investigations well beyond the VPN itself.

The bigger lesson is simple: privacy tools are not the problem, but criminal operators often rely on the same infrastructure normal users trust. Once that infrastructure is compromised, dismantled, or logged, the illusion of anonymity can disappear very quickly.

“The operation has already generated significant operational results at Europol’s level:

  • 21 Europol-supported investigations advanced through the intelligence obtained.”
  • 83 intelligence packages disseminated;
  • information linked to 506 users shared internationally;

“For years, cybercriminals saw this VPN service as a gateway to anonymity. They believed it would keep them beyond the reach of law enforcement. This operation proves them wrong. Taking it offline removes a critical layer of protection that criminals depended on to operate, communicate and evade law enforcement.” said Edvardas Šileris, Head of Europol’s European Cybercrime Centre

Follow me on Twitter: @securityaffairs and Facebook and Mastodon

Pierluigi Paganini

(SecurityAffairs – hacking, First VPN)







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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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