AI companies need usage data to align pricing with costs, improve products, identify churn risks, and grow revenue through informed decisions. The AI Paradox There is a paradox: AI companies exist to extract intelligence from data, yet many have almost no visibility into how their own software is being used. They design complex models, introduce their product, set a price, and then hope that customers renew their contracts. In a space as fast-paced as AI, such a strategy is no longer safe. Without usage as a guide, price decisions are made on assumptions rather than facts. Product roadmaps are driven by story, not by usage. Customer success teams learn about customer churn when renewal dates are approaching. In reality, usage intelligence has become one of the most important signals in an AI business, guiding everything from pricing strategy to product development and long-term monetization. Why AI Changes the Stakes for Usage Data AI changes the economics of software and, with it, the relevance of usage visibility. The economics of existing software products are linear in cost, whereas those of AI products are not. Compute, energy consumption, as well as third-party model licenses, can all be material variable costs. If usage increases rapidly (through enterprise adoption, agents, or workflows), the cost can grow faster than the revenue generated if pricing is not aligned with consumption. The opposite scenario is just as dangerous. If customers are not using key capabilities, churn may be approaching long before a renewal conversation begins. Research shows the extent of this visibility gap. For AI vendors, visibility is important on the commercial side, too. They need to know how customers are using the AI capabilities they sell. IDC research found that 48% of software buyers want better visibility into their usage. The pace of change is also a concern, with 70% ranking rapid change in the AI ecosystem among their top three AI-related security risks. Pricing, packaging, and business models must evolve at the same pace, and that requires reliable usage intelligence. What Usage Intelligence Actually Looks Like Of all the capabilities that make up a software monetization strategy, usage intelligence is the one most commonly treated as optional — and the one that most consistently separates companies that adapt from those that don’t. It is the feedback loop that connects real-world product consumption back to the decisions that shape pricing, packaging, and product direction. At the instrumentation layer, usage intelligence starts with telemetry data. This includes activation events, entitlements consumed, feature-level data, daily and monthly active users, and time-to-value metrics that track how quickly customers get value from the product. Thales Sentinel can also collect usage data from on-premises, offline, and disconnected environments, so vendors are not limited to tracking always-connected software. The operational layer is where all the telemetry data is made actionable. Usage dashboards in CRM systems enable account teams to see how customers use the software. Health scores can even detect reduced engagement well before any risk of renewal is seen. The enforcement layer makes sure that usage is consistent with contract rights. This is particularly important for consumption-based or token-based pricing schemes. Tracking accuracy is essential for determining customer fairness and vendor revenue. Finally, there is the feedback loop. The insights from usage need to flow back to those responsible for pricing, packaging, product development, and customer success. Without the feedback loop, the data is just more information. Pricing specialists have long argued for incremental experimentation. Research from Simon-Kucher reveals that companies that make frequent, data-informed pricing adjustments tend to achieve stronger growth than those that depend on static pricing models. Usage intelligence provides the evidence required to make those adjustments with confidence. How Usage Data Enables Multiple Business Models Business model agility is increasingly becoming a competitive advantage for AI companies, yet flexible business models cannot exist without trustworthy usage data beneath them. Consumption-based pricing, outcome-based pricing, token models, and hybrid subscription structures all depend on accurate measurement of how customers use the product. There is demand for these models, too. IDC research found that 45% of software buyers prefer usage-based models. In practice, most AI companies adopt a gradual sequence. They begin with subscription licensing to establish predictable recurring revenue. As adoption grows, they instrument feature usage and engagement patterns. That data then enables the introduction of consumption or token-based layers tied to high-value AI capabilities. One AI audio-recognition company did just that. It moved from perpetual licensing to several subscription tiers using Thales Sentinel. Its usage data showed which features customers used most, which helped the company decide how to structure those tiers without making changes to the core technology. Another AI recognition vendor delivering SDKs for embedded systems took a different approach using Sentinel. Their trial licenses expire after a defined number of executions instead of after a fixed time period. This usage-based trial model demonstrates genuine customer interest, giving them a clearer indicator of product-market fit than a simple time-limited evaluation would. Both examples show that usage intelligence transforms monetization strategies from guesswork into evidence-based decision-making. From Data to Decision: Three Practical Applications The real value of usage intelligence lies in informing concrete business decisions. Preventing churn is one of the most immediate uses. Feature-level engagement metrics can detect declining usage several months before a renewal date. This enables customer success teams to step in and show the value or introduce underutilized features, preventing a customer from leaving. Upsell and expansion opportunities can also show up in the usage data. Seat utilization may show that a team is close to capacity and needs more licenses. Feature usage can show when a customer is getting enough value from certain capabilities to justify moving to a higher tier or adding consumption-based features. Usage data can also inform the product roadmap. Rather than relying on assumptions about what customers want, AI companies can see which features people use and which get little attention. This is key in a market full of similar AI capabilities. Companies need to know which parts of their product customers value and are willing to pay for. There is also a need for the collection of usage intelligence to be privacy-conscious. For vendors operating under frameworks such as GDPR or the EU’s AI Act, it is necessary to design telemetry systems that prioritize aggregated data collection over personal data collection whenever possible. The Organizational Challenge However, technology alone is not enough to create usage intelligence. Businesses must also ensure that the intelligence is available to those who can act on it. Usage intelligence only delivers value if it reaches the people who can act on it. Pricing teams need consumption data to make confident adjustments to their models. Product teams need feature engagement data to inform roadmap decisions rather than rely on assumptions. Sales teams need account-level signals to time their conversations correctly. Customer success teams need early warning indicators to intervene before disengagement becomes churn. Finance teams need usage trends to build credible revenue forecasts. Getting that data to all of those functions consistently and in a usable form requires intentional data architecture and a shared organizational commitment to treating monetization as a cross-functional capability rather than a single team’s responsibility. This becomes harder as companies add more AI products. If usage data is split across separate products, teams may struggle to see how a customer is using the wider portfolio. Bringing the data together can show where products are being heavily used, where adoption is low, and where there may be room to expand an account. Building Systems Around Innovation The AI companies that sustain long-term commercial success are the ones who built a system around it that can learn, adapt, and improve as markets shift and customer needs evolve. Usage intelligence is what makes that system adaptive. Without it, pricing decisions are made on assumptions, packaging tiers are defined by intuition, and churn arrives without warning. With it, every commercial decision — what to charge, how to package, what to build next, which customers need attention — is grounded in evidence rather than expectation. In an industry defined by rapid innovation, the AI companies that listen to their own usage data will be the ones best positioned to turn experimentation into sustainable growth. About the Author: Kirsten Doyle has been in the technology journalism and editing space for nearly 24 years, during which time she has developed a great love for all aspects of technology, as well as words themselves. Her experience spans B2B tech, with a lot of focus on cybersecurity, cloud, enterprise, digital transformation, and data centre. Her specialties are in news, thought leadership, features, white papers, and PR writing, and she is an experienced editor for both print and online publications. She is also a regular writer at Bora. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, artificial intelligence)
Capcom disclosed a data breach exposing 11.7 million email addresses and account details after hackers accessed its CAPCOM ID service. Japanese gaming company Capcom has disclosed a data breach discovered on October 19 involving its CAPCOM ID service, which provides access to online services and games. As of October 22, attackers stole 11,774,247 email addresses, along with account details including creation dates, last login dates, login counts, and the IP addresses used for the latest logins. “We have recently discovered that unauthorized access by a third party occurred to our online membership service “CAPCOM ID” (*1) , resulting in the leakage of some customer information.” reads the company’s notice. “Number of leaked email addresses and system-specific identification information: 11,774,247 (worldwide total)” In 43 cases, hackers exposed date of birth, gender, country/region, and nickname. Capcom also reported that attackers illegally changed passwords on 196 accounts, and the company has already reset them. The company said the incident exposed personal information belonging to a small number of individuals. Capcom has activated its incident response procedures and confirmed that services using CAPCOM ID remain operational. The company has notified the relevant authorities, is investigating the breach, and has begun contacting affected customers through a dedicated support channel. The company also urges users to watch out for phishing attempts involving emails impersonating Capcom or its partners, particularly those containing suspicious links or attachments. It says it will investigate the incident, address the identified causes, and strengthen its security measures to prevent similar breaches. “In response, we implemented emergency measures and began identifying and addressing the attack method.” states the notice.”A task force was established within the company to gather information and take measures to prevent further damage. The investigation revealed that some customer information registered with “CAPCOM ID” had been leaked. In addition, unauthorized password changes were confirmed for some accounts. On the same day, the incident was reported to the Security Oversight Committee, our external advisory organization.” Capcom was previously targeted by the Ragnar Locker ransomware gang in November 2020. The attackers claimed to have stolen 1 TB of data, potentially affecting up to 390,000 people. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, data breach)
Microsoft says Teams will begin using AI-powered phishing detection in early November 2026 to detect malicious behavior in messages. The rollout is part of a targeted release phase expected to end by mid-November, with the new security feature likely reaching general availability worldwide between early and mid-December. Microsoft first revealed in a Microsoft 365 Roadmap entry last week that the AI-powered phishing detection will use conversation context (without retaining message content) to identify phishing and suspicious behavior in higher-risk messaging scenarios, such as external first-contact chats. "Microsoft Teams is introducing AI-powered, context-aware phishing detection to help identify phishing, social engineering, impersonation attempts, and deceptive intent," the company said in a Microsoft 365 Message Center update on Wednesday.
Cybersecurity researchers have discovered an artificial intelligence (AI)-driven intrusion ecosystem run by affiliates of the INC and The Gentlemen ransomware groups. Central to the operation is an "Autonomous Red Team Kill Chain Framework" referred to as Phantom. The threat actor is assessed to be Russian-speaking and functions both as an intrusion and data-extortion operator, as well as an
INTERPOL’s Operation Serengeti III led to 1,462 arrests, disrupted 10,909 malicious networks and recovered $105 million across 21 countries. INTERPOL announced the results of Operation Serengeti III, a multinational cybercrime crackdown that led to 1,462 arrests, disrupted 10,909 malicious networks and recovered more than $105 million in illicit funds. The operation brought together law enforcement agencies from 21 countries between June 29 and August 31, 2026, targeting online fraud, ransomware, business email compromise (BEC), scams and digital extortion. “One of Africa’s largest cybercrime enforcement operations saw law enforcement agencies from 21 countries arrest 1,462 suspects, disrupt 10,909 malicious networks and recover more than USD 105 million in illicit funds.” reads the agency’s announcement. “Authorities identified 65,929 victims targeted by cybercriminal networks, underscoring the scale and sophistication of today’s digital threats. Financial investigations linked criminal activity to losses exceeding USD 385 million. ” The scale of the operation is striking. Investigators identified 65,929 victims, while financial investigations linked the criminal activity to losses exceeding $385 million. The figures highlight how cybercrime continues to generate substantial profits by combining technical capabilities with familiar forms of deception. The results also show why tackling these operations requires more than taking down websites or blocking malicious IP addresses. Investigators need to identify the people behind the infrastructure, follow the money and connect evidence collected across jurisdictions. Uganda saw one of the largest actions in the operation. Authorities broke up two international fraud networks and arrested 365 foreign nationals suspected of running online investment and pension scams targeting people across Asia. Investigators found call centres where staff followed prepared scripts to convince victims to invest through fake trading platforms and websites. The scams targeted people who believed they were making legitimate investments, including pensioners. These cases show how criminal groups combine technology with organized social engineering to deceive victims and steal their money. Source: Interpol – Suspected Scam Call centre in Uganda. In Angola, authorities shut down two illegal cryptocurrency mining centres, arrested nine suspects and seized over 2,000 mining devices and six illegal power stations. The electrical equipment was worth more than $1 million. In Nigeria, investigators arrested a suspect linked to phishing and business email compromise (BEC), which involved stealing software credentials and collecting about 2.1 million email addresses. They also recovered a luxury car suspected of being bought with criminal money. A separate Nigerian investigation traced over $2.4 million linked to an investment scam promoted through messaging apps, with much of the money laundered through cryptocurrency. Authorities also targeted loan app fraud in Tanzania, vehicle import scams in Gabon and arrested 30 suspects in the Republic of Congo. Operation Serengeti III relied on intelligence provided before the enforcement phase. Investigators from 20 African countries and the United States received information about suspicious domains, IP addresses and command-and-control infrastructure associated with criminal activity. Private-sector cybersecurity companies also supported the operation, providing threat intelligence, technical analysis and investigative expertise. INTERPOL listed Group-IB, Kaspersky, Fortinet, TrendAI, Team Cymru, Uppsala Security, Cybercrime Atlas and Meta among the participating partners, alongside other organizations. This cooperation matters because criminal infrastructure rarely respects national boundaries. A phishing domain, a compromised account, a fraudulent investment platform and the bank accounts used to move stolen funds may all involve different countries and service providers. Investigators need to connect those elements quickly enough to identify suspects and preserve evidence. INTERPOL’s 2026 African Cyber Threat Assessment Report highlights the growing role of AI in cybercrime as financial losses rise. Criminals can use AI to create convincing messages, improve social engineering and target more victims. While AI does not replace the need for criminal infrastructure or ways to move stolen money, it can make scams easier to repeat and harder for victims to recognize. INTERPOL said several investigations remain active. Authorities are continuing to identify suspects, trace illicit proceeds and analyze digital evidence recovered during the operation. That qualification is important. Arrests and infrastructure disruptions are meaningful enforcement results, but they don’t automatically establish how many criminal organizations have been dismantled permanently, how much of the stolen money will reach victims, or whether other operators will rebuild the disrupted services. “Cybercrime networks operate without regard for borders, exploiting technology to target victims on an unprecedented scale.” INTERPOL Director of Cybercrime Neal Jetton said. “Operation Serengeti III reinforces a clear message: while cybercrime is increasingly borderless, international cooperation remains one of the most effective tools for disrupting criminal networks, recovering criminal assets and protecting communities from digital harm.” Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Operation Serengeti)
Law enforcement agencies have disrupted nearly 11,000 cybercriminal networks and arrested more than 1,400 people in Operation Serengeti III, which targeted business email compromise, ransomware, and online scams. Coordinated by Interpol, the operation involved authorities from 21 countries and private partners like Team Cymru, Kaspersky, Group-IB, and Fortinet, in a major crackdown on cybercrime across Africa. The action unrolled between June 29 and August 31 and led to the recovery of more than $105 million in illegal funds. In a press release today, Interpol says that Operation Serengeti III has resulted in the arrest of 1,462 suspected cybercriminals and the dismantling of 10,909 malicious networks. The investigation identified 65,929 victims and estimated financial losses at more than $385 million.
SafePay ransomware has hit 600+ organizations since 2024, using stolen credentials, VPN access and double extortion to pressure victims into paying. CISA, the FBI, the US Secret Service, Germany’s Landeskriminalamt Rheinland-Pfalz, and two UK law enforcement agencies jointly published an advisory this week on SafePay, a ransomware group they describe as one of the most active operations currently running. The group has breached at least 600 organizations across critical infrastructure sectors since August 2024, spanning North America, South America, Europe, and Australia. “Since August 2024, SafePay ransomware group’s operations have affected a wide range of businesses and critical infrastructure in North America, South America, Europe, and Australia. SafePay ransomware was among the most active ransomware groups in 2026. The SafePay threat actors have impacted at least 600 separate victims from multiple critical infrastructure sectors.” reads the joint advisory, “The authoring agencies encourage organizations to implement the recommendations in the Mitigations section of this advisory to reduce the likelihood and impact of SafePay ransomware incidents.” SafePay appeared in mid-2024 as an independent ransomware group that operates without affiliates. This is unusual because many major ransomware groups work like franchises, allowing other criminals to use their tools and services. SafePay grew as groups such as BlackCat and LockBit declined, and its activity has continued. The advisory includes findings from FBI, US Secret Service, and German investigations through August 2026. SafePay’s preferred entry points are exposed credentials, brute force password attacks, and VPN access. “The FBI, USSS, and cybersecurity researchers observed SafePay threat actors obtaining initial access [TA0001] to organizations through various means, including credential exposure, implementing brute force password attacks [T1110], and exploiting virtual private network (VPN) access.” states the advisory. “SafePay threat actors use the IP addresses of the VPN provider “Mullvad” to attain initial access—specifically, SafePay threat actors use phished account profiles to access victim networks to exploit the following known Common Vulnerabilities and Exposures (CVEs) for edge device products” Security teams should watch for unusual login attempts coming from Mullvad VPN IP addresses. SafePay also uses stolen account credentials and automated scripts to exploit known vulnerabilities in internet-facing devices. These include Cisco VPN flaws from 2020 and 2023, a Veeam vulnerability, and a SonicWall access control issue. Once inside a network, the attackers rely on common tools rather than specialized malware. They use PsExec, WinRM, RDP, and PowerShell to move between systems. A PowerShell script called ShareFinder.ps1 helps them find shared folders and identify valuable data. Mimikatz is used to steal credentials, while AnyDesk and TeamViewer provide remote access. To steal data, the group uses tools such as FileZilla, RClone, WinSCP, and even OneDrive. Encryption uses a ChaCha20 stream cipher combined with RSA for key exchange, with methods customized based on file type and size. Files get the .SafePay extension appended. “Data encrypted with the SafePay ransomware variant (appended with .SafePay) cannot be recovered without the decryption key. SafePay threat actors hold the encrypted data for ransom and typically demand a subset of company revenue in cryptocurrency in exchange for decryption software.” states the advisory. “SafePay threat actors leave a ransom note on the victim’s system, typically on the desktop or root directory, advising them that SafePay locked their system and to reach out over a chat portal at a TOR .onion website link with a victim ID as the login. “ Paying the ransom remains the only path to decryption for encrypted files, and even that isn’t guaranteed to work. The group runs a double-extortion model: steal first, encrypt second, threaten to post the stolen data publicly if the victim doesn’t pay. After encryption, SafePay sends victims to a Tor-based chat portal for negotiations. A representative also makes phone calls to apply pressure. If no payment comes, everything goes on the leak site. The group also deletes Volume Shadow Copies and resets network-attached backup devices to factory settings, which is the operational equivalent of cutting off every exit before the fire starts. The advisory recommends several basic security measures. Keep internet-facing devices, especially VPNs and firewalls, up to date. Prioritize vulnerabilities listed in CISA’s Known Exploited Vulnerabilities catalog. Enable multi-factor authentication (MFA) for VPNs, webmail, and accounts that access critical systems. Limit the number of accounts allowed to use administrative tools. SafePay relies on legitimate Windows tools to move through compromised networks, making its activity harder to detect. Organizations should also keep backups offline, encrypted, and immutable so attackers cannot access, change, or delete them. CISA also recommends reviewing all accounts with admin privileges, setting time-limited access where possible, and disabling command-line tools for any account that doesn’t need them. The advisory includes IOC files in STIX XML and JSON format, full MITRE ATT&CK mapping, and complete file and IP indicators. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, ransomware)
Microsoft on Wednesday disclosed that a China state-aligned actor dubbed Storm-0940 was behind the recent supply chain compromise of the ConnectWise Cloud Backup application. During the incident, Storm-0940 used ARTEX, a Go-based agentic AI penetration testing framework that was made closed-source in the wake of its abuse in cyber attacks targeting South Korean financial institutions, to speed
Chrome is getting new scam protections that use Gemini to detect deceptive websites and warn users when they copy suspicious commands from ClickFix attacks. The changes are part of Enhanced Protection in Safe Browsing, which Google claims makes users ten times safer from phishing, social engineering, notification abuse, and other scams compared with Standard Protection. Chrome is also adding a new "suspicious site" warning for pages that show deceptive patterns but haven't reached the confidence level required for its existing full-page red warning. "Suspicious site warnings notify users when a page exhibits deceptive patterns commonly associated with known malicious sites," the company explained. "By tailoring the warning UI in this way, we are able to safely expand our coverage to display a higher volume of protective warnings."
A Russian satellite made a close approach to a commercial radar satellite linked to Ukraine, raising concerns about surveillance and security in space. A Russian-made satellite called Kosmos 2612 got very close to ICEYE-X36 last week, a commercial Earth-observation satellite that may support countries like Ukraine. CNN reports it may have come within less than a mile. Orbital monitoring company LeoLabs says the behavior is consistent with rendezvous and proximity operations, meaning maneuvers designed to bring two spacecraft together. “A rare move by a Russian-made satellite may have brought it within less than a mile of a commercial Earth-observation satellite that may be used to provide national security services to countries such as Ukraine.” states CNN. “It’s not clear what, exactly, the Russian satellite, called Kosmos 2612 or Cosmos 2612, intended to accomplish with its close approach. But the behavior was consistent with rendezvous and proximity operations, or maneuvers carried out in orbit that are designed to bring two spacecraft together, said orbital monitoring services company LeoLabs, which tracks space junk and activity in Earth orbit, in a statement.” It is unclear what Russia wanted to achieve. LeoLabs said “While the intent remains uncertain, the behavior warrants continued monitoring.” Astrophysicist Jonathan McDowell noted that this could be the first known case of one spacecraft approaching another without permission. This is different from docking, when two spacecraft intentionally connect, a common practice in space. The events unfolded over several months. In May, four Russian-controlled satellites changed their orbits to align with ICEYE-X36, according to a June report by the Royal United Services Institute. On October 15, Kosmos 2612 began moving closer to ICEYE-X36, reducing the distance from 350 kilometers to less than 5 kilometers by early October 16. It stayed nearby, making small adjustments through the weekend, before starting to move away on Monday. The War Zone (TWZ) reports that Kosmos 2612 was one of six satellites launched together on April 17. The closest point is disputed. LeoLabs says it came within roughly 500 meters during part of the event, but warns that the repeated maneuvers and awkward tracking geometry make separation estimates uncertain. Space.com reports the satellites may have been a few meters apart at one point, and TWZ’s reading of public tracking data points to about 26 meters. The target matters. ICEYE operates about 80 radar satellites that image the Earth through clouds, day and night, and ICEYE-X36 belongs to its US subsidiary. At least some ICEYE satellites provide imagery to Ukraine, and in January the company signed a contract firming up that relationship, saying it gives Ukraine’s Ministry of Defence persistent situational awareness on tactical timelines. A Ukrainian official told Axios that Ukraine’s military works with ICEYE but couldn’t say whether this specific satellite is involved. Russia appears to be paying close attention to ICEYE-X36. Changing a satellite’s orbit to match another spacecraft’s path uses a lot of fuel, so it is not a decision taken lightly. Four Russian satellites made similar changes in May. However, it remains unclear whether ICEYE-X36 is specifically used to collect images of Ukraine. Close approaches between satellites are not new, but this case is different. Russia, the United States, and China have all moved satellites close to those operated by other countries, mainly in geostationary orbit. Kosmos 2612 operates in low Earth orbit. As far as is publicly known, Russia has carried out similar approaches there, but the United States and China have not. Victoria Samson of the Secure World Foundation pointed to two earlier Russian cases. In 2020, Kosmos 2543 came within about 30 kilometers of USA 245. In 2022, Kosmos 2558 approached USA 326 to within about 70 kilometers. Both targets were classified US spy satellites operated by the National Reconnaissance Office. ICEYE-X36, by contrast, is a privately owned satellite, and Kosmos 2612 came much closer than in those earlier cases. Space law professor Aaron Brynildson told Axios that there is no strict legal limit on how close satellites can approach each other. However, countries generally avoid getting within six miles of another satellite. Why do it? Samson lists a few options. Russia may want to learn the satellites’ capabilities and what they’re studying, or it may have interfered with sensors or communications. She leans toward a different reading: she thinks Russians are getting frustrated, in a stalemate for about six months, and this is a way to show military prowess in a way Ukraine can’t answer, since Ukraine matches Russia in drones but not in space. RUSI’s June report is blunter about the options. It lists intimidation or interference through jamming, which uses radio noise to stop satellites from communicating. It adds that the range of options for protecting an ICEYE satellite is limited, and that defending satellites is a famous dilemma. LeoLabs says the event doesn’t establish hostile intent, but shows capabilities that could support intelligence collection, inspection, coercive signaling, interference or preparation for future counterspace operations. ICEYE’s response was short: the company is aware, tracking closely, and says its constellation architecture is designed for continuity and resilience. CNN says it asked Russia’s defense ministry and Roscosmos for comment. The US Space Force had already described Russian satellites positioned near a Western commercial radar satellite used by Ukraine for imagery intelligence, according to Axios. Private companies and independent observers, rather than the US Space Force, revealed the satellite encounter, highlighting the need for greater government transparency as space becomes more contested. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, satellite)
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