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)
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.
AI agents paying for API calls with the x402 SDKs could sign a payment for their full balance of a supported token if a malicious API asked for it, security firm V12 said on October 22. The cause was the SDKs' default client, which set no spending limit until releases on August 18 added one. The risk was limited to agents who automatically signed payments and whose apps set
Cloud hosting provider Railway published an urgent warning to users after detecting suspicious activity on its systems that store customer environment variables earlier this week. The company says its investigation indicates that, in some cases, an unauthorized third party may have obtained decrypted variables and urges all users to proactively rotate secrets and credentials. “Our investigation to date indicates that an unauthorized third party may have accessed encrypted environment variables and, for some of them, obtained the decrypted contents,” Railway says. “Environment variables may contain secrets such as API keys or database and registry credentials,” the company clarifies. Railway is a cloud platform for developers to deploy and run applications, APIs, databases, and various backend services. It has around 4.8 million users and hosts more than 16 million services.
Right now, there is probably an AI agent running on a laptop somewhere in your network. It stores API keys and database passwords, can access your internal systems, and works without anyone watching. Nobody reviewed its access. Nobody owns it. If an employee had that much reach with that little oversight, you would call it an insider risk. So why do we still call the agent a productivity tool?
A critical Signal flaw could keep calls active after hang-up, exposing microphone or camera. Update to a fixed version immediately. A critical vulnerability, tracked as CVE-2026-113380 (CVSS score 9.3 of 10), in Signal ’s calling library could allow a malicious caller to interfere with call termination, potentially leaving the victim’s microphone and camera active after they believe the conversation has ended. The flaw affects versions before 2.72.0 of the library RingRTC, which the messaging app uses to provide voice and video calling. It highlights a security problem that deserves attention beyond a single messaging application: ending a call should be an immediate control action, not something that can be delayed by traffic coming from the other participant. According to the technical report published on GitHub, the vulnerability stems from how RingRTC handles events in the finite-state machine (FSM) that manages a call. The application places events in a queue for processing, but a malicious peer can flood that queue with events and prevent the local hang-up operation from being handled in a timely manner. “A local hang-up is not a kill switch: it is just one more event placed at the back of the call’s finite-state-machine event queue. The remote peer is trusted with that queue — there is no rate limit on peer-originated events and no priority lane for hang-up.” reads the technical report published on GitHub, “Flood the queue with junk and the hang-up waits behind attacker noise forever, so the call (microphone, possibly camera) is never torn down.” People expect that pressing the hang-up button ends the call and turns off the microphone and camera. But if the system is too busy processing other events, it may delay the command to end the call, leaving the audio or video active longer than expected. The report explains that someone taking part in a call could exploit this weakness by sending enough events to overload the system’s queue. This could stop the other person from ending the call properly, potentially keeping the microphone active and possibly the camera too. The underlying problem is not that Signal’s end-to-end encryption has been broken. Rather, it concerns the handling of call state and the reliable termination of a session. Encryption cannot protect users from every flaw in the software that controls when a communication session begins or ends. The vulnerability concerns RingRTC versions earlier than 2.72.0 and can affect Signal clients that embed a vulnerable build of the library. According to the CSIRT Toscana advisory, the affected application versions are Signal for Android before 8.27.0, Signal for iOS before 8.30.0, and Signal Desktop before 8.29.0. The official RingRTC fix and the comparison between versions 2.71.0 and 2.72.0 document the software changes. Users should install the latest available Signal release for their device rather than relying on the library version alone. The technical report also includes a conceptual Python proof of concept. It models the difference between a queue in which a flood can bury a hang-up event and one in which critical events receive priority. The demonstration is synthetic and offline, with no network activity or real calls, so it illustrates the underlying logic rather than providing a working exploit. This is an important distinction. The fact that the vulnerability has been made public does not mean attackers are already exploiting it. However, because it could expose private conversations, updating Signal promptly is a sensible precaution, especially for people who use it to discuss sensitive matters. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, mobile)
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."
State Bank of India has identified 3,897 customer records containing personal information in a set of about 375,000 records available on the dark web, and it does not yet know where the data came from. The state-owned bank, India's largest, said in a stock-exchange filing dated October 21 that it "has experienced an incident involving the loss of certain data." The count is
Google Cloud’s London outage disrupted Revolut payments and ATM withdrawals, while ING Germany reported online banking issues. Multiple Google Cloud services went offline in the London region this morning, according to Google’s status page, and the disruption didn’t stay contained to infrastructure dashboards. BREAKING: Revolut's app and cards have stopped working across Europe. Google Cloud's London region is affected by network failures. Revolut, and many other services, runs on Google Cloud. Revolut says money and data are safe but has not named a cause. pic.twitter.com/kyxOMlVaak — International Cyber Digest (@IntCyberDigest) October 20, 2026 British digital bank Revolut reported a widespread service outage at roughly the same time, and a person familiar with the matter told Reuters the two events were directly connected. “Multiple Google Cloud services experienced disruptions in the company’s London region on Tuesday, according to its service status page, while British digital bank Revolut also reported a service outage.” Reuters states, “A person familiar with the matter told Reuters that Revolut’s service outage was related to the Google Cloud service disruption.” The outage affected Google’s europe-west2 data center, its main facility in London. Google’s engineering team said they were investigating the problem. Alphabet did not immediately respond to Reuters’ request for more information. This is common during an ongoing incident, but it leaves customers unsure when their cards will work again. Revolut’s own status page showed most services as unavailable or partially unavailable, including card payments and ATM withdrawals. For a bank that exists entirely as an app with no physical branches to fall back on, that’s not a minor inconvenience. “We are currently experiencing a service disruption affecting multiple services. We can confirm that all our customers’ money and data are safe. We’ve been working to fix the issue since it was identified this morning and apologise to anyone affected and for any inconvenience.” the financial company wrote on X. We are currently experiencing a service disruption affecting multiple services. We can confirm that all our customers’ money and data are safe. We’ve been working to fix the issue since it was identified this morning and apologise to anyone affected and for any inconvenience… — Revolut (@Revolut) October 20, 2026 That last sentence is the right thing to say, and it’s also exactly what every bank says during an outage regardless of whether the underlying cause is confirmed. Money and data being safe is a different question from whether you can pay for your coffee. ING Germany also reported problems on Tuesday. Customers had trouble logging into online banking and the mobile app because of a technical issue. Reuters did not link these problems to the Google Cloud outage, and ING has not explained the cause. Two major financial services companies experiencing problems on the same morning could be a coincidence. However, it also raises questions about how much European banks depend on a small number of cloud providers. Google Cloud’s europe-west2 region in London supports many UK and European fintech services. Revolut alone serves tens of millions of customers across Europe. When a major data center goes down, the impact can quickly reach customers, preventing them from paying or accessing banking services before the cause is fully understood. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Revolut)
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