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.
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
A data breach exposed AfD emails, while an anonymous site published AI summaries of 421,000 messages, raising questions about authenticity and privacy. Germany’s Alternative für Deutschland (AfD) confirmed on October 19 that its hosting provider was hacked in September and that mailboxes were copied. By the time the confirmation reached Bild, a website called hexane.cc had already been online for hours offering an index of what it claims are 421,179 internal party emails. “In September 2026 there was a hacking attack on a hosting provider used by our party.” By then the website hexane.cc had been online for several hours with an index of what it says are 421,179 of the party’s emails. In place of the emails, the site shows summaries written by software, sorted into categories that include racism and antisemitism and graded from Low to Critical. International Cyber Digest (ICD), a cybersecurity news account on X, examined it on 19 October and reported that the index includes Gauland’s mailbox and that the newest message is dated 12 September 2026. According to the summaries ICD relayed, Gauland, the party’s 85-year-old honorary chairman and former co-leader, forwarded a 2018 email about a “Hitler pilgrimage” by a former judge on the party’s internal court in Thuringia.” AfD told told Bild. “A district board member sent a Christmas card with a swastika and the words “Heil Hitler”. Staff at the party’s Berlin headquarters discussed luring a photographer to an event and throwing him out by force, in a thread signed off “hail and victory”. The AfD’s statement gave a limited account of the incident. The party said the attackers copied parts of a few mailboxes, most of which had not been used for years, and did not access the leadership’s current accounts. However, the online index includes messages dated as recently as September 12, 2026, raising questions about the party’s account. The website hexane.cc does not publish the emails themselves. Instead, it provides AI-generated summaries grouped into categories such as racism and antisemitism, with severity ratings from Low to Critical. The site operators have not identified the AI software used to create them. When users try to open a record, the site displays the message “source text not published for this record.” So far, none of the summaries has been linked to an original email that can be verified. That’s an important detail that’s getting lost in the coverage. The summaries describe swastikas, Nazi greetings, Christmas cards bearing Heil Hitler, and a thread at the federal party headquarters signed off with “hail and victory.” If authenticated, some of this material would matter significantly in ongoing court proceedings about whether the party can be banned. But right now all of it is filtered through an anonymous operator’s software, and the original texts remain unpublished. Bild is the only German news outlet confirmed to have examined files from the archive directly, although it reviewed only a very small sample. The files appeared to be genuine party records, including identity documents, invoices, bank statements, membership applications, and regional member lists. They also included the identity card of Markus Frohnmaier, deputy leader of the AfD’s parliamentary group in the Bundestag. Berlin-based security researcher Lilith Wittmann, who exposed a flaw in the CDU’s campaign app in 2021, wrote on X that she believed the leak was genuine and the reported attack method was plausible. The hacker reportedly told the International Cyber Digest account on X that they gained access through “a single unpatched party website” and then reached the mail servers. The AfD has confirmed that its hosting provider was targeted, but the reported entry method has not been independently verified. The archive’s own file names offer another clue: one server is labeled “afd_exfil.” The term “exfil” is commonly used for a system that stages stolen data before attackers transfer it out of a network. The AfD is currently contesting in court its classification as a confirmed right-wing extremist organization by Germany’s domestic intelligence agency. Lawmakers in the Bundestag and Thuringia are separately debating whether to seek a formal party ban, which under German law can only come from the Federal Constitutional Court. The leak appeared on October 19, while the Cologne Administrative Court is actively weighing whether the intelligence agency may publicly apply the extremist designation, and two months before the AfD plans to seek the election of one of its leaders as state premier in Saxony-Anhalt. Within two hours of the first posts about the archive on X, some accounts were using it to argue that the AfD should be banned. If authenticated, the most legally relevant emails would be those showing how the party’s national headquarters operates and handles internal disciplinary cases. This is because a ban case focuses on the party as an organization, not on individual members. So far, none of the summaries has been linked to an original email. The website describes itself as an independent archive published in the public interest and claims to have minimized personal data. However, Bild found that the files it reviewed included membership applications and member lists containing full names and home addresses of people whose only apparent connection to the story was their party membership. By the afternoon of October 19, the site’s About, Method, and Support pages were returning server errors, leaving visitors unable to access its own explanations of how the archive was created and managed. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, AfD)
An attacker used a contributor's GitHub account to replace the download archives on five existing releases of OVHcloud's command-line tool on Monday, October 19. The account had been hijacked, and the new files were a build designed to steal secrets stored on the disk of any machine that runs it, the cloud provider says. They were live on GitHub for up to 94 minutes, from 12:47 to 14:21 UTC,
Nikkei Inc. disclosed a new data breach, the third in about a month, that exposed 1.83 million personal information records. The Japanese giant says subsets of the data contained personally identifiable information and other sensitive details, but most records contained no identifying info. The company said that 150,000 records contained at least one of the following data types: full names, email addresses, physical addresses, telephone numbers, and dates of birth. Email addresses appeared in 120,000 records. The remaining 1.68 million records contain no identifiers, the company said, and no credit card information was exposed. Nikkei Inc. is a major Japanese business news and financial information group, owner of Financial Times, the country’s largest business-focused media company. The company employs over 10,100 people and has an annual revenue of $2.5 billion (¥393.8 billion).
Staffing software company Bullhorn has disabled access to its platform to contain a cyberattack that caused a system-wide outage. The company says it has isolated the issue and eradicated the immediate threat. Currently, its team is working to restore services following thorough security reviews conducted with help from CrowdStrike specialists. Bullhorn is an American software company specializing in cloud-based staffing and recruitment solutions. Its platform helps staffing firms manage the recruitment lifecycle, including candidate tracking, onboarding, and payroll. The company says it serves more than 10,000 customers worldwide. According to a status page on the incident, customers started to experience issues with job posting in the Bullhorn application on Friday. The next day, the company said it was "experiencing a system-wide outage affecting the Bullhorn platform," and later confirmed that the issues were caused by a cyberattack.
The open-source AI coding project OpenCode has notified some users that stored API keys they used with external providers were exposed in a security breach. Multiple users reported that the incident led to unauthorized API charges and suspended projects in Google Cloud. According to the project maintainer, the breach affected 1.4% of OpenCode Console users, with some reporting more than $1,700 in unauthorized charges overnight. OpenCode is a model-agnostic AI coding agent that helps users understand code, write features, and fix bugs using natural-language instructions, through a terminal, desktop app, or code editor. Users can connect AI models from multiple AI providers to the OpenCode console to streamline and automate the development process. On Saturday, OpenCode announced that it suffered a large distillation attack that impacted only developers who linked external providers, including Anthropic, OpenAI, and Google.
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