Enterprise Account Executive

Azul appoints Kenny Johnston as Chief Product Officer. August 11, 2026 Azul has announced the appointment of Kenny Johnston as Chief Product Officer. The appointment comes following the retirement of Martin Van Ryswyk, after five years in the role. Johnston joins from Luciq, where he was Chief Product and Technology Officer. He has over two decades of experience in technology. His recent roles have seen him gain deep experience of building agentic, AI-powered enterprise DevOps and observability tools and platforms. At Azul, he will be responsible for leading the product strategy and execution of Azul's AI-first Java Platform. Moreover, the platform supports, secures, and maintains Java ecosystems that organisations have invested in. It consists of Azul Prime, Azul Core, Azul Intelligence Cloud, Azul Payara Micro and Azul Payara Server. Azul Payara Micro and Azul Payara Server were added to the Azul platform through Azul's December 2025 acquisition of Payara. Johnston will advance the roadmap to meet AI demands on Java estates. He will tackle cost, performance, security, modernization, and productivity head-on. AI pressures now drive every initiative in the enterprise Java landscape. Scott Sellers, Co-Founder and CEO, said, "Kenny's track record of scaling enterprise product organisations to build products that address business-critical use cases makes him exactly the right product leader as AI reshapes the performance, security and cost demands of every enterprise Java estate. "Under his leadership, we're focused on continuing to build out our AI-first Java platform and helping drive Azul into its next phase of growth." Who is Kenny Johnston. Johnston spent four years at Luciq, first as VP of Product and then as Chief Product and Technology Officer. He led global teams across Customer Success, Support, Product, Design and Engineering functions. He was responsible for delivering agentic observability built for enterprise mobile developers to help their apps meet the increasingly high bar that mobile users expect. Johnston helped the development function transform using Agentic SDLC with AI tools like Claude Code, FigmaMake, CodeRabbit & HumanLayer. Before Luciq, he was the Senior Director of Product Management at GitLab for nearly four years, leading a globally distributed Product Management function. He also held product leadership positions at both Rackspace and Hewlett-Packard. At Rackspace, he served as director of product management for Rackspace Private Cloud. More than tripling annual revenue and earning recognition as HPE's Global Service Provider Partner of the Year. At HP, he managed strategy development and feature prioritization for HP Helion - OpenStack Distribution. Johnston said, "Java is the backbone of the world's most critical enterprise systems, and AI is amplifying the pressure on all of it. The opportunity is to bring an AI-first, DevOps-centric vision to how Java is developed, deployed, secured and monitored in the enterprise. "I've spent my career helping world-class teams accelerate through transformative moments like this, and I'm excited to build on the strong foundation Azul has already established." Enterprise times: what does this mean. Johnston's arrival gives Azul a product leader with relevant experience at the point where Java, AI and enterprise modernisation are converging. Azul is arguing that Java will remain central to production AI, not just legacy systems. With Johnston's background in DevOps, observability and agentic AI, this should help connect that message to practical product execution. The key test will be whether Azul can turn its AI-first Java platform into measurable benefits around performance, security and cloud cost. If Johnston succeeds, Azul could strengthen its position as enterprises reassess Java estates for the AI era. Looking forward, Azul's product roadmap will focus on alleviating the pressures AI places on enterprise Java: absorbing AI workload demand and optimising cloud cost (Azul Prime), narrowing the AI-era security and patch-velocity gap (Azul Core), enabling AI-driven modernisation (Azul Intelligence Cloud, Azul Payara), and giving AI-modernised workloads the scalability, security and automation they need (all Azul products).

Java news roundup: Hardwood 1.0, Endive 1.0, Azul Payara, Quarkus, WildFly, LangChain4j, OSSI. * Michael Redlich Java Champion | Java Queue Lead Editor | Director at GSJUG Follow OmniFaces on. This week's Java roundup for June 22nd, 2026, features news highlighting: the GA releases of Hardwood 1.0 and Endive 1.0; the June 2026 edition of Azul Payara; point releases of Quarkus, LangChain4j; the first beta release of WildFly 41; and introducing Eliya JDK and the Open Source Sustainability Initiative (OSSI), the latter of which was founded by HeroDevs and Commonhaus Foundation. JDK 27. Build 28 of the JDK 27 early-access builds was made available this past week featuring updates from Build 27 that include fixes for various issues. Further details on this release may be found in the release notes. JDK 28. Build 4 of the JDK 28 early-access builds was also made available this past week featuring updates from Build 3 that include fixes for various issues. More details on this release may be found in the release notes. Azul Payara. The Azul Payara team has released the June 2026 edition of Azul Payara 7.1.0 that includes Community Edition 7.2026.6, Enterprise Edition 6.39.0 and Enterprise Edition 5.88.0. Along with bug fixes and component upgrades, all three editions deliver: a resolution to a Cross-Site Request Forgery (CSRF) and Server-Side Request Forgery (SSRF) issue in the Admin Console and REST management interface due to unnecessary passing the REST URL in a servlet request that can otherwise be obtained via session information; and support for overriding methods using the Spring Framework @Transactional annotation on Jakarta Data repository interfaces. Further details on these releases may be found in the release notes for Community Edition 7.2026.6, Enterprise Edition 6.39.0 and Enterprise Edition 5.88.0. Quarkus. The release of Quarkus 3.37.0 delivers bug fixes and notable changes such as: support for Hibernate ORM 7.4.0.Final, Hibernate Reactive 3.4.0.Final and Hibernate Search 8.4.0.Final; a new experimental extension, JLink, that generates a custom runtime image with jlink that includes only the necessary JDK modules in an application; and enabling reflection-free Jackson serializers by default. More details on this release may be found in the release notes. WildFly. The first beta release of WildFly 41 ships with bug fixes, dependency upgrades and new features such as: support for handling data in the WildFly Transactions Subsystem during a graceful shutdown; and the ability to enable authentication requests to be signed and optionally encrypted via the request and request_uri parameters within the WildFly Elytron Subsystem. Further details on this release may be found in the release notes. LangChain4j. The formal release (along with the twenty-seventh beta release) of LangChain4j 1.17.0 provides bug fixes, dependency upgrades and new features such as: a new DebatePlanner class to support the Debate agentic pattern; a new OracleChatMemoryStore class that adds an Oracle database for chat memory; and a new onUnmappedRawEvent streaming callback that allows for exposing raw streaming events that are not mapped by LangChain4j. More details on this release may be found in the release notes. Eliya JDK. Asymm Systems has introduced Eliya JDK, their downstream distribution of OpenJDK that tracks the quarterly OpenJDK 25 Critical Patch Updates (CPUs) and delivers builds for Linux x86_64 and Linux aarch64. This initial release, version 25.0.3, is based on the Oracle Critical Patch Update Advisory for April 2026. Further details on this release may be found in the release notes and this InfoQ news story. Hardwood. The release of Hardwood 1.0.0, a parser dedicated to the Apache Parquet file format, ships with notable changes such as: the close method, one for both the ColumnReader class and the RowReader interface, are now idempotent to resolve a performance regression; an instance of the AvroRowReader class now honors column projections such that the DECIMAL, UUID, UINT_32, and FIXED columns are correctly read; and the entry point for the Hardwood class now accepts an implementation of the HardwoodContext interface for control over decoder thread-pool sizing and the ability to share a context across readers. More details on this release may be found in the release notes and InfoQ will follow up with a more detailed news story. Endive. The release of Endive 1.0.0 delivers; WasmGC host integration with support for the externref value type as a Java Object such that host functions work naturally with GC types; and an optimization of tail call correctness by eliminating unnecessary stack frame allocation that benefits languages, such as CPython, that adopt tail calls in their interpreter loop. Edive, a new JVM-native WebAssembly (Wasm) runtime, has its roots from Chicory, created in September 2023 by Benjamin Eckel, chief technical officer at Dylibso, and Andrea Peruffo, principal software engineer at IBM. Open Source Sustainability Initiative. HeroDevs, the platform that identifies, remediates and prevents end-of-life open source risk, announced that they have joined the Commonhaus Foundation as the founding member of the Open Source Sustainability Initiative (OSSI) to "support sustainable open source ecosystems through improved lifecycle transparency and collaboration between maintainers, foundations, ecosystem partners, and the broader open source community." HeroDevs has already partnered with the Hibernate, Jackson, and Quarkus communities to support enterprises that need assistance in CVE remediation. HeroDevs also plans to partner with other projects under the auspices of the Commonhaus Foundation. Michael Redlich. Michael Redlich has been an active member within the Java community for the past 25 years. He founded the Garden State Java User Group (formerly the ACGNJ Java Users Group) in 2001 that remains in continuous operation. Since 2016, Mike has served as a Java community news editor for InfoQ where his contributions include monthly news items, technical writing and technical reviews. He has presented at venues such as Oracle Code One, Emerging Technologies for the Enterprise, Trenton Computer Festival (TCF), TCF IT Professional Conference, and numerous Java User Groups. Mike serves as a committer on the Jakarta NoSQL and Jakarta Data specifications and participates on the leadership council of the Jakarta EE Ambassadors. He was named a Java Champion in April 2023. With 33-1/2 years service, Mike recently retired from ExxonMobil Technology & Engineering in Clinton, New Jersey with experience in developing custom scientific laboratory and web applications. He also has experience as a Technical Support Engineer at Ai-Logix, Inc. (now AudioCodes) where he provided technical support and developed telephony applications for customers.

Azul ships JVM scanner to find unpatched Java before attackers do. Key takeaways. * Azul launches Mythos to discover and inventory JVMs across enterprise environments * 35+ billion JVMs run globally, many unpatched and invisible to security teams * AI-powered vulnerability scanning makes outdated Java runtimes easier targets Azul Systems is betting that most enterprises cannot answer a simple question: how many Java Virtual Machines are running in your environment right now? The company's new security product, Mythos, scans networks to find JVMs, catalog their versions, and flag those running without critical patches. The pitch is straightforward. If you cannot find your Java runtimes, neither can your security team. But attackers, increasingly armed with AI-assisted scanning tools, can. The timing matters. Log4Shell, the critical vulnerability disclosed in December 2021, affected 93% of enterprise cloud environments according to cloud security researchers. That crisis forced organizations to realize they had no inventory of where Java ran, which versions existed, or who owned them. Two years later, the visibility problem persists. Why JVM visibility remains a blind spot. Azul estimates 35 billion JVM instances run globally. That number sounds inflated until you consider how Java actually deploys. A single application server might spawn dozens of JVM processes. Containers multiply the count. Microservices architectures spin up instances dynamically. The JVM is everywhere, and everywhere invisible. Traditional asset management tools track servers and containers. They do not track what runs inside them. A security scanner might find an open port or a known CVE in a package manifest. It will not tell you that a JVM running your payments service is three minor versions behind and missing a critical deserialization fix. Scott Sellers, Azul's CEO, has been blunt about the gap: "Most organizations have no idea how many JVMs they're running, let alone which versions or patch levels. It's a massive blind spot." Mythos aims to close it by discovering JVMs across an environment, fingerprinting their exact versions, and correlating that data against known vulnerabilities. What Mythos actually does. The product works as an agent-based scanner. Deploy it across your infrastructure, and it inventories every JVM instance it finds. For each one, Mythos reports the vendor (Oracle, Azul, Amazon Corretto, Eclipse Adoptium, and others), the exact version string, and the patch level. It then maps those findings against Azul's vulnerability database. The output is a prioritized list. Which JVMs are critically vulnerable? Which are merely outdated? Which belong to production workloads versus development environments? That context matters for triage. Patching a staging server running Java 11.0.15 is different from patching the same version on a customer-facing application. Azul has an obvious commercial interest here. The company sells Zulu, its own JDK distribution, along with support contracts and the Azul Platform Prime runtime optimized for low-latency workloads. Mythos feeds that pipeline. Find the unpatched JVMs, then sell the customer a supported, automatically patched replacement. The AI angle is real, not hype. Azul's marketing leans into AI-powered threats, and the concern is legitimate. Large language models and AI agents can accelerate reconnaissance. Feed a model a list of IP addresses and ask it to identify which ones run vulnerable Java versions. Combine that with automated exploit generation, and the window between vulnerability disclosure and active exploitation shrinks. Recent demonstrations support this. Anthropic's Claude model found flaws in classified U.S. government systems during a red team exercise, completing in hours what human analysts might take days to discover. The same acceleration applies to mundane vulnerability scanning. Attackers do not need sophisticated AI to probe for outdated JVMs. But AI makes it faster, cheaper, and harder to outrun. Competitive landscape for Java security. Azul is not alone in this market. Snyk, Veracode, and Contrast Security all offer application security tools that cover Java. Container security vendors like Aqua Security and Sysdig scan images for vulnerable packages, including JDK components. Traditional vulnerability scanners from Tenable and Qualys can detect outdated Java installations. Mythos differentiates on specificity. General-purpose scanners treat the JVM as one component among thousands. Azul treats it as the primary runtime layer that deserves dedicated tooling. Whether that depth justifies a separate product depends on how heavily your stack relies on Java. For shops running 60% or more of their workloads on JVM platforms, which describes much of financial services, healthcare, and large enterprise IT, the focus may be worth it. What this means for engineering teams. The broader lesson is not about Azul specifically. It is about runtime visibility as a security requirement. Container registries track image versions. Package managers track dependencies. But the runtime itself, the JVM or Node.js or Python interpreter executing your code, often escapes inventory. Log4Shell taught that lesson painfully. Organizations spent weeks hunting for vulnerable Log4j instances because they did not know where Java ran. Two years later, how many have built systematic visibility into their runtime layer? For most, the answer is still not enough. Logicity's take. Azul's Mythos solves a real problem, but it is also a lead generation engine for Zulu subscriptions. Engineering leaders should evaluate it against their existing tooling. If you run Snyk or a container security platform, check whether those tools already report JVM versions with sufficient granularity. If not, dedicated JVM scanning fills a gap. Pricing is not public, but expect enterprise licensing tied to the number of hosts or JVMs scanned. For teams on Amazon Corretto or Eclipse Temurin looking to avoid vendor lock-in, consider that Mythos discovery works regardless of which JDK you deploy, so the vendor lock-in risk is lower than it might appear. Frequently asked questions. How many JVMs are running globally? Azul estimates more than 35 billion active JVM instances worldwide, counting individual processes across servers, containers, and cloud workloads. Why is JVM visibility a security problem? Traditional asset management tracks servers and containers but not the runtimes inside them. An outdated JVM with known vulnerabilities can run undetected for years. What does Azul Mythos do? Mythos scans enterprise environments to discover JVM instances, identify their exact versions and vendors, and flag those with known vulnerabilities or missing patches. How does AI increase the risk to unpatched JVMs? AI-powered tools can scan networks and identify vulnerable software faster than manual methods, shrinking the window between vulnerability disclosure and exploitation. Does Mythos only work with Azul's JDK? No. Mythos discovers and inventories JVMs from any vendor, including Oracle, Amazon Corretto, Eclipse Adoptium, and others. Need help implementing this? If your team needs guidance on JVM security, runtime visibility, or evaluating tools like Azul Mythos against your existing stack, reach out to Logicity's consulting partners for a security architecture review. Manaal Khan Tech & Innovation Writer Produced with AI assistance and reviewed by the Logicity editorial team. Learn more in its Editorial Policy.