Check Point® Software Technologies Ltd.(link is external) announced that its Quantum Firewall Software R82 — the latest version of Check Point’s core network security software delivering advanced threat prevention and scalable policy management — has received Common Criteria EAL4+ certification, further reinforcing its position as a trusted security foundation for critical infrastructure, government, and defense organizations worldwide.
Leading organizations around the world are adopting cloud native technologies to build next- generation products and achieve the agility that they need to stay ahead of their competition. Although cloud native and Kubernetes are very disruptive technologies, there is another technology that is probably the most disruptive technology of our generation — artificial intelligence (AI) and its subset, machine learning (ML).
We already see AI in digital assistants like Siri and Alexa, chatbots on websites and recommendation engines on retail sites. In the near future, AI will be embedded in almost all the products that surround us, from self-driving cars to next-generation medical devices.
Organizations that are building cloud-native applications today will need to evolve their capabilities to manage AI workloads because the next generation of cloud-native applications will have AI at their core. We call those "smart cloud-native" applications because they have AI built in.
Kubernetes a Perfect Match for AI
Kubernetes has become the enterprise cloud-native platform of choice and is a natural fit for running AI and ML workloads for a number of reasons:
■ Kubernetes can easily scale to meet the resource needs of AI/ML training and production workloads.
■ Kubernetes enables sharing of expensive and limited resources like graphics processing units between developers to speed up development and lower costs.
■ Kubernetes provides a layer of abstraction that enables data scientists to access the services they require without worrying about the details of the underlying infrastructure.
■ Kubernetes gives organizations the agility to deploy and manage AI/ML operations across public clouds, private clouds, on-premise, and secure air-gap locations, and to easily change and migrate deployments without incurring excess cost. A smart cloud-native business application consists of a number of components, including microservices, data services, and AI/ML pipelines. Kubernetes provides a single consistent platform on which to run all workloads, rather than in silos, which simplifies deployment and management and minimizes cost.
■ As an open-source cloud-native platform, Kubernetes enables organizations to apply cloud-native best practices and take advantage of continuous open-source innovation. Many of the modern AI/ML technologies are open source as well and come with native Kubernetes integration.
Smart Cloud-Native Challenges
Organizations that want to build smart cloud-native apps must also learn how to deploy those workloads in the cloud, in data centers, and at the edge. AI as a field is relatively young, so the best practices for putting AI applications into production are few and far between. The good news is that many of the best practices that exist around putting cloud native applications into production transfer easily to AI applications.
However, AI-driven smart cloud-native applications pose additional challenges for operators once in production because AI and ML pipelines are complex workloads made up of many components that run elastically and need to be updated frequently. This means that organizations need to start building operational capabilities around those AI workloads.
Cloud-native technologies have been around for about a decade, and enterprises are increasingly moving their most mission-critical workloads to cloud-native platforms like Kubernetes. This creates a slew of new challenges for organizations:
■ First, because those workloads are so mission-critical, it puts a much higher burden on operations teams to keep those workloads running 24/7 while making sure they are resilient, can scale, and are secure.
■ Second, those workloads tend to include more sophisticated technologies like data workloads, AI workloads, and machine learning workloads, which have their own operational challenges.
■ Third, modern cloud-native applications tend to run on a broad range of infrastructures, from a cloud provider or multiple cloud providers to data centers and edge deployments.
A Firm and Future-Proof Foundation
Organizations that want to adopt cloud-native technology must figure out how to address these challenges. To do this they need to change their workflows and culture to take full advantage of cloud native’s potential. They must learn how to build applications in a cloud-native way and to adopt the technologies that enable them to put those applications into production in a resilient and repeatable way.
The speed of innovation in the cloud-native ecosystem is unparalleled. Organizations that can keep pace with that innovation and learn how to adopt cloud-native and AI technologies will be able to build highly differentiated products that can put them ahead of their competition. They will be able to build their next-generation products much faster and in a more agile way, and they will be able to leverage AI to build smarter products.
Industry News
Postman announced full support for the Model Context Protocol (MCP), helping users build better AI Agents, faster.
Opsera announced new Advanced Security Dashboard capabilities available as an extension of Opsera's Unified Insights for GitHub Copilot.
Lineaje launched new capabilities including Lineaje agentic AI-powered self-healing agents that autonomously secure open-source software, source code and containers, Gold Open Source Packages and Gold Open Source Images that enable organizations to source trusted, pre-fixed open-source software, and a software crawling and analysis engine, SCA360, that discovers and contextualizes risks at all software development stages.
Check Point® Software Technologies Ltd.(link is external) launched its inaugural AI Security Report(link is external) at RSA Conference 2025.
Lenses.io announced the release of Lenses 6.0, enabling organizations to modernize applications and systems with real-time data as AI adoption accelerates.
Sonata Software has achieved Amazon Web Services (AWS) DevOps Competency status.
vFunction® announced significant platform advancements that reduce complexity across the architectural spectrum and target the growing disconnect between development speed and architectural integrity.
Sonatype® introduced major enhancements to Repository Firewall that expand proactive malware protection across the enterprise — from developer workstations to the network edge.
Aqua Security introduced Secure AI, full lifecycle security from code to cloud to prompt.
Salt Security announced the launch of the Salt Model Context Protocol (MCP) Server, giving enterprise teams a novel access point of interaction with their API infrastructure, leveraging natural language and artificial intelligence (AI).
The Cloud Native Computing Foundation® (CNCF®), which builds sustainable ecosystems for cloud native software, announced the graduation of in-toto, a software supply chain security framework developed at the NYU Tandon School of Engineering.
SnapLogic announced the launch of its next-generation API management (APIM) solution, helping organizations accelerate their journey to a composable and agentic enterprise.
Apiiro announced Software Graph Visualization, an interactive map that enables users to visualize their software architectures across all components, vulnerabilities, toxic combinations, blast radius, data exposure and material changes in real time.
Check Point® Software Technologies Ltd.(link is external) and Illumio, the breach containment company, announced a strategic partnership to help organizations strengthen security and advance their Zero Trust posture.