Latest news with #Kubernetes
Time Business News
5 days ago
- Business
- Time Business News
5 Signs Your Company Needs a Kubernetes Consultant Right Now
Kubernetes is the go-to solution for container orchestration. However, in the case of many businesses, the passage into making it big is not as rosy as it is written on paper. Having the right expertise can be the solution whether your company is in the middle of growing pains, security problems, or performance bottlenecks. In case your group cannot quite make the most out of Kubernetes, then it is perhaps time to have a specialist on board. These five indicators show you that your company is in need of a Kubernetes consultant: Although Kubernetes is easy to learn, it can be complicated especially when your infrastructure expands. Namespaces, pods, services, ingress controllers, network policies, more names. In case your team is too exhausted with configurations or spends more time dealing with clusters rather than work on features, then consultants can intervene and clean up your architecture. A knowledgeable Kubernetes consultant can perform the evaluation of your current environment, de-technical-debt it, and apply best-practices scaling, monitoring, and automation. This not only simplifies, but also gives a concrete base of future development. Is your application unexpectedly crashing, responding slowly or is it going down during deployment? Kubernetes is only prepared to manage high availability and zero-downtime rollouts as long as it is correctly configured. A consultant can diagnose the underlying issue in your performance problems and optimize your cluster to have a better use of resources, autoscaling, and resiliency. A combination of adequate tuning lets your team perform less firefighting and more building. Most organizations start using Kubernetes clusters without a well-paved security plan. Your infrastructure may be under attack due to unattended RBAC roles, vulnerable services and depleted components. Kubernetes security is also multi-layered and subtle, and an expert can help you find the weak spots in your configuration. They will aid in the enforcement of role-based access control (RBAC), network policy, secrets management, and audit logs essential in compliance and in peace of mind. This is where consulting for your Kubernetes strategy becomes not just helpful, but essential. You'll gain expert guidance on building secure, compliant, and reliable systems from the ground up. Modern development requires Continuous Integration and Continuous Deployment (CI/CD), however, integration of such tools with Kubernetes is not an easy process. When your team continues to deploy in a manual way and pipelines are breaking all the time, then obviously, it is a red flag. The expense of clouds may inflate tremendously on the off chance that your Kubernetes group is not enhanced. Autoscaling or underuse of services, nodes that are over-provisioned or lying unused can also devour your budget without you noticing. A consultant may evaluate how you use it, trim your clusters and install autoscaling and cost control processes. The result? Improved result to reduced cost. Kubernetes is an effective platform, and any platform is not always easy to explore on your own. Whether you are just making your first steps in Kubernetes, or already run production loads, hiring a consultant can speed your development, and help you not to make expensive mistakes. TIME BUSINESS NEWS
Techday NZ
5 days ago
- Business
- Techday NZ
DuploCloud AI Suite launches on AWS Marketplace to boost DevOps
DuploCloud has announced the availability of its AI Suite through the new AI Agents and Tools category in the AWS Marketplace. The launch enables AWS customers to discover, purchase, and deploy DuploCloud's Agentic Help Desk for DevOps, providing tools designed to accelerate the development and deployment of AI agents and workflow automation within AWS environments. Marketplace expansion The AI Agents and Tools category in AWS Marketplace functions as a unified catalogue for a variety of artificial intelligence solutions from AWS Partners. DuploCloud's inclusion in this catalogue means customers can streamline their procurement events, accessing AI solutions via their existing AWS accounts and infrastructure. According to DuploCloud, the AI Suite is designed to help organisations minimise manual DevOps tasks and automate cloud operations. The suite includes built-in security protocols to simplify DevOps management and scale cloud-based initiatives, offering customers the ability to focus resources on core software development rather than operational overheads. Agentic Help Desk and AI Studio AI Suite introduces several key components for customers. At the centre is the AI Help Desk, which leverages large language models (LLMs) alongside live infrastructure data. This tool converts user requests into executable actions, facilitating human approval and teamwork as part of agentic DevOps workflows. The suite also features an AI Studio that supports the creation and deployment of agents within containerised Kubernetes environments. These tools together allow users to automate even highly complex workflows based on written prompts. The platform executes these tasks securely, not merely suggesting actions, but taking steps towards their completion whilst retaining oversight and approval workflows for teams managing critical infrastructure. Streamlining procurement Participation in the AWS Marketplace's new AI category enables organisations to accelerate their procurement and deployment of AI technologies. By leveraging their AWS accounts, customers are able to maintain central oversight of software licenses, billing, and user permissions, reducing delays previously caused by extended vendor negotiations and multi-party evaluations for technology adoption. Venkat Thiruvengadam, Chief Executive Officer at DuploCloud, highlighted the company's aims for the partnership, stating: "We're excited to offer AI Suite in the new AWS Marketplace AI Agents and Tools Category. AWS Marketplace allows us to provide customers with a streamlined way to access our Agentic Help Desk for DevOps, helping them elevate DevOps from writing scripts to building agentic, cross-system workflows." Focus on automation and security The company describes its AI Suite as capable of supporting both human oversight and end-to-end automation, integrating compliance and security controls throughout its operations. It aims to empower teams to operate cloud infrastructure efficiently without the necessity of traditional Infrastructure-as-Code expertise. As the AI marketplace grows, automation and collaboration features are positioned as essential for enterprises seeking rapid development and compliance within cloud environments. Customers in the AWS ecosystem now have access to DuploCloud's tools without navigating new procurement processes or vendor relationships. By centralising AI DevOps workflow capabilities in the AWS Marketplace, organisations can adopt self-service models underpinned by automation, while maintaining existing security and governance standards. DuploCloud positions its offering for both startup and enterprise customers looking to launch products faster and scale DevOps practices without significantly increasing personnel or operational complexity. Follow us on: Share on:
Associated Press
7 days ago
- Business
- Associated Press
DuploCloud Announces Availability of AI Suite in the New AWS Marketplace AI Agents and Tools Category
PRESS RELEASE: Paid Content from ACCESS Newswire. The AP news staff was not involved in its creation. Published [hour]:[minute] [AMPM] [timezone], [monthFull] [day], [year] SAN JOSE, CA / ACCESS Newswire / July 16, 2025 / DuploCloud , a leading provider of AI-powered DevOps and automation, today announced the availability of AI Suite in the new AI Agents and Tools Category in AWS Marketplace. This enables AWS customers to easily discover, buy, and deploy DuploCloud's Agentic Help Desk for DevOps using their AWS accounts, accelerating AI agent and agentic workflow development. The AI Agents and Tools Category in AWS Marketplace serves as a centralized catalog for hundreds of AI solutions from trusted AWS Partners. AI Suite helps organizations eliminate manual DevOps work, accelerate cloud operations, and enable self-service with built-in security guardrails, helping customers simplify and scale their DevOps so they can focus on building. 'We're excited to offer AI Suite in the new AWS Marketplace AI Agents and Tools Category,' said Venkat Thiruvengadam, CEO at DuploCloud. 'AWS Marketplace allows us to provide customers with a streamlined way to access our Agentic Help Desk for DevOps, helping them elevate DevOps from writing scripts to building agentic, cross-system workflows.' AI Suite delivers essential capabilities including an AI Help Desk, which turns user requests into actions by connecting to LLMs, using live infrastructure data, and supporting human approval and teamwork for agentic DevOps workflows. It also has an AI Studio for building and deploying agents as containers in Kubernetes. These features enable customers to automate complex DevOps workflows from simple prompts-not just recommending actions, and securely executing them. Through the AWS Marketplace AI Agents and Tools Category, customers can significantly accelerate their procurement process to drive AI innovation, reducing the time needed for vendor evaluations and complex negotiations. With centralized purchasing using AWS accounts, customers maintain visibility and control over licensing, payments, and access through AWS. To learn more about DuploCloud's AI Suite in AWS Marketplace, visit . To learn more about the new AI Agents and Tools Category in AWS Marketplace, visit . About DuploCloud: DuploCloud is the industry leader in AI for DevOps, empowering teams to build, configure, secure, and operate cloud infrastructure through intelligent agents and end-to-end automation. DuploCloud enables rapid application deployment with 24x7 compliance and security baked in-no Infrastructure-as-Code required. Startups and enterprises use DuploCloud to launch products quicker and scale without growing their DevOps team. Contact InformationKate Pietrelli PR 760-518-2633 SOURCE: DuploCloud, Inc. press release
Forbes
7 days ago
- Forbes
Container Security Testing: QA Strategies For Kubernetes And Beyond
Margarita Simonova is the founder of It should come as no surprise that container orchestration platforms like Kubernetes now dominate production environments. Their ability to offer scalability, high availability, standardization, efficiency and automation makes them essential. But with this popularity comes the responsibility of quality assurance (QA) teams to enforce robust security solutions to safeguard these platforms. Securing container platforms involves such activities as scanning images, validating configurations and simulating attacks. In this article, we will take a close look at these practices so we can ensure that our container security is rock solid. Image Scanning The first activity that QA teams can perform is scanning container images for known vulnerabilities. Containers can include a number of weaknesses such as hardcoded credentials, outdated software libraries and packages with known vulnerabilities. Scanning can help weed out these issues before and after the deployment of containers. Image scanning works by first unpacking the images by layers. Then it can inspect files, OS packages and libraries to make matches against the common vulnerabilities and exposures (CVE) database. If vulnerabilities are found, containers can be flagged as not meeting security thresholds. Some popular image-scanning tools include Trivy, Clair and Anchore. With these tools in your CI/CD pipeline, you can automatically detect vulnerabilities. It's important to start scanning as early as possible in the CI/CD process. Integrating security early on in the process is referred to as 'shifting left' and involves starting to scan from base images and any included dependencies. It's also critical to keep scanning even after deployment because new vulnerabilities are always being added to the CVE. With a regular image-scanning schedule and a tool that is continually updated, your container security testing habits will be off to a great start. Validating Configurations In addition to scanning container images for known vulnerabilities, it is also crucial to check for proper configurations. Improperly configured containers can open an organization up to a wide range of issues. First of all, misconfigurations can lead to security risks, which may result in data leaks. Misconfiguration can also lead to stability issues, such as resource exhaustion that slows or halts a system. One more issue is noncompliance, which can result in failing to comply with standards such as NIST benchmarks. There are several common areas to check for misconfigurations in a containerized environment. First are container-level configurations, such as the use of privileged mode, missing definitions for resource limits, read-only root file systems and hardcoded secrets in ENV variables. At the image level, some configurations to check include whether trusted base images are signed, whether the OS layer is minimal and whether there are no multistage builds or latest tags. Orchestration-level configurations that need to be checked include network policies, namespace isolation and whether audit logging is enabled. One more issue relates to configuring security controls, including whether containers are running as root, whether SSL has been enabled and whether secrets are being properly managed. A good way to address configuration errors is to adopt a declarative policy-as-code (PaC) framework. This means turning policies into a machine-readable format that can be applied during development, deployment and runtime. PaC can enforce consistency, transparency and the auditability of policies. Two popular PaC tools are Open Policy Agent and Kyverno. These tools help enforce best practices, such as using non-root containers, following the principle of least privilege, checking for network segmentation and failing builds that violate policies. Simulating Attacks Penetration testing (pentesting) for container orchestration goes beyond just scanning for vulnerabilities; it actually simulates an attack by exploiting found vulnerabilities. This type of invasive test is useful because it uncovers critical weaknesses that may have slipped through an organization's other controls. It also lets a container administrator see how their entire ecosystem is viewed externally from the viewpoint of an attacker. That includes any reconnaissance an attacker would perform through open-source intelligence or direct social engineering simulations on employees. When pentesting container orchestration, some Kubernetes-specific issues need to be looked out for. To start, misconfigured role-based access control (RBAC) and authentication can be a big issue. Pentesting can try to enumerate service accounts and roles, check for over-privileged cluster-admin access, exploit the impersonate verb on roles and attempt token theft. Another issue is API server exposure. To address this, pentesting can check for open authentication endpoints or try to bypass namespace boundaries. One more big threat is insecure etcd access because the entire Kubernetes cluster state is stored there, including secrets. Pentesting can try such actions as getting into etcd without authentication, attempting to extract secrets and trying to perform write operations. A pentest will further attempt to gain persistent access to a network once it has found a vulnerability to exploit. When performed by a professional third-party organization, a comprehensive report will be given at the end that offers advice on patching the container orchestration and often includes follow-up tests that can be performed once recommended actions have been taken. Why It's Timely With 90% of organizations running containerized workloads by 2025 and supply-chain attacks on the rise, QA teams need specialized container security testing to prevent breaches and ensure compliance. The threat landscape is only growing larger as attackers use more sophisticated tools and artificial intelligence (AI) to become even more dangerous. Organizations will need to step up their game to always stay one move ahead of threat actors. This will involve thorough testing and using even more sophisticated tools than attackers have access to. This will be especially important for organizations that operate in industries with strict regulatory and compliance pressures. Conclusion By paying attention to the aspects of container security covered here (image scanning, validating configurations, simulating attacks), QA teams can be assured that they are following best practices to keep container orchestration as secure as possible. So, let's take a proactive approach to security and follow these guidelines to keep our container orchestration operating smoothly and without interference from threats. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
India.com
7 days ago
- Business
- India.com
Reimagining Transaction Intelligence: How Chandrashekar Althati's Innovations Are Shaping Modern Data Platforms
Today's world is driven by digital transactions, organizations are under constant pressure to manage risk, understand customer behavior, and ensure compliance. Earlier, rule-based transaction monitoring systems fell short when faced with evolving patterns of fraud and increasingly complex regulatory demands. The need for intelligent, scalable, and real-time classification systems has never been greater. This is where Chandrashekar Althati's contributions to data architecture and machine learning step in. One such notable innovation is the U.S. patent US8965820B2, titled 'Multivariate Transaction Classification,' co-invented by Chandrashekar Althati during his tenure at SAP. This patent introduced a methodology to classify transactions using a multivariate approach powered by machine learning and an innovation that has since started the groundwork for scalable, real-time fraud detection, customer segmentation, and compliance monitoring. From Concept to Implementation: What Is This Patent? The core idea of the Multivariate Transaction Classification system is to analyze transactional data using multiple variables rather than relying on static rule sets. This helps businesses to adapt changing transaction behaviors, a critical feature in industries where fraudsters and compliance requirements keep changing. ' At SAP, our goal was to solve a problem that rule-based systems couldn't—how do we build a system that not only detects known fraud patterns but can also evolve with new ones?' said Chandrashekar Althati. 'Our approach used machine learning models to evaluate risk across dimensions like transaction value, time, location, and behavior anomalies.' The patent describes innovations like thresholding mechanisms, automated data processing pipelines, and adaptive learning models that change themselves over time. For SAP's enterprise customers, this resulted in not only improved fraud prevention but also enhanced decision-making and real-time business insights. Beyond SAP, the impact of this technology has repeated across sectors like financial services, e-commerce, healthcare, and even regulatory agencies now rely on similar approaches for intelligent transaction management. Deep Expertise Across Domains Chandrashekar Althati's trajectory since then has only reinforced his reputation as a leader in data architecture and applied AI. Currently serving as a Data Platform Architect at a leading health tech provider, he has been instrumental in designing real-time data processing solutions that support personalized patient care at scale. At a major healthcare analytics company (anonymized), Chandrashekar led the design of a fault-tolerant, cost-optimized Kubernetes infrastructure on AWS, powering hundreds of dynamic ETL workflows through Apache Airflow and container orchestration. His work with data scientists and engineers helps ML pipelines using tools like AWS SageMaker, Kubeflow, and Apache Spark, and enhance operational agility. ' We needed to streamline data movement while keeping the computer cost in check. Building production-ready, auto-scaled systems on Kubernetes helped us ingest and process data more efficiently and securely,' Chandrashekar shared. His leadership here involved not only architectural design but also hands-on implementation, security management, and cost optimization shows his end-to-end mastery of enterprise data systems. Earlier, during his time at Deloitte Consulting, Chandrashekar focused on AI-driven analytics and serverless architectures. He designed machine learning solutions to extract structured insights from unstructured web data and led projects that integrated AWS services through Lambda, Step Functions, and Redshift. His ability to integrate DevOps and MLOps practices made him a valuable contributor in cross-functional, globally distributed teams. Scholarly Contributions and Global Recognition A strong advocate for knowledge sharing, Chandrashekar Althati is also a published researcher whose work in AI, predictive analytics, and data platform optimization has been cited widely. His Google Scholar profile reflects over 100 citations and an h-index of 7 clear indicators of academic influence. In particular, his recent article 'Enhancing Data Integration and Management: The Role of AI and Machine Learning in Modern Data Platforms' (JAIGS, 2024) has received notable attention in the data science community. Collaborating with peers across North America and India, he has published on themes ranging from AI-driven analytics in annuity products to optimization techniques for storage-constrained systems. ' My research interests are often inspired by the challenges I see in real-world enterprise data systems. Publishing these findings allows us to raise the bar for the industry while encouraging interdisciplinary collaboration,' Chandrashekar noted. His scholarly presence is matched by active participation in academic and industry forums. Chandrashekar frequently contributes as a co-author with other noted professionals like Bhavani Krothapalli, Jegatheeswari Perumalsamy, and Manish Tomar. The Road Ahead: Scalable, Responsible AI Chandrashekar believes that future transaction analytics systems will integrate deeper layers of intelligence. ' We're moving toward architectures where deep learning, edge computing, and blockchain all converge. The goal is to provide decision-support systems that are transparent, compliant, and fast,' he explained. There's also a strong push toward responsible AI, something that aligns with his current work in regulated sectors like healthcare. His emphasis on governance, reproducibility, and real-time monitoring of AI pipelines reflects a mature and forward-looking understanding of enterprise needs. Chandrashekar is also exploring opportunities to extend his patented work into domains like decentralized finance and real-time patient health monitoring, where transaction-like events must be classified with high accuracy and low latency. Closing the Loop: From SAP Labs to Industry Standard The journey of the Multivariate Transaction Classification patent is a story of practical innovation. What began as a forward-looking idea at SAP Labs has evolved into an industry standard, echoed in modern data platforms around the world. Chandrashekar Althati's career—spanning healthcare, consulting, and enterprise software—illustrates how deep technical expertise, combined with a collaborative and inquisitive mindset, can drive lasting impact. Whether enabling scalable cloud-native systems or publishing influential research, Chandrashekar continues to influence how we build and trust intelligent systems. His contributions demonstrate that impactful technology starts with thoughtful design and a vision that bridges research with enterprise scale.
