Since a full 10-page document is quite extensive, I have structured a comprehensive blueprint and the foundational content for your tutorial. You can use this as the “Source of Truth” for your document.

The Integration of DevOps and Cybersecurity: Maximizing Risk Management

1. Introduction: The Evolution of Risk

In the legacy model, security was a “Point in Time” check. In a DevSecOps world, risk is managed through Continuous Assurance. The goal is to shift security “Left” (into development) and “Right” (into production monitoring).

2. The Core Pillars of DevSecOps

To maximize risk management, we focus on four key areas:

• Visibility: You cannot secure what you cannot see.
• Automation: Manual security checks are the enemy of velocity.
• Immutable Infrastructure: Reducing configuration drift.
• Shared Responsibility: Culture shift where “security is everyone’s job.”

3. The Process: A Risk-Based Lifecycle

A robust DevSecOps process integrates specific security checkpoints into the standard CI/CD pipeline:

4. The Essential Toolstack

As a veteran, you know tools don’t solve problems—processes do. However, these are the industry standards for 2026:

A. Static Analysis (SAST)

• Tools: SonarQube, Snyk, Checkmarx.
• Focus: Scanning source code for vulnerabilities without executing it.

B. Software Composition Analysis (SCA)

• Tools: GitHub Advanced Security, Black Duck.
• Focus: Managing Open Source Software (OSS) risk and License compliance.

C. Infrastructure as Code (IaC) Security

• Tools: Checkov, Terrascan, tfsec.
• Focus: Ensuring Terraform/CloudFormation scripts follow the “Least Privilege” principle.

D. Container & Cloud Security

• Tools: Aqua Security, Prisma Cloud, Trivy.
• Focus: Image scanning and Kubernetes admission controllers.

5. Step-by-Step Implementation Guide

Step 1: Governance and Threat Modeling

Before writing code, identify the “Crown Jewels.” Use the STRIDE model to evaluate threats to your architecture.

Note: $Risk = Threat \times Vulnerability \times Impact$. Use this formula to prioritize your backlog.

Step 2: Securing the CI/CD Pipeline

Harden your runners. Ensure that your Jenkins, GitLab Runner, or GitHub Actions use ephemeral environments and encrypted secrets.

Step 3: Integrating Automated Gates

Set “Break the Build” policies. If a high-severity vulnerability is detected during the SCA or SAST phase, the pipeline must stop. This prevents technical debt from reaching production.

Step 4: Continuous Monitoring and Feedback Loop

Integrate your production logs into a SIEM (like Splunk or ELK). Use the data to feed back into the Plan phase for the next sprint.

6. Advanced Practices for Risk Maximization

• Policy as Code (PaC): Use Open Policy Agent (OPA) to enforce compliance automatically.
• Chaos Security Engineering: Purposefully injecting security failures to test resilience.
• Zero Trust Architecture: Never trust, always verify, regardless of whether the request comes from inside or outside the network.

7. Conclusion: The Roadmap to Maturity

Risk management is not about achieving zero risk—it’s about informed acceptance of risk. By automating the mundane, your security team can focus on complex architectural threats.

Next Steps for your 10-page Doc

1. Why Communication Matters in DevOps

DevOps breaks down silos between development, operations, QA, and business stakeholders. Effective communication ensures:

• Rapid incident response (clear alerts and escalation).
• Collaboration on code and infrastructure (shared visibility).
• Continuous feedback loops (faster iteration).
• Cultural alignment (transparency and shared responsibility).

Without the right tools, even the best CI/CD pipelines will fail because teams won’t be aligned.

2. Key Communication Needs in DevOps

When choosing tools, evaluate them against these communication categories:

1. Real-Time Chat & Collaboration – For quick discussions, decisions, and alerts.
2. Asynchronous Communication – For documentation, knowledge sharing, and time zone differences.
3. Incident & Alert Management – For critical events and on-call coordination.
4. Code-Centric Collaboration – For PR discussions, reviews, and traceability.
5. Visual Collaboration & Planning – For workflows, retrospectives, and design discussions.
6. Cross-Platform Integration – For connecting monitoring, CI/CD, and ticketing systems into communication hubs.

3. Evaluating Tools by Category

A. Real-Time Chat & Collaboration

• Slack: Widely used; integrates with Jenkins, GitHub, Datadog, PagerDuty.
• Microsoft Teams: Strong enterprise adoption, good if using Azure DevOps.
• Mattermost / Rocket.Chat: Open-source, self-hosted alternatives for compliance-driven orgs.

👉 Best for: day-to-day DevOps chat, pipelines notifications, CI/CD status updates.

B. Asynchronous Communication

• Confluence / Notion: For documenting runbooks, architecture, retrospectives.
• GitHub Discussions / GitLab Wiki: For community-driven discussions within code platforms.
• Google Workspace / Office 365: For documentation and decision records.

👉 Best for: knowledge retention, cross-timezone collaboration, compliance audits.

C. Incident & Alert Management

• PagerDuty: Incident response orchestration, escalation policies, on-call rotation.
• Opsgenie: Atlassian’s incident tool, integrates tightly with Jira.
• VictorOps (Splunk On-Call): Strong in post-incident reporting.
• ServiceNow: For enterprises with ITSM/ITIL processes.

👉 Best for: structured incident management and automated escalation.

D. Code-Centric Collaboration

• GitHub / GitLab / Bitbucket: PR/MR review workflows, inline comments, integrated CI/CD notifications.
• Gerrit: For enterprises needing advanced code review workflows.

👉 Best for: dev-to-ops collaboration on infrastructure-as-code and feature reviews.

E. Visual Collaboration & Planning

• Jira / Azure Boards: Popular for backlog management and release planning.
• Trello / ClickUp: Lightweight planning and task-tracking.
• Miro / Lucidchart: Whiteboarding for architecture and incident diagrams.

👉 Best for: sprint planning, retrospectives, workflow visualization.

F. Cross-Platform Integration Hubs

• Slack / Teams Bots: Integrate build failures, alerts, deployments.
• Zapier / n8n / GitHub Actions: Automate communication triggers across systems.
• Statuspage / Status.io: Communicate outages externally to customers.

👉 Best for: unifying communication across DevOps ecosystems.

4. Selection Criteria for the Right Tools

When deciding, weigh tools against:

• Integration capability – Does it connect to your CI/CD, monitoring, version control?
• Security & Compliance – Self-hosted vs. SaaS, encryption, audit logs.
• Scalability – Handles large, distributed teams without slowing workflows.
• Cost – SaaS pricing vs. self-hosted infrastructure overhead.
• Adoption & Familiarity – What your team is already comfortable with.

5. Example Tool Stack for a Mature DevOps Workflow

• Slack: Real-time collaboration + integrated CI/CD alerts.
• Confluence + GitHub Wiki: Knowledge base and documentation.
• PagerDuty: Incident management and on-call scheduling.
• GitHub + GitHub Actions: Code collaboration and pipeline feedback.
• Jira: Agile planning and reporting.
• Miro: Visual brainstorming for retrospectives and system design.

This creates a single loop of communication: Code → Build → Deploy → Monitor → Alert → Learn → Improve.

6. Best Practices for DevOps Communication

• Automate status updates (don’t manually copy CI/CD logs into chat).
• Define clear on-call escalation rules.
• Use chatops (run commands directly from chat).
• Maintain single source of truth (avoid scattering documentation).
• Record postmortems publicly inside the team’s knowledge base.
• Foster a blameless culture in communications.

✅ Bottom line:
The right communication tools in DevOps are those that integrate seamlessly into your toolchain, provide visibility, reduce noise, and support collaboration across teams and time zones. A balanced mix of chat, async knowledge tools, incident management, and planning boards ensures that DevOps communication is transparent, automated, and effective.

As the “telco-cloud” world moves rapidly toward cloud-native edge-to-edge orchestrated solutions, ecosystems are hard at work collaborating on conceptualizing, designing, developing, testing, and validating disruptive new ways to bring enterprises, businesses, and consumers a truly low-latency real-time, on-demand, web-scale, automated and intelligent extension and eventually replacement of dated legacy mobile networking.

Industry initiatives like the Linux Foundation’s LF Edge project, O-RAN for Open Ran, multiple IEEE projects, Industrial Internet Consortium testbeds, and edge orchestration solutions like the one ZEDEDA rolled out last year feature agile, automatic, and intelligent operations, monitoring, and management.

We are watching history being made, with comprehensive cloud adaptation of networks, operating systems, and micro-services is a prerequisite for this much anticipated digital transformation that will be a big part of the theme at next week’s Mobile World Congress in Barcelona.

Today, two industry disrupters, Mavenir and MobiledgeX, announced their successful integration enabling Deutsche Telekom to roll out simplified and harmonized Global Edge Connectivity.

Their innovations in the lab, supporting the cloud-native dynamic deployment of a 5G core driven by emerging application needs including 4G/5G Private Networks, have “demonstrated and validated the reference design for deploying cloud-native 5G User Plane Function (UPF) to support Multi-Access Edge Computing (MEC) applications, tailored to each use case need for proximity and performance,” according to their announcement.

The reference design is fully cloud-native, abstracted away from underlying cloud architecture, infrastructure vendor, and operating owner.

“We are excited to partner with Mavenir and MobiledgeX to demonstrate dynamic cloud-native deployment of 5G Core UPF for users where it is needed,” said Alex Choi, SVP Strategy & Technology Innovation Deutsche Telekom. “The UPF can run inside the operator’s owned network, inside a private 4G/5G network, inside the roaming network, or in the public cloud. The ability to dynamically and seamlessly orchestrate traffic across our own footprint and the footprint of others opens up the possibility for new connectivity solutions for customers.”

“This is a successful milestone for cloud-native 5G Core traffic routing, giving Deutsche Telekom the freedom to deliver a point of interconnect/local breakouts anywhere in the world, on any cloud,” says Bejoy Pankajakshan, Chief Strategy Officer, Mavenir. “Cloud-native design of 5G network functions integrated with orchestration tools makes it possible to achieve a new level of flexibility in network function placement. Automation, agility, and flexibility are at the heart of this project.”

“The MobiledgeX Edge-Cloud platform enables our operator customers to dynamically manage and allocate UPF functionality across their own cloud infrastructure independent of vendor, in roaming partners mobile networks, in private 4G/5G networks, and in public clouds, all through one pane of glass and in one trusted and secure operation,” says Sunay Tripathi, Chief Technology Officer & EVP of Engineering & Product, MobiledgeX. “Cloud-native UPF operation transforms traffic patterns in mobile operator networks, making the telecom operator the largest customer of their own edge cloud offering, which is necessary to be a true cloud leader. We are very excited that Deutsche Telekom has taken the initiative to lead.”

A service-based, cloud-native UPF is at the core of the reference design. In addition, MobiledgeX has emphasized the importance of mobile operators running critical 5G functions on their own edge networks. Validating UPF in this environment represents a step toward mirroring the successful application deployment strategies of the major global cloud companies.

The User Plane Function (UPF) is one of the network functions (NFs) of the 5G core network (5GC). The UPF is responsible for packet routing and forwarding, packet inspection, QoS handling, and external PDU session for interconnecting Data Network (DN) in the 5G architecture.

Since the introduction of mobile packet data in 3G, all data traffic, irrespective of source, destination, and requesting application, has been tunneled back to a limited number of fixed (packet gateway) anchor points inside the mobile networks, the companies explained.

This is increasingly inefficient, slow, and prone to bottlenecks.

5G Core standards enable dynamic placement of the anchor point (the UPF) based on the application and network’s needs.

Mavenir officials said the combination of Mavenir’s widely deployed 4G/5G cloud-native Converged Packet Core solution, coupled with the MobiledgeX Edge-Cloud platform, enables the UPF to be placed on different clouds from different vendors with different ownership.

This leads to greater control by CSPs, as security and privacy are maintained by the operator at all times, even when roaming.

This architecture helps optimal placement of the application’s backend servers, enabling a superior user experience and increased network efficiency by significantly reducing the distance the data needs to travel before it is processed.

MobiledgeX’s work with Mavenir follows the company’s announcement last week that it has updated its Edge-Cloud platform to version 3.0. The latest evolution provides a common interface to manage public and private edge cloud rollouts, laying the groundwork for telecom to diversify hyperscaler engagement – and take better control over where and how applications are deployed.

Further, according to the announcement:

“For this trial validation, the Mavenir 5G Core is running in Deutsche Telekom’s network. Two network and vendor-independent cloudlets located in Germany have been deployed as valid UPF placements in the 5G Core routing. For instance, a video application is started on a 5G phone connected to Deutsche Telekom’s 5G network in Berlin. The request is sent to the 5G Core that selects UPF placement in the Berlin cloudlet. Tunneling is established. The video request is passed to the local edge-placed video cache, and delivery commences. The identical request is made in Hanover. The traffic is then established to the Hanover cloudlet, and local fulfillment occurs there. The MobiledgeX Edge Cloud 3.0 platform dynamically controls the secure placement, lifecycle management, and video traffic routing of the edge-based UPF and the video application.”

In the 5G era, a single network infrastructure can meet diversified service requirements. A Cloud-Native E2E network architecture has the following attributes, according to a post by Cisco:

Provides logically independent network slicing on a single network infrastructure to meet diversified service requirements and provides DC-based cloud architecture to support various application scenarios.
Uses CloudRAN to reconstruct radio access networks (RAN) to provide massive connections of multiple standards and implement the on-demand deployment of RAN functions required by 5G.
Simplifies core network architecture to implement on-demand configuration of network functions through control and user plane separation, component-based functions, and unified database management.
Implements automatic network slicing service generation, maintenance, and termination for various services to reduce operating expenses through agile network O&M.
MobiledgeX is working with global mobile operators to deliver full control over multi-cloud edge deployments.

The company’s MobiledgeX Edge-Cloud platform provides a common interface for managing application workloads anywhere, abstracted from the underlying infrastructure, to maintain control over data, privacy, and security.

It also provides device native SDK and matching engines for the operator and third-party application developers to maximize performance and efficiency for cloud-native applications at the edge. MobiledgeX Edge-Cloud is proven in the market today via trials, proofs of concepts, and deployments that involve more than 20 global operators and a range of next-gen app developers.

MobiledgeX is an edge computing company founded by Deutsche Telekom AG and headquartered in San Jose, California.

Growth Forecast Report on “ Continuous Integration Tools Market size | Industry Segment by Applications (BFSI , Retail and eCommerce , Telecommunication , Education , Media and Entertainment , Healthcare , Manufacturing and Others), by Type (Cloud and On-premises), Regional Outlook, Market Demand, Latest Trends, Continuous Integration Tools Industry Growth & Revenue by Manufacturers, Company Profiles, Growth Forecasts – 2026.” Analyzes current market size and upcoming 5 years growth of this industry.

The Continuous Integration Tools market report firstly introduced the basics: definitions, classifications, applications and market overview; product specifications; manufacturing processes; cost structures, raw materials and so on. Then it analyzed the world’s main region market conditions, including the product price, profit, capacity, production, supply, demand and market growth rate and forecast etc. In the end, the Continuous Integration Tools market report introduced new project SWOT analysis, investment feasibility analysis, and investment return analysis.

This study has been analyzed and takes into account the CAGR of the market, Valuation, Volume, Revenue (Historical and forecast), sales (current and future), and other key factors related to global Continuous Integration Tools Market.

Primitive manufacturers involved in the Continuous Integration Tools market report:

IBM
Atlassian
Red Hat
Broadcom
Puppet
Cloudbees
AWS
Microsoft
Oracle
Micro Focus
Circleci
Jetbrains
Shippable
Electric Cloud
Smartbear
Vsoft Technologies
Autorabit
Appveyor
Drone.Io
Rendered Text
Bitrise
Nevercode
Travis Ci
Phpci and Buildkite
The study objectives of Specialty Oleochemicals Market Report are:

To analyze and research the Continuous Integration Tools market status and future forecast in United States, European Union and China, involving sales, value (revenue), growth rate (CAGR), market share, historical and forecast.
To present the Continuous Integration Tools manufacturers, presenting the sales, revenue, market share, and recent development for key players.
To split the breakdown data by regions, type, companies and applications
To analyze the global and key regions Continuous Integration Tools market potential and advantage, opportunity and challenge, restraints and risks.
To identify significant trends, drivers, influence factors in global and regions
To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the Continuous Integration Tools market.
By the product type, the market primarily split into:

Cloud and On-premises
By the product Applications, the market primarily split into:

BFSI
Retail and eCommerce
Telecommunication
Education
Media and Entertainment
Healthcare
Manufacturing and Others
Continuous Integration Tools Market in World presents critical information and factual data about Continuous Integration Tools Industry, with an overall statistical study of this market based on market drivers, market limitations, and its future prospects. The widespread trends and opportunities are also taken into consideration in Continuous Integration Tools Market study.

Key Points Describing Various Features of report: –

Manufacturing Analysis – The Continuous Integration Tools Market gives a section featuring manufacturing process examination permitted by means of vital data gathered through Industry specialists and Key authorities of profiled organizations.

Remote Diagnostics Market Competition – Leading professionals have been examined depending on their company profile, product database, capacity, product/service value, transactions, and cost/revenue.

Demand & Supply and Efficiency – Continuous Integration Tools Industry report additionally provides distribution, Production, Consumption & EXIM (Export & Import).

What this Continuous Integration Tools Industry research report offers:

Global Continuous Integration Tools Market size, share assessments for the regional and country level segments.
Global market Shifts, Drivers, Constraints, Growth Opportunities, Threats, Challenges, Investment Opportunities, and recommendations.
Continuous Integration Tools market forecasts for 5 – 6 years along with historical data of all the mentioned segments, sub segments and the regional markets.
Competitive landscaping mapping along with the key trends.
Major company profiling with their detailed policies, financials, and recent developments.
Strategic recommendations for the new companies and start-ups.
Supply chain movements mapping the latest technological advancements.
Strategic endorsements in business segments based on the market assessments.

SAN FRANCISCO–(BUSINESS WIRE)–Element, a leading software provider in IT/OT data management for industrial companies, today announced a new offering featuring an API integration between its Element Unify product and AWS IoT SiteWise, a managed service from Amazon Web Services, Inc. (AWS), that makes it easy to collect, store, organize, and monitor data from industrial equipment at scale. The API integration is designed to give customers the ability to centralize plant data model integration and metadata management, enabling data to be ingested into AWS services, including AWS IoT SiteWise and Amazon Simple Storage Service (S3) industrial data lake.

Available in AWS Marketplace, the Element Unify AWS IoT Sitewise API integration is designed to allow engineers and operators to monitor operations across facilities, quickly compute performance metrics, create applications that analyze industrial equipment data to prevent costly equipment issues, and reduce gaps in production.

“We are looking forward to bridging the on-premises data models we’ve built for systems like OSIsoft PI to AWS for equipment data monitoring using Element Unify,” said Philipp Frenzel, Head of Competence Center Digital Services at Covestro.

Element also announced its ISO 27001 certification proving both the security controls protect customer data and the Information Security Management System (ISMS) provide governance, risk management, and controls required for modern SaaS applications. Element Unify also supports AWS PrivateLink to provide an additional level of network security and control for customers.

“Our customers are looking for solutions that can help them improve equipment uptime, avoid revenue loss, cut O&M costs, and improve safety,” said Prabal Acharyya, Global Head of IoT Partners for Energy at AWS. “Now, the Industrial Machine Connectivity (IMC) on AWS initiative, along with Element Unify, makes possible a seamless API integration of both real-time and asset context OT data from multiple systems into an industrial data lake on AWS.”

Industrial customers need the ability to digitally transform to maximize productivity and asset availability, and lower costs in order to remain competitive. Element Unify, which aligns IT and operational technology (OT) around contextualized industrial IoT data, delivers key enterprise integration and governance capabilities to industrials. This provides them with rich insight, enabling smarter, more efficient operations. By integrating previously siloed and critical time-series metadata generated by sensors across industrial operations with established IT systems, such as Enterprise Asset Management, IT and OT teams can now easily work together using context data for their entire industrial IoT environment.

“The tie-up between Element and AWS IoT SiteWise not only enhances AWS’s ability to manage IT and OT data together in an industrial data lake, but further demonstrates Element’s leading DataOps infrastructure solution for industrial companies,” said Joe Perino, Principal Analyst for LNS Research.

Though the API integration, AWS IoT SiteWise customers can now:

Centralize plant data model integration and metadata management into a single contextual service for consumption by AWS IoT SiteWise.
Ingest data directly into AWS services, including AWS IoT SiteWise and Amazon S3 data lake.
Integrate and contextualize metadata from IT/OT sources in single-site, multi-site, and multi-instance deployments, and deploy the data model(s) to AWS IoT SiteWise.
Enable metadata to be imported into AWS IoT SiteWise from the customers systems through Inductive Automation Ignition and PTC KEPServerEX to create context data.
Keep both greenfield and brownfield AWS IoT SiteWise asset models and asset hierarchies updated as the Element Unify models adapt to changes in the underlying data.
Close the gap between raw, siloed, and disorganized IT/OT data and enriched, contextualized, and structured data that can be easily paired with business intelligence (BI), analytical tools, and condition monitoring tools like AWS IoT SiteWise.
Empower the user to build complex asset data models with ease.
Build and deploy data models to AWS IoT SiteWise and keep them up to date with changes happening in source systems.
Easily create the underlying data models to power AWS IoT SiteWise and many other analytical systems including Amazon SageMaker, Amazon QuickSight, and Amazon Lookout for Equipment.
“Operations data must be easy to use and understand in both the plant and in the boardroom. Industrial organizations who transform their plant data into context data for use in AWS IoT SiteWise, and other AWS services, will drive greater operational insights and achieve breakthrough business outcomes,” said Andy Bane, CEO of Element.

Element Analytics

Element is a leading software provider in IT/OT data management, digitally enabling operations for industrial organizations. Element Unify transforms siloed IT/OT data into context data by enabling contextualize once, compute anywhere, consume everywhere data that is used to achieve cleaner, safer, healthier, more profitable operations. Element’s customers represent over $750 billion in revenue, $500 billion in fixed assets and 450,000 employees.

In the duality of continuous integration/continuous delivery (CI/CD), CI focuses on build automation. Leveraging CI practices for software delivery is normal for most organizations and can be seen as a solved problem. However, the demand for increasingly distributed applications has risen with the expansion of microservices, and development teams have the expectation that every commit should be a build.

Continuous integration has, surprisingly, become an unexpected bottleneck with these expectations. To begin modernizing CI, it’s important to first understand some of the challenges. Let’s dig in.

Challenges of Continuous Integration
Given that build and release candidates follow advancements in development technology closely — such as new languages, packaging and paradigms in testing the artifact — expanding the capabilities in continuous integration implementations can be challenging. With the introduction of containerized technology, the firepower and velocity required to build greatly increased.

Scaling Continuous Integration Platforms
The infrastructure required to run a distributed CI platform can be as complex as the applications they are building because of the heavy compute requirements. Consider how much a local machine’s resources are tied up during a local build and test cycle. Now, multiply that by the number of folks on a team or in an organization and you start to understand just how much compute power is being used. Distributed build runners are just one area that can be complex, and managing when new build nodes are spun up and spun down can depend on the platform/end user.

Keeping Up With the Technology Velocity
The adage of “the only constant in technology is change” is true. New languages, platforms and paradigms are to be expected as technology pushes forward. The ease of including new technologies in a heterogeneous build or accepting new testing paradigms, however, can be difficult for more rigid CI platforms. These legacy platforms were designed for only a small subset of technologies. With legacy or rigid approaches, the dependency management required to maintain technical velocity becomes a significant burden.

What it Takes to Modernize Continuous Integration
The simplest way to change continuous integration practices and platforms is to take what I call the four-pillar approach: platform infrastructure modernization, engineering efficiency strategy, test optimization and rapid pipeline development. Making strides in any of these pillars will put you on the path toward modernizing your continuous integration platforms and practices. Making strides in all of the pillars will set up your DevOps teams for significant success.

Platform Infrastructure Modernization
The location where the actual builds and packaging take place (e.g. build nodes/runners) do most of the heavy lifting and have a fairly elastic workload nature. Builds (e.g. a JAVA JAR build) and packaging (e.g. a Docker Image Compose) are compute-heavy tasks. Once the build and packaging are complete, the runners can sit idle. This shows the importance of having an ephemeral build node. The build node spins up during the task, then spins down or is destroyed after the build task is done, so as to not drain resources.

Engineering Efficiency Strategy
A core tenet of engineering efficiency is meeting your internal customers where they are. For software engineers, this means being as close to their tools and projects as possible. Like many modern pieces of application infrastructure, shifting left to the developer means being included in the project structure in source code management (SCM).

A common disconnect in CI platforms is dependency management. Over the past decade, for software engineers, this problem has been solved with package or build tools such as Maven, Gradle and NPM. Engineers need to simply define implicitly or explicitly what they need, and the dependencies will be resolved. Continuous integration tools can suffer from a disconnect, since we are leveraging several tools that potentially don’t have a common syntax. So, again, it’s important developers have close oversight on all the tools they need to complete their projects to reduce this friction.

Test Optimization
While initial confidence in getting tests into the CI pipeline expands, more tests — and sometimes inappropriate test coverage — is introduced by the ease of integration with the pipeline. An even harder problem to identify and rectify is flaky tests. A flaky test is one that both passes and fails periodically without any code changes. This becomes a twofold problem: increasing execution time and lack of confidence with flakiness. The most logical solution to avoid these issues is adding test optimization. A modern continuous integration solution should be able to visualize pipeline order, timings and overall execution to help identity (and eventually rectify) excessive coverage and flakiness.

Rapid Pipeline Development
Software is an exercise in iteration. The lower the barriers to entry for iteration to occur, the more gains in engineering efficiency and agility are achieved. Local builds happen dozens of times before reaching a committable stage, moving them forward to a dev-integration environment. It’s here where having a local environment is key. Oddly, most continuous integration pipelines are designed to run externally from a local machine. So, the ability to move to a local CI pipeline helps build confidence that the externally run pipeline will be successful.

Continue on Your Continuous Integration Journey
Continuous integration might seem like a solved problem for many organizations, but as with any technology, there is always room for improvement and modernization. Like any engineering efficiency paradigm, CI is a journey and the destination ever-evolving.

Eds: Disclaimer: The following press release comes to you under an arrangement with Business Wire India. PTI takes no editorial responsibility for the same.) Bangalore, Karnataka, India & Munich, Germany – Business Wire India Trusted Platform Modules (TPM) enable secured remote software updates, disc encryption and user authentication. Hence, they are crucial for connected industrial, automotive and other embedded devices. To further facilitate seamless integration in Linux-based systems, Infineon Technologies AG (FSE: IFX / OTCQX: IFNNY) now provides its leading OPTIGA™ TPM 2.0 solution with a comprehensive TSS* host software implementing the latest FAPI standard. Infineon has developed the open-source software jointly with Intel Corporation and Fraunhofer Institute for Secure Information Technology SIT. By using Infineon’s plug-and-play OPTIGA TPM 2.0, IoT system integrators can significantly improve the security of connected products. Software integration with TSS-FAPI does not require specific skills in low-level security specifications and reduces source code development by a factor of up to 16. Therefore, expenses and time to market can be reduced. Additionally, manufacturers can accelerate the process for certifying their industrial devices according to the IEC 62443 standard for industrial applications, which requires hardware-based safety from level 4 upwards. The FAPI specification has been released recently as an international standard by the Trusted Computing Group (TCG). The specification is implemented in the TSS stack1 together with the associated tools and plug-ins. The TSS stack is open-source software, which allows seamless integration of the TPM 2.0 in Linux-based systems. This includes the support of typical Linux software for device authentication, data encryption, software updates and remote device management. In addition, the FAPI enables the native support of the PKCS#11 standard as a generic interface for user authentication, single sign-on and e-mail encryption/signing. The FAPI provides a default configuration for cryptographic functionalities, system integration and automated processing of security mechanisms according to the latest state-of-the-art and industrial best-practices. The OPTIGA TPM acts as a vault for sensitive data in connected devices and lowers the risk of data and production losses due to cyberattacks. Infineon’s TPMs are certified by independent certification bodies according to Common Criteria, an international set of guidelines and specifications developed for evaluating information security products. The TSS stack including the recent FAPI has been verified with the Infineon TPM portfolio to achieve compliance and interoperability. Availability Application developers can use the OPTIGA TPM SLB 9670, OPTIGA TPM SLI 9670 and OPTIGA TPM SLM 9670 Iridium boards and TSS Quickstarter offered by Infineon to get started right away. Board and source code packages for the Infineon AURIX™ as well as for Arduino microcontrollers are available now.

NEW YORK, March 18, 2021 /CNW/ — QuerySurge today announced the availability of its enterprise data validation & testing solution in the Microsoft Azure Marketplace, an online store providing applications and services for use in the Azure cloud. QuerySurge customers can now take advantage of the productive and trusted Azure platform, with streamlined deployment & management and pay-as-you-go pricing.

“We are excited to release QuerySurge in the Azure marketplace as a pay-as-you-go solution,” said Bill Hayduk, CEO of RTTS and leader of the QuerySurge software division. “Microsoft Azure solves some of the biggest challenges that our customers face - procuring the optimal environment with flexible scalability, full disaster recovery capabilities and enterprise-level security. And the new pay-as-you-go pricing, where customers pay only for what they use each month, provides great financial flexibility.”

QuerySurge automates the data validation and testing of data warehouses, big data lakes, data migration projects, BI reports and enterprise applications with full Azure DevOps functionality for continuous testing of customers’ data pipeline.

Integration with Microsoft Azure DevOps

QuerySurge now provides an extension for Azure DevOps, Microsoft’s toolchain for developing and deploying software.

“The QuerySurge integration with Azure DevOps allows users to automate the data validation in their DevOps CI/CD pipelines,” said Hayduk. “Implementing QuerySurge throughout the development cycle ensures that data changes, whether during data migrations from on-prem to the cloud or full in-cloud data warehouse testing, load as expected throughout the current DevOps cycle and corresponding releases.”

QuerySurge

Launched in 2012, QuerySurge ensures that data extracted from data sources remains intact in the target data store by analyzing and pinpointing any differences quickly. With over 150 customers in 30+ countries and a partner ecosystem that includes 70+ leading technology vendors, systems integrators and consulting organizations, QuerySurge is at the forefront of the data quality movement.

QuerySurge is the software division of RTTS, the premier pure-play QA & testing organization that specializes in test automation. Headquartered in New York, RTTS has had 1,000+ successful engagements at over 700 corporations since 1996. RTTS is a Microsoft Gold DevOps Partner and has been part of the Microsoft partner ecosystem since 2005.

Harness CI Community Edition (aka Drone) now includes pipeline execution visualizations, live debugging capabilities, and enterprise-grade security and governance

SAN FRANCISCO – March 18, 2021 – Harness, the leading platform for software delivery, today announced major updates to its open source module, Harness Continuous Integration (CI) Community Edition (formerly Drone.io). Along with a new dev experience and user interface designed to mirror Harness’ industry-leading Software Delivery Platform, today’s release provides open source contributors with new pipeline execution visualizations, advanced governance and security controls, and live CI pipeline debugging capabilities, to build and test code in record time.

Today’s software development and engineering teams waste hours of valuable time and resources waiting on pipeline builds, only to scramble to find a solution when one breaks. To mitigate this frustrating and time consuming cycle, Harness enhanced its marquee open source CI module, with a series of updates that allow developers to quickly respond to failures and create a shorter path back to innovation. Rebranded as Harness CI Community Edition, if a build does break, the developer is now presented with a link to the failed CI server allowing them to debug with ease.

“Following the acquisition of Drone, Harness made it our mission to provide the OSS community with the investment and tools they need to simplify and accelerate software delivery,” said Brad Rydzewski, CEO and founder of Drone.io. “We’re proud to not only deliver on that message and meet the needs of today’s overburdened engineer, but do so with features that surpass proprietary vendors.”

Today’s news marks Harness’ first major contribution to the open source software community following its acquisition of Drone in August 2020. Since Drone was founded in 2013, it has earned more than 100 million downloads from DockerHub, boasts 50,000 active monthly users, and is the most ‘liked’ CI/CD project on GitHub with 21,319 likes – 5,500 more than Jenkins, 13,000 more than Spinnaker, and 18,000 more than Jenkins-X. Additional updates to Harness CI Community Edition include:

New Dev Experience: A sleek, modern user interface designed to match other Harness product modules creates a smooth transition for developers working between Harness modules.

Pipeline Execution Visualizations: Developers can understand exactly what pipeline steps are executing, when, and in what order – all using a visual map. This is an easier and faster way of tracking complex pipelines than reviewing them as text in log files.

Live CI Pipeline Debugging: Debugging builds that work on developers’ computers and not on the CI server can be a time consuming and difficult undertaking. With live CI pipeline debugging, developers can solve build failures occurring on CI instances quickly.

Enhanced Governance & Security: Managers no longer need to add users and control access to pipelines via a command line or the API. The CI system is managed in a faster and less complicated user interface that is also scalable.

Harness will continue to invest, innovate and support the open source community with additional OSS announcements planned for 2021.
Harness

Based in San Francisco, Harness is the only end-to-end platform for complete software delivery designed to provide a simple, safe and secure way for engineering and DevOps teams to release applications into production. Harness uses machine learning to detect the quality of deployments and automatically roll back failed ones, saving time and reducing the need for custom scripting and manual oversight. Harness customers include NCR, Kohl’s, Discover Financial, Nationwide Insurance and McAfee.

The report on global Continuous Integration Tools market gives information about the probability of gaining profits and working of the new emerging projects and tasks that the organizations and emerging startups are looking forward to execute. Further, the document consists of key opportunities and growth prospects that will exist in this industry space. Moreover, the document offers detailed knowledge about the major crisis and critical situations that have negative impact on the business space. The document also contains data about the past as well as current events that has happened in the business space and gives evaluation of several industry aspects based on those statistics.

Key Companies that are covered in this report:

Cloudbees
Micro Focus
Atlassian
Microsoft
CA Technologies
IBM
Puppet
AWS
Red Hat
CircleCI

Moreover, the market offers data on growth matrix and also data about the production as well as consumption pattern that have an overall impact on the industry space in the coming years. Also, the document gives thorough insights about the growth drivers, major trends, and key growth accelerators that have a major impact on the growth of global Continuous Integration Tools market over the analysis timeframe.

The current document on global Continuous Integration Tools market consists of several major trends and aspects that have a major impact on the industry share. The document divides the regional landscape in various geographies and all of them are analyzed based on their production patterns, market share, and growth rate of each region during the forecast timeframe. Further, the document contains information about the production patterns, supply chain ecosystem, and the product demand witnessed around several regions.

Key segments of the global Continuous Integration Tools market

Product Overview, 2012-2026 (USD Million)

Cloud
On-Premises

Application Overview, 2012-2026 (USD Million)

Education
Healthcare
Media and Entertainment
Others

Moreover, the report on global Continuous Integration Tools market contains thorough evaluation of several segments and sub segments and each segment is well analyzed based on the current industry trends and forecasts that help in market growth over the analysis time frame.
Further, the archive offers far reaching viewpoint on the store network framework when the COVID-19 transmit and how the pandemic has affected the economy of a couple of nations all things considered. Essential countries that contribute a huge industry share in the global Continuous Integration Tools market are Argentina, Thailand, South Africa, India, Nigeria, Switzerland, Netherlands, Spain, UK, China, Poland, Belgium, Canada, Philippines, Columbia, Indonesia, Germany, South Australia, United States, Malaysia, Mexico, Italy, Chile, Saudi Arabia, Turkey, Egypt, UAE, Sweden, Korea, Taiwan, France, and Rest of the World.

The report further gives detailed information about the inspiring stories of the companies that have made it work from them and went from small to large organizations over a span of few years.

Why you should buy this report?

* This report offers a concise analysis of the Continuous Integration Tools market for the last 5 years with historical data & more accurate prediction for upcoming 6 years on the basis of statistical information.

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* The report is a complete guideline for the clients to arrive an informed business decision since it consists of a detailed information for better understandings of the current & future market situation.
The report also answers some of the key questions given below:

* Which end-user is likely to play a crucial role in the development of the Continuous Integration Tools market?

* Which regional market is expected to dominate the Continuous Integration Tools market in the forecast period?

* How is consumer consumption behavior impacting the business operations in the market due to the current COVID-19 scenario?

Our target audience is a wide range of corporations, manufacturing companies, product/technology development institutions and industry associations that require understanding of a market’s size, key trends, participants and future outlook of an industry. We intend to become our clients’ knowledge partner and provide them with valuable market insights to help create opportunities that increase their revenues. We follow a code– Explore, Learn and Transform. At our core, we are curious people who love to identify and understand industry patterns, create an insightful study around our findings and churn out money-making roadmaps.

Anchore, the leading providers of continuous security and compliance for containers, has announced an integration with GitLab, the complete DevSecOps platform, delivered as a single application. With this integration, organizations will be able to automate security and compliance checks from the early stages of the development cycle, speeding software development and reducing risks. Anchore also announces it has joined the GitLab partner program.

Anchore now integrates seamlessly with GitLab to simplify security and compliance workflows for developers and to enable DevSecOps practices. Anchore performs deep container image scanning that identifies vulnerabilities and surfaces a wide range of security and policy infractions, including vulnerabilities and other risks during development. Together, the Anchore and GitLab security integrations will:

Display vulnerability results of container scans directly in GitLab security dashboards
Surface these findings in merge requests that identify changes needed to remediate issues
Enable updating of merge requests with a package version to resolve vulnerabilities
Seamlessly manage the risk profile in one place through GitLab’s Risk Management Framework (RMF), reducing the friction of typical software security scanning

Digital transformation has changed software development practices as organizations seek to deliver applications more quickly and update them more frequently. This shift, combined with increasing cybersecurity threats, requires developers to implement security and compliance checks throughout the DevSecOps life cycle. The integration between Anchore and GitLab helps to automate these DevSecOps best practices for enterprises, government agencies, and open source communities,” said Saïd Ziouani, Anchore CEO and Cofounder.

“Strengthening the software development security and compliance postures of enterprises and public sector organizations is paramount to the missions of both GitLab and Anchore. We are pleased to partner through this integration to help our joint customers to increase their speed to mission delivery and to reduce the risks associated with software development,” said GitLab Vice-President of Global Channels, Michelle Hodges.

The U.S. Department of Defense (DoD) uses both Anchore and GitLab as part of their Platform One Initiative to speed the development of secure and compliant software. Anchore is used to develop hardened containers for Iron Bank (DoD’s software repository for containers) based on DoD best practices.

“Today, more than ever before, security is the most essential aspect of software development for government and critical infrastructure—and is the core of the DoD DevSecOps Initiative and Platform One. Now developers have the ability to push validated code into production on an ongoing basis, resulting in shorter development cycles, less debugging, and more rapid feature development,” said Major Robert Slaughter, Director of DoD Platform One.

The global Continuous Integration Software market research report provides the detail analysis of various factors that used for the growth of the market. These factors provide thoughtful and deep knowledge of the flow and future directors of the market. Along with this, these factors also help to provide new market opportunities for the new players who are entering in the global Continuous Integration Software market. The statistical and numerical data that is provided in the market research report is integrated in the tabular, graphical and pie charts format, which makes easy for the marketers to understand the facts and figures. Moreover, the market research report also covers the information on market drivers, restrains, future opportunities and challenges that helps in the growth of the global Continuous Integration Software market.

The report study has also discovered new suggestions and applications of various organizations, which the manufacturers to improve their business on a global scale. The research report also covers the current Covid-19 impact on various industries and channels and what will be its future impact on the global Continuous Integration Software market.

Top Companies Covered:
CircleCI
Jenkins
Codeship
Travis CI
Semaphore
Bitrise

The global Continuous Integration Software market research report covers market attractiveness analysis, where each segment of the market is benchmarked based on its market size, growth rate and general appeal. The global Continuous Integration Software market is expected to achieve market growth as well as upcoming market opportunities or challenges in the forecast period of 2020 to 2027. The company researcher analyses the market is mounting at a vigorous CAGR in the forecast period.

Moreover, the global Continuous Integration Software market research analysis report also provides the complete details on the major elements of the market like mergers and acquisition of the major key players, research and development activities, new product launches, product pricing and Covid-19 impact on the major players of the market. The global Continuous Integration Software market research report covers estimation of the market from 2020-2027 and historical information from 2015-2019.

Global Continuous Integration Software Market by Types:
Cloud Based
Web Based

Global Continuous Integration Software Market by Applications:
Large Enterprises
SME

Global Continuous Integration Software Market by Geographical Region:

North America (U.S., Canada, Mexico)
Europe (Germany, U.K., France, Italy, Russia, Spain, and Rest of Europe)
Asia Pacific (China, Japan, India, Russia, and Rest of Asia Pacific)
Latin America (Cuba, Brazil, Argentina, and Rest of Latin America)
Middle East & Africa (South Africa, GCC and Rest of the Middle East & Africa)

The Market Coverage chapter in the market research report provides an important assessment of market segments like market types, application and region, also provides application view, significant makers, product offerings and measure period.

The Outline chapter in the market research report emphases on all the key market drivers, restrains and market limitations, which helps the growth of the global Continuous Integration Software market in the current and forecast period. Along with this, this chapter also provides market development rate, drive market patterns and major market viewpoints.

The Regional Analysis chapter in the market research report focuses on all the division and sub-division across the globe. In this chapter, the marketers analyze and evaluate the most recent market import and fare patterns, along with this, the report also provides information on the production and utilization quantities of the Continuous Integration Software products. The market major parts in every area and income period details are also covered in the global Continuous Integration Software market research report.

Takeaway points from this report:

The global Continuous Integration Software market research report provides in-depth analysis of market size, important industry players and key fragments.
The research report covers various features that helps manufacturers to understand the current industry patterns and also helps them to plan for their dragging objectives and fast-track their growth cycle.
The research report also provides data on the recent market trends and developments, product classification, technological advancements and industry chain analysis, which also helps the industries in their growth factors.

We at Zeal Insider aim to be global leaders in qualitative and predictive analysis as we put ourselves in the front seat for identifying worldwide industrial trends and opportunities and mapping them out for you on a silver platter. We specialize in identifying the calibers of the market’s robust activities and constantly pushing out the areas which allow our clientele base in making the most innovative, optimized, integrated and strategic business decisions in order to put them ahead of their competition by leaps and bounds. Our researchers achieve this mammoth of a task by conducting sound research through many data points scattered through carefully placed equatorial regions.

Computer vision, or the capability of artificially intelligent systems to “see” like people, has been a subject of expanding interest and thorough examination research for recent decades. As a method of copying the human visual framework, the research in the field of computer vision implies to create machines that can automate tasks that require visual cognition. Notwithstanding, the way toward interpreting pictures, because of the fundamentally more noteworthy amount of multidimensional information that needs analysis, is considerably more perplexing than understanding different types of binary information. This makes creating AI systems that can perceive visual information more complicated.

Computer vision software is changing companies and making the lives of shoppers simpler as well as additionally fascinating. As a field, computer vision has got a lot of exposure and a good amount of investment. The North American market for computer vision programming has had a total investment of $120 million, while the Chinese market flooded to $3.9 billion.

Keeping the growth prospects in mind, advances or amalgamation of computer vision with different technologies will dominate the year of 2021.

Integration with Edge Computing
Machines that connect with the internet and the cloud can gain from the information accumulated by the network overall and change as needs be, optimizing the system’s performance. However, internet connection and the cloud isn’t generally an assurance. That is the place where edge computing comes in.

Edge computing tackles the issue of network accessibility and latency. Presently devices can be set in areas where a network connection is awful or nonexistent without it considering the consequences of the analysis. Moreover, edge computing can counterbalance some of the expenses of utilization and maintenance of cloud computing for data sharing.

For computer vision software, this implies a likelihood to respond better in real-time, and just move significant bits of knowledge to the cloud for further analysis. This component is particularly helpful for autonomous cars.

Emotion AI
Emotion AI utilizes CV technology to analyze facial appearances and eye developments in photographs and videos, with the point of reading a person’s emotional responses. A London-based emotion AI start-up records the facial expression of a sample crowd who watch a specific promotion through the camera of their PC or cell phone. Realeyes brought $12.4m of funding up in 2019 to help large brands, for example, AT&T, Mars, Hershey’s, and Coca-Cola, distinguish emotions from pictures of facial expressions prior to rating every promotion for attention, emotion, sentiment. Apple additionally gained Emotient in 2016, and Facebook is building up its own products.

Embedded Vision
Embedded vision incorporates image capture and processing abilities to a single device. This computer vision innovation has discovered its way into numerous modern applications which incorporate arranging systems that demand inspection services. Moreover, providers have even integrated embedded vision into autonomous drones, smartphones and self-driving automobiles.

Mixed Reality: VR and AR upgraded
Today, any VR or AR framework establishes a vivid 3D environment, yet it has little to do with the real one the consumer is situated in. Most AR devices can perform basic outputs of the climate (Google ARCore can, for instance, recognize level surfaces and changes in light conditions), and VR frameworks can identify the developments of the customer through head tracking, controllers, and so on, however, their capacities stop there.

Computer vision programming is driving VR and AR into the next phase of advancement that some call Merged Reality (MR). It’s already in the market, however, 2021 will see more developments in this phase.

CV as a Service (CVaaS)
CVaaS is a sort of software-as-a-service hosted in the cloud. It permits organizations to lease as opposed to building a computer vision platform. Running computer vision in the cloud has carried innovation to new customers. The on-demand access to algorithms and APIs under a pay-as-you-go model, make the innovation both affordable and scalable.

In the years to come, CVaaS will turn into a vital piece of a business’ automation process. Additionally, CV models will progressively run at the edge, permitting them to be embedded in a more noteworthy number of devices.

Continuous Integration and Continuous Delivery (CI/CD) can bring a seamless integration from end-to-end for the software development and deployment process. By doing this, CI/CD allows developers to dedicate more of their time developing code to improve software features instead of worrying about the deployment.

Yet, they are still faced with many security challenges. CI/CD might speed up the process but not the security. With DevSecOps – Development, Security, and Operations -, however, there is a possibility to accelerate the delivery of security within the software. DevSecOps engineers try to operate most security controls as part of the software by introducing it as a design constraint and then getting it checked by the CI/CD pipelines without damaging the integrity of controls.

Why DevSecOps?

As digital transformations increase, there is a vital need for safe and secure software, otherwise, everything, from the building to the delivery can be at risk. Security breaches are now one of the biggest threats to companies and products.

DevSecOps promotes collaboration between the development and security teams, hence, avoiding late handoffs to security professionals. By introducing security at the beginning of the process, the value and quality of the product can only be reinforced. Indeed, without DevSecOps, the software might be considered unsafe at the last minute, thus causing multiple costly iterations. With DevSecOps, security standards are implemented directly into the pipelines making the products more secure from the beginning.

Overall, DevSecOps ensures credibility and agility in the market, as well as trust with consumers.

DevSecOps in CI/CD

There are many security vulnerabilities that can exist in open-source software. Hence, implementing DevSecOps practices within CI/CD will bring continuity to securing software deliveries.

Integrating automated security checks within the pipeline will allow developers to have early warnings of vulnerabilities and monitor any security defects or else. With an integrated continuous security approach, companies can expand while upgrading their security and development process as it goes.

Moreover, unit tests and static code analysis operate close to source code as well as run checks without executing the code. Hence, investing in security unit tests and static analyzers will only be beneficial as it can speed up the lifecycle while detecting quickly any vulnerabilities.

The future of DevSecOps and CI/CD pipelines

With the many challenges our world bring today, security is crucial in order to remain on top of the market. With DevSecOps, companies are able to speed up their CI/CD pipelines all the while keeping it secure from any vulnerabilities. Collaboration and communication are then vital between development and security teams and shouldn’t be overlooked.

With the rise of DevSecOps, security has become an important part of the continuous delivery pipeline. Having continuity and security ensures the best software delivery.

Java may be slipping down the charts, but Microsoft is still feeling the love for the world’s second most popular language. The Azure cloud is now equipped with new libraries to ease the integration of Java code with online resources, the company announced in a blog post.

“The latest Azure Resource Management Libraries for Java is a result of our efforts to create a resource management client library that is user-friendly and idiomatic to the Java ecosystem,” said Weidong Xu, senior software engineer, Azure Management SDK for Java. “These new libraries provide a higher-level, object-oriented API for managing Azure resources, that is optimized for ease of use, succinctness, and consistency.”

The libraries are designed around a fluent or chained methodology, which embodies a workflow approach to chaining together the components for a complete process. In particular, the libraries are designed to automatically fulfil dependencies or resource allocation processes for each task.

For example, creating a virtual machine can chain in the required storage account and service plan, and it is similarly easy to programmatically batch create and delete Managed Disk Instances.

Other new library functions include support for Microsoft Authentication Library (MSAL) through the new Azure Identity library, which enables developers to acquire tokens from the Microsoft identity platform endpoint in order to authenticate users and access secured APIs.

The company lists some twenty services available through the new libraries, including CDN, Compute, Containers, DNS, SQL, and Network and Traffic Management. Documentation and code snippets are available in the Azure SDK and developer blogs.

Overview

Microsoft announced the public preview of Event Grid on IoT Edge, bringing event-driven publish/subscribe messaging with routing capabilities to Internet of Things devices running anywhere — including fully disconnected or intermittently connected environments outside of Azure.

What Is Azure Event Grid?

Azure Event Grid has been available since August 2017 as a fully managed event-routing service. It connects event publishers to event subscribers using a push-push publish/subscribe mechanism, with filtering to ensure each subscriber receives only the events it cares about. The service also implements reliability features such as automatic retries and dead-lettering for undeliverable messages.

Events in Event Grid conform to one of three schemas:

• EventGrid schema — the native out-of-the-box schema provided by Azure.
• CustomEvent schema — allows an application to define its own event structure.
• CloudEvents schema — implements the CNCF CloudEvents industry standard for interoperable event-driven communication.

Each event consists of an envelope (used to route the message) and a data payload whose structure depends on the originating application or service.

Core Event Grid Concepts

• Publishers (sources) — Azure services or third-party sources that emit events. Examples include IoT Hub, Azure Blob Storage, and Azure Event Hubs.
• Topics — The channel to which publishers send events. Topics can be system-defined or custom.
• Subscribers (event handlers) — One or more consumers that receive events from a topic. Examples include Azure Functions, WebHooks, and Event Hubs.

What Is IoT Edge?

Azure IoT Edge is a managed service that extends IoT capabilities to devices running outside of Azure. Devices integrate with Azure IoT Hub to maintain cloud communication while also being able to operate in disconnected or bandwidth-constrained scenarios. IoT Edge workloads run as Docker containers called modules, coordinated by an IoT Edge runtime that handles message routing between those modules.

Event Grid on IoT Edge: What Changes?

With this public preview, Event Grid is now packaged as a Docker container module that can run directly on an IoT Edge device. This enables full publish/subscribe event-driven messaging at the edge, without requiring a round-trip to Azure. Specifically, the integration supports:

• Local event processing — modules on the same device can publish and subscribe to events without leaving the device.
• Device-to-device communication — events can be forwarded between IoT Edge devices.
• Cloud exchange — events can flow between edge devices and Azure services.
• Feature parity — Microsoft states there is complete feature parity between Event Grid on Azure and Event Grid on IoT Edge.

Supported Modules at Preview Launch

At the time of the public preview announcement, the Blob Storage module was the only out-of-the-box IoT Edge module with built-in Event Grid integration. It can trigger events whenever a blob is created in a container. Other custom modules can integrate with Event Grid by calling the HTTP endpoints exposed by the Event Grid module — details are documented in Microsoft’s official documentation.

Getting Started

To use Event Grid on IoT Edge, follow these high-level steps:

1. Provision an IoT Edge device — this can be a physical device or a virtual machine available from the Azure Marketplace pre-configured for IoT Edge.
2. Configure modules in IoT Hub — define the desired modules (including the Event Grid module) in your IoT Hub configuration. IoT Hub pushes this configuration to the device automatically.
3. Download and install Docker images — the IoT Edge runtime pulls the required container images, including the Event Grid module available on Docker Hub.
4. Configure routing — set up event subscriptions and routing rules to connect publishers to subscribers on the device, across devices, or to Azure.

Microsoft provides several end-to-end examples in the official documentation, covering local events, cloud event exchange, and event forwarding scenarios.

Why This Matters for Edge Architectures

Bringing Event Grid to the edge is significant for IoT architects and DevOps engineers building reliable, event-driven systems on constrained or intermittently connected devices. Previously, event-driven coordination between device modules required custom messaging solutions or full cloud connectivity. With Event Grid on IoT Edge, teams can apply the same familiar Azure event-routing patterns — filtering, retries, dead-lettering, and multiple subscribers — directly on the device, reducing latency and cloud egress while improving resilience in offline scenarios.

Attribution: This article is based on the original InfoQ report and Microsoft’s public preview announcement of Event Grid on IoT Edge (December 2019). Original InfoQ coverage referenced Steef-Jan Wiggers, Microsoft Azure MVP and Cloud Azure Technology Consultant.

Testing Is Still in the Dark Ages

The uncomfortable truth in software delivery is that testers, test engineers, and developers spend a disproportionate amount of their working day waiting. Even teams that have adopted continuous integration, continuous testing, and continuous delivery pipelines still contend with systemic inefficiency: too much waste, too much complexity, too many bottlenecks, and too much idle time.

That waiting persists whether a team runs automated pipelines or traditional waterfall processes. Every delivery process has choke points, and the most consistent bottleneck in software delivery is testing.

Software testing became a formal discipline in the 1990s. Despite decades of advances in automation, tooling, and practitioner skills, the fundamental approach has changed surprisingly little. The application under test remains largely a black box. Testers rarely get meaningful visibility into how code is executed at runtime, so they compensate by casting the widest possible testing net — hoping to catch defects through sheer coverage breadth.

The problem with a broad net is twofold:

• The holes are often too large — defects escape.
• The net is sometimes aimed in the wrong direction — tests validate code paths that haven’t changed while genuinely risky areas go untested.

Teams compensate by growing their test suites and automating more. But automation has a ceiling. Once the cost and effort of maintaining tests outweighs the value those tests deliver, further automation becomes its own form of waste. At that point, the principle of less is more — running fewer, better-targeted tests — becomes more effective than adding more test cases.

Consider this: a large proportion of the tests run in any given iteration cover code paths that haven’t changed in that build cycle. Those test runs produce no new information. Meanwhile, genuinely risky, recently changed, or poorly covered code paths may receive inadequate attention. The result is applications that reach production carrying hidden quality risks.

How AI Can Illuminate Testing

Artificial intelligence is broadly defined as the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

AI performs best when it has large volumes of data to analyse, pattern-match, and draw inferences from. Software testing generates exactly that kind of data at scale. Modern applications can comprise millions of lines of code and thousands of associated test cases. The sheer volume makes human analysis impractical — no team can manually reason about which tests to prioritise across a codebase of that size for every build.

AI and quality analytics address this by instrumenting the application under test and observing how code is actually executed. By gathering millions of data points across test interactions — tracking which lines of code each test exercises and what behaviour it validates — an AI system can:

• Identify untested or under-tested code paths (test gap analysis).
• Correlate specific code changes to the tests most likely to detect regressions in those changes.
• Produce prioritised recommendations for where to invest new test effort.
• Surface actionable quality analytics in seconds, rather than requiring manual review of coverage reports.

In short, AI shifts testing from trawling with a broad net to targeted spearfishing: fewer tests, better aimed, with a higher probability of finding real defects.

Reducing Quality Risks Through AI

Even in mature Continuous Delivery pipelines, applications typically pass through multiple test stages — unit, integration, system, and user acceptance — before reaching production. Most teams rely on static code coverage tools to understand how much of their codebase is tested. However, static coverage metrics tell you whether a line of code was touched during testing, not how well the behaviour of that code was validated across different test stages and real interaction paths.

This gap in understanding creates hidden quality risks. Test suites can show high coverage percentages while still leaving critical logic paths unverified. Defects in those unverified paths escape to production.

AI-driven quality analytics address this by tracking the detailed “footprint” each test leaves across the application at runtime. By processing those footprints at scale, an AI system can:

• Identify the highest-risk areas of the application — code that is complex, frequently changed, or poorly covered.
• Pinpoint specific test gap traps where existing tests provide the illusion of coverage without meaningful validation.
• Guide teams to allocate test investment where it will reduce the most risk, rather than where it is easiest to add tests.

Reducing Test Execution Time Through AI

In a typical regression test cycle, the vast majority of the code being tested has not changed since the previous build. Tests that re-validate unchanged code paths provide no new signal — they consume time and infrastructure without improving confidence in the release.

If AI can identify which code has changed and which tests meaningfully exercise that changed code, teams can run a precisely targeted subset of their full test suite for each build. Advocates of this approach argue that intelligent test selection could reduce test execution time and commit-to-deploy cycle time significantly, though specific figures will vary by codebase, pipeline, and tooling.

An AI-powered test selection system can factor in multiple signals when composing an optimised test run:

• Change impact: Which tests cover the code paths affected by the current code diff?
• Flakiness: Which tests have an unstable pass/fail history that warrants closer attention?
• Production usage: Which parts of the application are accessed most frequently by real users, making regressions there most damaging?
• Complexity: Which recent changes introduced significant new complexity and therefore carry higher inherent risk?
• Historical failure rates: Which tests have the highest probability of detecting defects and should therefore run first and most often?

This targeted testing model finds defects earlier in the pipeline, tightens the feedback loop between code commit and defect detection, and reduces the cognitive overhead on developers waiting for test results. Faster, more precise feedback also means testers get quicker confirmation when their work uncovers a real problem — a meaningful improvement to day-to-day job satisfaction.

Embracing AI in Your Testing Strategy

Modern applications — particularly microservices architectures — involve millions of lines of code, complex dependency graphs, and thousands of test cases accumulated over years of development. No human team can reason about that volume of information in real time for every build. That is not a criticism of testing professionals; it is simply a scaling problem that manual processes cannot solve.

As business demands push teams toward faster release cadences and shorter cycle times, AI-augmented testing offers a practical path to:

• Maintaining or increasing delivery velocity without proportionally increasing test infrastructure costs.
• Closing coverage gaps that static analysis tools miss.
• Eliminating the repetitive, low-value work that leads to tester burnout.
• Giving developers and testers faster, more actionable feedback.

AI does not replace skilled testers — it removes the drudgery and amplifies their impact by directing effort where it produces the most value. The teams that adopt intelligent quality analytics early will carry less technical debt, ship with greater confidence, and spend more time on the creative and investigative work that testing professionals find most rewarding.

Originally written by Scott Dainty, Head of Enterprise Sales at SeaLights, a Software Quality Intelligence Platform. First published on softwaretestingnews.co.uk (October 2019).

Integrating UX with DevOps is essential for DevOps teams to ensure they solve the right problems and are understanding the user’s real needs.

DevOps is a rapidly evolving organisational model with teams arranged around a product or service to deliver value to their users more quickly. Through breaking down organisational silos, the delivery teams work on a product or product improvements instead of projects which are usually no longer maintained or supported after they’re completed. For many organisations, it is a significant culture shift when transitioning to product or service orientated teams and adopting DevOps practices helps to make the transition easier.

Nowadays, DevOps is much more than just getting development and operations teams to work together. This is a culture where trust, collaboration, communication and a failing fast mindset are a part of the day-to-day work of the delivery teams as well as the whole organisation. It’s also a model where our users are at the heart of everything we do. We identify the user needs by exploring the problem area, then improve our service based on the user feedback and continually learn about our users throughout the whole service lifecycle.

Therefore, the DevOps culture can also be described as a culture of continuous improvement where the teams continually learn about their users, innovate and evolve through creating new ideas and taking daily efforts to make things better, faster and more efficient.

The Build – Measure – Learn feedback loop is a very common practice through which the teams build a solution, measure the results based on data and learn based on the user feedback. This cycle can be repeated and applied to new ideas, experiments or product developments. Therefore, the ultimate goal is to deliver value to our customer, our customer’s customer and keep up with their changing needs through adopting DevOps practices.

The DevOps teams bridge the gap between Development and Operations as they work in small cross-functional teams to bring together all of the skills and capabilities needed to deliver DevOps solutions. They share tools and practices which include agile planning, agile software development (iterative and incremental development), continuous integration, continuous deployment, automated and continuous testing, and proactive monitoring of the production environment.

In order to identify what value you’re trying to deliver and what is your user’s biggest pain, many DevOps teams embed a UX professional (UX, product or service designer) into their cross-functional team. This is to ensure we solve the right problem and we understand the real user needs.

What is UX?

User experience (UX) is a scientific, psychological, and problem-solving side of product, experience, and service design. It focuses on having a deep understanding of users, what they need, what motivations and habits they have as well as limitations. It also takes into account the business strategy and business objectives with the purpose of delivering an end-to-end solution.

It is very important to understand your end user to make sure you deliver the service they need and that they’re going to use. UX helps you truly understand what your users need so that you know their needs early on, and you can decide to pivot or abandon the idea.

UX with DevOps!

When UX is integrated into the DevOps team, requirements are researched and designed, the solution is user-tested before the engineering team builds it. If the user or business stakeholder changes their mind, UX is a part of the process so the team can quickly adapt and change the direction of the service by implementing the user feedback.

On the contrary, when UX is not integrated into DevOps, the team receives the requirements from the client or the wider business and builds the solution straight away. Then, it gets to production and it’s delivered to the client who often says: ‘That’s not what I wanted!’

After the team receives the feedback, the new solution needs to be built and the whole cycle repeats until they get it right. As a result, a lot of time and money might be wasted as the delivery teams build the wrong thing or the user requirements change too many times – ultimately reducing speed to market.

If your DevOps team supports the internal development teams, their users are not only external customers but also development teams from other parts of the business who will use their DevOps solutions. The DevOps team’s goal is not to throw their work over the fence but to understand the developer’s needs and treat them as customers.

In this case, UX should be similarly integrated into the DevOps team. For example, I’ve been working with a DevOps team who have built different DevOps solutions, but the users preferred to use their own continuous integration / continuous deployment (CI/CD) pipelines and monitoring tools. As a result, none of the solutions have been used by the development teams. This means the user needs weren’t identified, and the problem area explored enough, to understand what solutions would have solved the user’s biggest problem and pain points.

‘What if we found ourselves building something that nobody wanted? In that case, what did it matter if we did it on time and on budget?’ Identifying your users and value is one of the main ‘lean’ principles Eric Ries talks about in his book Lean Start-up.

A common challenge for many large organisations is that their development teams may struggle with different parts of the development process and have different needs depending on their maturity, therefore different opportunities may arise.

Design Sprint Concept

Apart from traditional user research techniques (one-to-one user research interviews, workshops and observations), the DevOps teams can use a ‘design sprint’ concept as a powerful UX technique to identify developer needs for the CI/CD pipeline.

On my most recent enterprise project, the centralised DevOps team and I created user personas and decided to implement the design sprint approach because the level of cloud experience within the development teams varied significantly. This is to understand the user needs and business value, find commonalities, create a prototype and validate a few CI/CD pipeline design concepts with the users to get feedback. All work has happened during four intensive days of work with the purpose of reducing the risk of spending six months and delivering the wrong thing.

The results have been impressive and the benefits outstanding to the whole organisation. The Proof of Concept (PoC) has been proven to reduce a significant amount of time – from a project initiation phase, to deploy, to production – but more importantly, it’s greatly improved the user and stakeholder engagement.

Engagement & Needs

Engagement is particularly important if you want to stay up-to-date with the user needs. The user needs change all the time so if you want them to remain fresh and timely, engage with your users regularly, do ongoing user research and user testing throughout the whole service lifecycle (Discovery – Alpha – Beta – Live), and not just during discovery when the project is initiated.

When organisations embrace the DevOps culture, engagement is also crucial for organisational change as face-to-face communication increases across all teams and departments and organisational silos are starting to break down.

Another principle that allows you to stay closer to your users is a ‘lean’ principle – go see for yourself. If you wonder what your users may like or what they struggle with, go and talk to them directly. You’ll see the benefits of engaging and interacting directly with your users very quickly. Even though the users can’t see if your team applies agile or lean principles or how many sprints you need to build the service they need, they will definitely see the user experience.

While most organisations agree that DevOps focuses on driving business value, they’re challenged to move beyond the typical IT-focused metrics. Even though there are very good team IT metrics (cycle time, mean time to recovery, deployment frequency, fewer support tickets, reduced time to deploy a ‘change’ to production), it’s important to remember what business change our IT initiative is going to drive and how are we going to measure its success.

User-focused metrics such as user satisfaction surveys or a number of adopted DevOps solutions should give you a good understanding of whether your internal users (development teams) are delighted with your service within your organisation.

Similarly, reducing cost and time spent by each development team building their own tech stack or components through identifying repeatable patterns and building common solutions will bring the most value to your organisational metrics. Instead, the teams can spend more time on delivering value to their end users or customers.

Lastly, if you’re working on an end-to-end DevOps transformation programme and using a DevOps framework assessing your organisation’s or team’s DevOps maturity, try to apply a variety of DevOps metrics within technology, people and process capability to adequately measure the DevOps transformation success.

That maturity, however, comes with a caveat: the threat landscape has shifted. A series of high-profile data breaches in recent years has pushed security to the top of every boardroom agenda, and regulatory frameworks such as the EU’s General Data Protection Regulation (GDPR) have introduced real legal consequences for organisations that fail to protect their data. Security is no longer optional — and neither is compliance.

The Security Challenge Specific to Cloud Environments

For development and operations teams, the cloud’s flexibility and speed are clear advantages. For security teams, those same qualities introduce a new class of problems. Traditional security practice involved carefully controlled, relatively static environments. In the cloud, infrastructure can be spun up, modified, and torn down within minutes — often without the security team’s direct involvement.

The tight integration between cloud platforms and application code means that security requirements can no longer be bolted on at the end of a project. They must be incorporated into the code and infrastructure definitions themselves. When cloud platform configurations can change daily, the window for misconfiguration to go undetected is wide open.

This risk is well-recognised in the industry. Gartner has projected that by 2020, the majority of cloud security incidents would be attributable to customer misconfiguration, mismanaged credentials, or insider threats — not to vulnerabilities in the cloud provider’s own infrastructure. The implication is clear: the responsibility for securing cloud environments lies overwhelmingly with the organisations that use them.

Translating Security Policy into Developer Workflow

A growing category of cloud security posture management (CSPM) tools addresses this problem by bridging the gap between security policy and development practice. These tools allow security teams to define their compliance requirements — mapped to standards such as PCI-DSS, CIS Benchmarks, NIST, or HIPAA — and then surface violations to development teams in an actionable, concise format.

The practical benefit is significant. When security guardrails are codified and enforced automatically, developers no longer need to manually cross-reference pages of written policy every time they make a change to cloud infrastructure. They work within well-defined boundaries, reducing the risk of misinterpretation and human error while freeing them to focus on building features rather than auditing configurations.

A Practical Continuous Security Workflow

The following workflow illustrates how continuous cloud security can be integrated into the development lifecycle in a structured, repeatable way. It is based on a real-world implementation built to address gaps found in existing multi-cloud security tooling:

• Deployment: Stand up a new cloud subscription or account (for example, an Azure subscription or an AWS account).
• Permission: Create the necessary IAM controls and grant the security team the access they need to configure the security inspection tool for that new environment.
• Definition: The security team defines or applies the compliance policies that the cloud environment must meet. These policies can be updated as the environment grows or as compliance requirements evolve.
• Building: Development teams begin deploying cloud resources into the environment. For early-stage deployments, developers can focus on functionality, knowing that security compliance will be evaluated in real time by the tooling.
• Consumption: Query the security tool via API to identify exactly where a deployment fails to meet the defined security policy. If it passes, the tool generates a report to serve as evidence of compliance.
• Remediation: Where violations are found, fixes can be applied programmatically. A corrected infrastructure template can be pulled back into the repository, or the API can return the necessary JSON for independent integration. GUI-based alert review and remediation is also available for teams that prefer it.
• Reporting: As the project progresses, compliance reports are shared with stakeholders. These reports support risk analysis and inform sprint planning by clearly identifying which areas carry the greatest security risk.

Security Visibility Across the Entire Delivery Pipeline

Embedding security checks throughout the integration cycle — rather than only at the end — means that risk can be surfaced and addressed at the most appropriate stage. Early in the pipeline, the tooling can flag risks in smoke testing. In UAT, quick remediations can be applied. In staging, deployments can be validated before they reach production. Once in production, the same tooling can be handed over to operations teams to monitor for manual configuration changes or for configuration drift over time.

For security teams, continuous cloud security tooling provides an immediate, centralised view of the security posture across all cloud environments — including short-lived environments that might otherwise go unmonitored. With full visibility of the infrastructure and how its components interconnect, security teams can identify weak points proactively rather than reactively.

The audit trail is equally valuable. Every configuration change can be monitored in real time, with alerts routed to communication and logging platforms such as Slack, Sumo Logic, or email whenever a resource falls out of compliance. This creates a continuous, auditable record of security posture — something that is increasingly required for regulatory compliance and internal governance alike.

Why Continuous Cloud Security Benefits Both Security and Development Teams

The traditional model — where security reviews happen at the end of a development cycle — is poorly suited to the pace of cloud-native development. Continuous security integrates compliance into the cloud lifecycle itself, allowing security and development teams to work in parallel rather than in sequence.

Security teams gain the tooling to define, enforce, and audit policy at scale. Development teams gain clear guardrails and automated feedback that let them move quickly without inadvertently introducing compliance risk. Both teams operate on the same infrastructure, using the methods best suited to their respective roles.

As cloud adoption continues to deepen in regulated industries, the organisations that treat continuous security as a first-class part of their developer journey — not an afterthought — will be better positioned to move fast, stay compliant, and respond to an evolving threat landscape.

Originally sourced from cloudcomputing-news.net (2019).