What is cloud native?
Cloud native is a modern approach to building and running applications that fully exploits the advantages of cloud computing, including on-demand scalability, resilience, and automation. This matters because it lets organizations ship features faster, recover from failures automatically, and scale individual services independently instead of redeploying an entire application.
The CNCF's official definition frames cloud native as loosely coupled systems, combined with robust automation, that allow engineers to make high-impact changes frequently and predictably with minimal toil. It is not a single product or technology. It is a combination of architectural patterns like microservices and immutable infrastructure, development practices like CI/CD and DevOps, and operational models like declarative configuration and observability, all working together.
Cloud native applications can run across public, private, and hybrid cloud environments because they are built on portable abstractions like containers and orchestration platforms. But the same properties that make cloud native powerful, including ephemeral workloads, distributed services, and rapid deployment, also create new security challenges that traditional tools designed for static infrastructure were never built to handle.
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What are the 4 pillars of cloud native?
The four pillars of cloud native are microservices, containers, CI/CD, and DevOps. Together, these practices let teams build loosely coupled software, package it consistently, deploy it automatically, and operate it collaboratively.
Microservices: Microservices split an application into smaller services aligned to business capabilities such as billing, search, or user authentication. This design lets teams deploy one service without redeploying the entire application, which improves release speed and fault isolation.
Containers: Containers package code, libraries, and runtime dependencies into a portable unit that behaves consistently across laptops, CI pipelines, and production clusters. Technologies such as Docker, containerd, and OCI images make this portability practical.
CI/CD: Continuous integration and continuous delivery automate code build, test, security scanning, and deployment. CI/CD reduces manual handoffs and helps teams ship changes frequently with controls such as unit tests, canary releases, image signing, and IaC validation.
DevOps: DevOps gives development and operations teams shared ownership of reliability, performance, and security. In cloud native environments, DevOps shows up through automation, observability, incident response, and Git-based workflows that keep delivery fast without losing control.
These pillars are interdependent. Containers without CI/CD still create manual bottlenecks. Microservices without DevOps culture create organizational friction where teams throw code over the wall. The value comes from adopting them together as a system, not picking one in isolation.
What is the CNCF?
The Cloud Native Computing Foundation (CNCF) is a vendor-neutral foundation, part of the Linux Foundation, that hosts and governs the most widely adopted cloud native open-source projects. Key graduated projects include Kubernetes, Prometheus, Envoy, containerd, and Helm.
The CNCF Cloud Native Landscape serves as a reference map for the ecosystem, cataloging hundreds of projects and tools across categories from orchestration to observability to security. If you are evaluating cloud native technologies, the landscape is a useful starting point for understanding the breadth of available tooling.
How does cloud native architecture work?
Cloud native architecture breaks applications into small, independent components that are packaged in containers, orchestrated automatically, and connected through APIs. Each component solves a specific problem, and together they form a system that can scale, heal, and deploy independently.
Microservices
Microservices are small, independently deployable services, each responsible for a specific business capability. They replace monolithic applications where a single codebase handles everything, meaning teams can build, test, and deploy their service without waiting on other teams.
The tradeoff is real, though. Individual services are simpler, but coordinating dozens or hundreds of them introduces complexity around communication, data consistency, debugging, and security considerations unique to microservices architectures. A single user request might touch ten services before returning a response, and tracing a failure across that chain requires dedicated tooling. Understanding the security posture of each service also requires cross-service visibility, because permissions, network paths, exposed APIs, and downstream data access can turn one weak link into a broader attack path.
Containers
A container is a lightweight, portable unit that packages an application and all its dependencies, including libraries, runtime, and config files, together. Docker is the most widely recognized container platform. In modern cloud native environments, Docker commonly handles image build and developer workflows, while runtimes such as containerd and runc execute containers in production. With 91% of organizations using containers in production, containers run consistently whether on a developer's laptop, in a CI/CD pipeline, or across any cloud provider.
Containers matter because they start in milliseconds, use resources far more efficiently than virtual machines, and let you run many workloads on a single host. They follow the Open Container Initiative (OCI) standard, which keeps them portable across different runtimes and platforms.
Kubernetes and orchestration
Kubernetes is the open-source orchestration platform that manages containerized workloads at scale, handling scheduling, scaling, networking, and self-healing. In practice, when a container crashes, Kubernetes restarts it. When demand spikes, it scales up new replicas. When you deploy a new version, it rolls it out gradually.
Kubernetes is the de facto orchestration standard for containerized workloads and is widely used in production across enterprise environments. It automates scheduling, scaling, service discovery, and self-healing, which is why it has become central to many cloud native platforms. However, it is one component of cloud native, not a synonym for it. You can run a monolithic application in a single container on Kubernetes, which uses Kubernetes but is not cloud native.
Service mesh and APIs
A service mesh is an infrastructure layer that manages communication between microservices, handling load balancing, encryption, authentication, and observability without requiring changes to application code. Istio and Envoy are common examples. APIs are the interfaces through which services expose functionality to each other and to external consumers.
In cloud native environments, east-west traffic (service-to-service communication within the cluster) often far exceeds north-south traffic (external requests coming in). This makes internal communication patterns a critical design and security concern, since a compromised service can move laterally across the mesh if policies are not enforced.
Immutable infrastructure and declarative configuration
With immutable infrastructure, instead of patching or updating running servers, you replace them entirely with new instances built from a known-good template. This eliminates configuration drift, where a server's actual state slowly diverges from its intended state over time.
Declarative configuration works alongside this pattern. You describe the desired state of your infrastructure in code, for example "three replicas of this service behind a load balancer," and the platform converges to that state automatically. Tools like Terraform, AWS CloudFormation, and Pulumi make this possible by defining infrastructure in version-controlled code that teams can review, test, and deploy consistently across environments. GitOps workflows use Git repositories as the single source of truth for infrastructure state.
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What are the benefits of cloud native?
Cloud native does not just change how you build software. It changes how fast you can respond to customers, how efficiently you use infrastructure, and how resilient your systems are under failure.
Faster release cycles without sacrificing reliability: CI/CD pipelines, containers, and microservices let teams deploy independently and frequently. Cloud native organizations commonly deploy multiple times per day versus monthly or quarterly release cycles in traditional environments. Automated testing and canary deployments built into pipelines catch issues before they reach users, so speed does not come at the cost of stability.
Scales with demand automatically: Kubernetes can add or remove container replicas based on CPU, memory, or custom metrics in seconds. Compare this to traditional capacity planning, where you provision servers for peak load and waste resources the rest of the time. Cloud native flips this model: you pay for what you use, and scaling happens automatically rather than through weeks of procurement.
Reduces infrastructure lock-in: Because your application runs in containers orchestrated by Kubernetes, it can run on AWS, Azure, GCP, or on-premises clusters with minimal modification. This portability gives organizations leverage in vendor negotiations and the flexibility to adopt multi-cloud strategies.
Improves fault isolation and resilience: If one microservice crashes, it does not bring down the entire application. Kubernetes automatically restarts failed containers and redistributes workloads across healthy nodes. Patterns like circuit breakers and retries help cloud native applications handle failures gracefully, degrading individual features instead of taking down the whole system.
Cloud native development practices
Cloud native architecture defines the building blocks, but development practices determine how fast and safely teams can ship changes using those blocks.
CI/CD pipelines
Continuous integration means automatically building and testing code on every commit. Continuous delivery means automatically deploying validated code to staging or production. A code commit triggers an automated sequence of build, test, security scan, and deploy steps, and this is what turns cloud native architecture into actual development velocity.
CI/CD pipelines are also where security checks like image scanning, IaC validation, and secret detection should be integrated. Catching a misconfigured Terraform template in a pull request is far cheaper than finding the exposed S3 bucket it creates in production.
DevOps and GitOps
DevOps is the cultural and operational model where development and operations teams share ownership of building, deploying, and running software. GitOps takes this further by using Git repositories as the single source of truth for both application code and infrastructure configuration. Changes happen through pull requests, and automated controllers reconcile the live environment to match the declared state in Git.
Pull requests become the change management and audit trail for infrastructure changes, replacing manual ticketing systems with a reviewable, version-controlled workflow.
Serverless computing
Serverless computing is a cloud execution model in which the provider manages most infrastructure operations for you. Function as a Service (FaaS) is the most common serverless pattern, but the broader serverless category also includes services such as serverless databases, event buses, and managed application backends.
Serverless sits alongside containers as a cloud native compute option, not a replacement. Some workloads fit serverless well, like event-driven and short-lived tasks. Others need the control that containers and Kubernetes offer. The tradeoff is that serverless simplifies operations but introduces constraints around execution duration, cold starts, and vendor-specific APIs that can increase lock-in.
Challenges of cloud native adoption
Cloud native delivers significant advantages, but the transition introduces real challenges that teams should plan for.
Distributed system complexity: Debugging a request that flows through a dozen microservices is harder than tracing an error in a monolith. Teams need investment in distributed tracing, centralized logging, and observability tooling.
Skills and cultural shift: Cloud native requires developers to own operational concerns, operations teams to embrace automation, and both to collaborate through shared tooling and processes. Without that cultural change, you get distributed complexity instead of distributed resilience.
Security surface expansion: More components mean more container images to scan, more service accounts to manage, more network policies to configure, and more dependencies to track for vulnerabilities. Without a unified view that connects vulnerabilities, identities, network exposure, and data access, teams end up triaging thousands of isolated findings instead of focusing on the risks that are actually exploitable. A comprehensive defense in depth strategy is essential. The Wiz Research team found critical vulnerabilities in Ingress NGINX that affected a large percentage of Kubernetes clusters, illustrating how a single misconfigured component can expose thousands of environments at once.
Cost management: Auto-scaling and microservices can increase cloud spend when teams lack governance, rightsizing, and cost visibility. Cloud native teams need cost observability alongside security observability so they can catch idle workloads, overprovisioned clusters, and inefficient scaling policies before waste accumulates.
Container and image sprawl: Organizations running at scale can accumulate hundreds or thousands of container images. Keeping base images updated, removing stale images, and tracking dependency drift across all of them is an ongoing operational challenge.
Securing cloud native environments with Wiz
Cloud native environments demand security that operates at the same speed and scale as the applications running on them. Wiz was built for this reality.
Wiz connects to cloud environments through cloud provider APIs, covering the full spectrum of cloud native workloads, including containers, Kubernetes clusters, serverless functions, managed PaaS services, and AI workloads, without deploying agents, sidecars, or any runtime components that impact workload performance. This agentless approach means full coverage from day one, with no blind spots from workloads that lack an installed agent.
Once connected, the Wiz Security Graph maps the relationships between resources, identities, configurations, network paths, and data stores to show how risks actually connect. A vulnerability in a container image is assessed in the context of whether that container runs with a privileged service account, has network access to sensitive data, and is reachable from the internet. This turns thousands of isolated findings into a small set of risks that are actually exploitable.
For teams building CI/CD pipelines, Wiz Code extends the same contextual analysis into code repositories and build pipelines, connecting vulnerabilities and misconfigurations found in code to their runtime impact in the cloud. Developers see the issues that matter, mapped to their code and repositories, so remediation happens at the source. Cribl, a fast-growing cloud-native organization, consolidated multiple security use cases into Wiz, reducing identification and remediation time from days to minutes while deploying the Wiz Runtime Sensor in its Kubernetes clusters for real-time threat detection.
As organizations extend cloud native patterns to AI workloads like training pipelines, inference services, and connected data stores, Wiz applies the same contextual risk model to AI infrastructure, treating AI security as part of the broader cloud native security posture rather than a separate problem.
Ready to see how Wiz maps risk across your cloud native environment? Get a demo to see the Security Graph in action.
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