 is right around the corner, and there are quite a lot of changes to unpack! Removing enhancements with the status of “Deferred” or “Removed from Milestone” we have 64 Enhancements in all listed within the 

Kubernetes 1.33 brings a whole bunch of useful enhancements, including 35 changes tracked as ‘

’ in this Kubernetes release. From these, 

 are graduating to stable, including the ability to specify a new 

 commands like get, patch, edit and replace commands, which provides a new way to allow fetching and updating of subresources like status, scale, and more.

 features are also listed in the enhancements tracker, with a bit of conversation at Kubecon Europe around the ability to implement declarative validation of Kubernetes native types using 

 to generate validation code. Amongst other benefits, DevOps teams will benefit from streaming encoding specifically for LIST response. This enhancement significantly reduces memory consumption on the API Server when serving large LIST responses.

The Cloudsmith team is really excited about this release and everything that comes with it! Let’s jump into all of the major features and changes in Kubernetes 1.33.

Here are a few of the changes that Cloudsmith employees are most exciting for in this release:

The current approach to deleting resources within a Kubernetes namespace is somewhat flawed, especially in environments that demand strict security controls. Relying on a semi-random deletion order introduces unnecessary risk. One particularly dangerous scenario is when a Pod outlives its associated NetworkPolicy, effectively stripping it of its intended network protections and leaving it wide open to unrestricted access. This creates a serious risk.

To address this, the Kubernetes maintainers proposed a deliberate and opinionated deletion strategy that prioritises security by enforcing a logical order: Pods should be deleted before their dependent resources, like NetworkPolicies. This isn’t just about order, it’s about responsibility. By acknowledging some real-world implications of resource dependencies and acting accordingly, you can dramatically reduce the chance of exposing workloads during namespace teardown.

Nigel Douglas – Head of Developer Relations

What excites me most is the long-anticipated improvement around support for user-specific namespaces. This feature would allow our teams to better isolate security-sensitive elements such as user and group IDs, root directories, capabilities, and encryption keys. User namespaces offer a way to differentiate identity and permissions inside a container from those on the host system. We’re looking to avoid scenarios where a process could operate with full privileges (user ID 0) within its namespace while remaining an unprivileged user outside of it.

Bringing user namespace support to Kubernetes would enable pods to run with distinct user and group IDs compared to the host. This means a process that appears privileged inside the container is effectively limited in scope if it escapes to the host, reducing our risk of a system-wide compromise. It's a powerful security boundary that can help contain potential breaches and enforce least privilege at runtime.

I'm especially pleased to see the introduction of in-place updates for Pod resource requests and limits, eliminating the need to restart Pods or their Containers. Adjusting resource allocations can become necessary for a number of reasons, whether due to a spike in workload, under-utilisation of assigned resources, or simply because the initial settings were misconfigured.

Until now, modifying a Pod's resource configuration required a full restart, as the resource fields in the PodSpec are immutable. While this may be manageable for resilient, stateless applications, it poses a challenge for workloads that are stateful, batch-oriented, or run with minimal replicas. In such cases, restarting Pods can lead to unnecessary downtime or increased operational costs. The ability to adjust resources dynamically, without disruption, is a valuable improvement that directly improves availability.

It’s important to note, however, that while these resource values can now be updated in place, the Pod’s Quality of Service (QoS) class remains immutable. This is a key consideration because the QoS classes (

) - is determined at Pod creation based on the presence and nature of resource requests and limits. Since the QoS class influences how Kubernetes schedules and evicts Pods under resource pressure, it’s crucial to set these values thoughtfully at the outset.

For example, you might increase a Pod’s CPU and memory limits significantly using an in-place update, but if the Pod was initially classified as 

, Kubernetes may still deem it a lower priority and evict it during contention, even though it's now consuming a large share of resources. This kind of mismatch can lead to surprising and frustrating behaviour in production, which is why understanding the fixed nature of QoS classes is just as important as the flexibility offered by in-place updates.

In the current Kubernetes implementation, all indexes within an indexed job share a single backoff limit. This means if the overall job hits that limit, the entire job is marked as failed, even if some indexes haven't finished running. This model doesn't align well with embarrassingly parallel workloads, where each index operates independently. For example, using indexed jobs for a suite of long-running integration tests would halt all tests after a single failure, limiting visibility into other potential issues. In contrast, systems like 

 already treat each index separately, highlighting the need for a more flexible approach in Kubernetes.

To address this, the proposal introduces a per-index backoff policy, allowing each index to fail or succeed independently. This would let jobs continue running even if some indexes fail. It also suggests a new API field to control the number of allowed failed indexes, and a new FailIndex action within the 

 to proactively stop retries for specific indexes before hitting the backoff limit.

Some batch workloads, such as those using MPI or PyTorch, only rely on specific "

" indexes to determine whether a job has succeeded. However, the current Kubernetes behaviour requires all indexes in an indexed job to succeed before the job is marked as complete, which limits flexibility for these use cases. To address this, a proposed extension to the Job API introduces a configurable success policy, allowing users to define custom conditions under which an indexed job can be considered successful.

With this enhancement, a job can be marked as succeeded based on specific criteria - for example, if certain indexes [0, 1, 2] complete successfully, or if a minimum number of indexes succeed. Once the job meets the defined 

, any remaining pods are terminated, avoiding unnecessary resource consumption. This proposal does not alter the behaviour of jobs that don’t use a 

, nor does it apply to non-indexed jobs at this time, as those scenarios currently lack strong use cases. A new condition, 

, will indicate when a job satisfies its defined success policy.

Deployments in Kubernetes currently show inconsistent behaviour when dealing with terminating pods, depending on the rollout strategy or scaling activity. In some cases, it might be more efficient to wait for pods to fully terminate before creating new ones, while in others, launching new pods immediately is preferable. To address this inconsistency, a new field, 

, is proposed to give users control over when replacement pods should be created during deployment updates.

The goal is to let users define whether a Deployment should wait for pods to terminate before spinning up new ones or proceed without delay, all while respecting the chosen deployment strategy. Additionally, the status of Deployments and ReplicaSets would be enhanced to reflect the number of managed terminating pods. This distinction is important, as pods marked for deletion (with a 

 field. This can lead to temporary over-provisioning - especially during rollouts or unexpected deletions like evictions - which can cause resource strain, potential scheduling issues, and even unnecessary autoscaling, which also may increase cloud costs in large-scale or tightly resourced environments.

A key long-term goal for improving Kubernetes usability is to shift validation logic from controllers to the front-end. Currently, the handling of validation for unchanged fields presents a barrier for both Custom Resource Definition (

) authors and Kubernetes developers, preventing broader adoption of validation features. CRD authors face difficulties when modifying value validations, as they must increment the version even for minor changes, which can be cumbersome and disrupt the user workflow.

This proposal aims to eliminate barriers that hinder CRD authors and Kubernetes from adjusting value validations. By removing these obstacles, CRD authors can more easily widen or tighten value validations without fear of breaking workflows. The goal is to automate this process for all CRDs installed into clusters with the feature enabled, ensuring minimal overhead-negligible persistent overhead and up to a 5% time overhead for resource writes. The approach also emphasises correctness by ensuring that invalid values, which would fail a known schema, are not allowed.


This proposal introduces a more secure leader election mechanism for component upgrades and rollbacks, primarily leveraging leases with two significant changes. First, instead of competing for the lease, component instances declare their candidacy, and an election coordinator selects the best candidate by choosing the one with the lowest version, ensuring skew rules are upheld. Second, the election coordinator can mark a lease as "

" which prompts the current leader to stop renewing the lease and allows the coordinator to replace it with a better-suited leader. This approach addresses the issues that arise during node-by-node upgrades and rollbacks in systems like Kubernetes, Cluster API, kubeadm, and KIND, which often lead to skew violations due to timing inconsistencies in component upgrades.

The proposed mechanism aims to resolve challenges such as the potential for skew violations when a new version of a controller runs while old versions of the API server are still active. It also prevents lease loss during upgrades or rollbacks, which can cause component version flip-flopping. The goal is to enable more predictable version changes, avoid skew violations, and ensure a smoother upgrade process while allowing for canary deployments or pauses during upgrades. This enhancement, when combined with other tools like 

, improves the ability to manage control plane components during upgrades, rollbacks, and downgrades. The proposed solution offers an opt-in mechanism to elect the oldest version candidate and preempt the current leader, reusing the existing lease structure as much as possible.


This proposal introduces an opinionated deletion process for Kubernetes 

 to ensure secure and predictable removal of resources. Currently, the deletion order is semi-random, which can result in security gaps, such as Pods remaining active after their associated NetworkPolicies have been deleted. By implementing a more structured deletion order, Pods will be deleted before other resources based on logical and security dependencies, improving both security and reliability. This change addresses risks caused by non-deterministic deletion and ensures the safe termination of resources in a more controlled manner.

The current random deletion process poses significant challenges, especially in security-sensitive environments. For example, deleting a 

 before its associated Pod can expose the Pod to unrestricted network access, creating a security vulnerability. The proposed opinionated deletion process aims to enhance security by ensuring that resources like NetworkPolicies remain active until all dependent resources are properly terminated. Additionally, it provides a more predictable and consistent cleanup process, reducing operational disruptions. The proposal also maintains compatibility with existing Kubernetes workflows and APIs while ensuring safe and reliable resource cleanup.

Currently, working with sub-resources such as 

 in Kubernetes through kubectl is a cumbersome process. Users must rely on 

 to fetch sub-resources, and updating them (like patching the 

) requires resorting to direct curl calls against the API server. This workflow is far from intuitive and creates unnecessary friction for developers and operators when testing, debugging, or interacting with the Kubernetes API.

To improve this experience, a proposed enhancement introduces a new 

. This change makes subresources like status, scale, and resize accessible and manageable across all supported resource types, including custom resources. When using the new flag, subresource outputs will be formatted consistently with the main resource view, ensuring clarity and usability. It's important to note that the subresource API behaves the same as the full resource, so changes, like updating the status, may still be subject to reconciliation by the corresponding controller.


 Implement a .kuberc file to separate user preferences from cluster configs


 phase, this feature will be enabled through the 

. Users can override this location using the 

). The kuberc file will serve as an optional configuration to separate user preferences from cluster credentials and server configurations. This proposal aims to introduce a new file dedicated to user preferences, which will offer better flexibility compared to 

, which currently under-utilises its preferences field due to the creation of new files for each cluster that mix credentials with preferences.

The kuberc file will provide a clear separation between server configurations and user preferences, allowing users to define command aliases, default flags, and other customisations. The file will be versioned to support easy future updates, and users will be able to maintain a single preferences file, regardless of the 

 environment variable. This proposal also suggests deprecating the kubeconfig preferences field to streamline configuration management. The kuberc file will be entirely optional, providing users with an opt-in way for custom kubectl behaviour without impacting any existing setups.

This enhancement introduces the ability for users to dynamically expand the pool of IP addresses available for Kubernetes Services. Services, particularly those of type 

), which must be unique throughout the cluster. Currently, if a user tries to assign a ClusterIP that's already in use, the operation fails. The existing IP allocation mechanism has notable limitations - most critically, the inability to resize or extend the range of IPs. This creates challenges when IP exhaustion occurs or when network ranges overlap in your cluster.

To resolve these issues, a new allocation mechanism is being proposed that is scalable, tunable, and fully backward compatible. This system introduces two new API resources: 

. By enabling the creation of additional ServiceCIDR objects, users can effectively increase the available IP space for Services on the fly. The updated allocator intelligently draws from any defined ServiceCIDR, functioning similarly to how adding more disks expands capacity in a storage system.


 field to the Kubernetes Service spec, intended to replace the now-deprecated

 annotation. These earlier mechanisms aimed to influence service routing based on topology but had notable limitations. 

, for example, offered rigid, user-defined routing preferences without allowing for dynamic or feedback-based optimisations, and its flexibility often made implementation difficult or impractical.

Subsequent attempts to improve on this, like 

, introduced more intelligent and flexible routing via implementation-specific heuristics and node-local traffic control. However, they lacked predictability and granular user control, especially for scenarios like preferring node-local traffic with fallback to zone or region. The new 

 field addresses these gaps by aiming to balance user intent with implementation flexibility. It introduces a standardised way for users to influence traffic routing behaviour while giving implementers the freedom to support advanced or evolving strategies, such as routing based on topology, latency, or custom heuristics.


As of February 2025, the scope of this Kubernetes Enhancement Proposal (KEP) has been significantly reduced. Initially, it aimed to graduate both the hints field in 

 and a topology-aware routing mechanism using the 

 annotation. However, only the hints field is moving to General Availability (GA) in this release. While the routing-related features and annotations remain out of scope for GA, they are still documented in the 

 KEP for historical context and to clarify the rationale behind the hints implementation. Portions of the original proposal, particularly those related to production readiness, remain relevant as other components may graduate separately under different KEPs. (see: 

 mechanism stems from the growing deployment of Kubernetes clusters across multiple zones. In such environments, traffic is often distributed randomly among service endpoints, leading to unnecessary cross-zone communication. This proposal introduces a lightweight, automatic method for topology-aware routing by allowing 

 producers to suggest which endpoints consumers (like 

) should use. The goal is to favour in-zone traffic when possible, offering benefits such as reduced latency, improved performance, and lower cross-zone data transfer costs. The design emphasises simplicity, relying on existing topology labels and requiring minimal user configuration to influence traffic locality.

 Define ClusterTrustBundle, a resource for holding X.509 trust anchors


Kubernetes currently allows workloads to request certificates via the 

 API, but lacks a standardised way for signers to distribute their trust anchors to workloads. Trust anchors, which are typically root or intermediate 

, are essential for validating certificate chains, and are often context-specific. To address this gap, this KEP introduces a new cluster-scoped resource called 

, a specialised object for storing and sharing trust anchors. These bundles are optionally linked to a specific certificate signer using 

, with RBAC controls governing their mutation. To make these trust bundles consumable by workloads, a new Kubelet 

 projected volume source allows pods to mount trust anchor sets as files that automatically update as the bundle changes. This new system is designed to eventually replace the legacy 

A practical example of this is distributing trust anchors from a private CA to workloads. Suppose a custom signer 

 issues certificates to a server pod. A client pod can consume the related trust anchors by mounting a 

The client pod references this bundle in its volume configuration:

Kubelet merges all relevant trust bundles into 

, ensuring the client always has up-to-date trust anchors (even through rotations) enabling seamless and secure communication with the server.


 Bound service account token improvements


Token projection and alternative audiences on 

 issued by the Kubernetes apiserver enable external systems to verify the identity and attributes (like an associated ServiceAccount or Pod) of a requesting entity. However, it's currently not possible to confirm a Pod's association with a specific Node solely from the token without fetching the Pod object and cross-referencing its 

. To enhance the integrity of identity verification, especially in cases aiming to prevent token replay attacks, embedding the identity of the Node into the JWT would allow external systems to validate that the Pod and its projected token originate from the same trusted Node.

This enhancement involves adding the Node reference as a private claim in JWTs issued by the 

, similar to how ServiceAccounts, Pods, and Secrets are currently handled. It also introduces support for directly binding tokens to Node objects, enabling more precise verification and improved auditability. Additionally, unique identifiers for tokens will be embedded to allow traceability in audit logs, linking token usage to its origin. These improvements aim to strengthen the trust chain from the original requester through to the JWT, making token validation more secure and verifiable across distributed systems.

 API for external signing of Service Account tokens


Kubernetes currently relies on service account keys loaded from disk at 

 startup for JWT signing and authentication. This static method limits key rotation flexibility and poses security risks if signing materials are exposed via disk access. To address these limitations, a proposal suggests enabling integration with external key management systems (such as 

). This would allow service account JWT signing to occur out-of-process without restarting the kube-apiserver, improving both ease of rotation and security.

) to support signing and key management externally, similar to Kubernetes’ existing KMS API model. It maintains backward compatibility by preserving current file-based behavior unless a new signing endpoint is explicitly configured. The key goals for this update include supporting public key listing, ensuring token compatibility with existing standards, and avoiding performance regression for in-process keys.

To enhance security and consistency on your nodes, this proposal aims to ensure that read-only volumes in Kubernetes are recursively read-only. Currently, mounting a directory like 

 as read-only does not enforce the same restriction on its submounts (for example 

), potentially exposing data to unintended modifications. By leveraging the OCI Runtime's 

 (recursive read-only) bind mount option, this limitation can be addressed. This option uses 

 flags to enforce recursive immutability. However, this requires Linux kernel version 5.12 or later, along with compatible OCI runtimes such as 

 struct in the pod spec, enabling fine-grained control with options like 

. This setting allows users to request recursive read-only behaviour while maintaining compatibility with environments that may not support it.

Here’s how it would look in a pod manifest:

While this feature improves isolation, it introduces a broader API surface and may create a false sense of security if not properly implemented. To address this, it is suggested that Kubernetes reflect the actual recursive read-only status through 

 Add options to reject non SMT-aligned workloads


This proposal introduces enhancements to Kubernetes’ 

 aimed at improving performance predictability for latency-sensitive workloads on systems with Simultaneous Multithreading (

 policy in CPUManager already prevents virtual CPU sharing, it does not ensure exclusive use of entire physical cores. This can lead to performance degradation due to shared resources like caches when threads from different workloads are scheduled on the same core.

To address this, a new Kubelet configuration option, 

 can be set to ensure that workloads are assigned only full physical CPUs, avoiding partial core allocations. This approach strengthens workload isolation by preventing thread-level resource sharing and improving cache efficiency. The proposal emphasises tighter control over CPU allocation to better support the requirements of latency-sensitive applications.

 Built-in support for sidecar containers pattern


The sidecar container pattern has been part of Kubernetes since its early days, with a 

 clearly describing the concept. Over time, sidecars have become increasingly common and diverse in use, though current Kubernetes features don't always support them cleanly, often requiring awkward workarounds. To address this, a proposal has been introduced to enhance init containers with a 

 to designate them as sidecars. Unlike typical init containers, these would not block pod startup but instead run alongside other containers after their startup is confirmed via a startup probe or the completion of a 

Sidecar containers configured this way won't prevent pod completion; if the main containers finish, the sidecars will be terminated. Their 

 can override the pod-level policy, ensuring they can restart even if the pod itself is set not to. This is designed to support advanced patterns, like in the following example:

Additionally, sidecar containers will benefit from full lifecycle support, including 

 handlers, as well as all types of probes (

). Their readiness probes will even influence the overall pod readiness, further integrating them into core workload behaviour.

 Take toleration/taints into considering when computing skew


, nodes with taints that the Pod doesn’t tolerate, particularly the 

taint, should ideally be excluded from skew calculations. Ignoring such taints can cause scheduling issues, especially during node upgrades or replacements.

Nigel Douglas

Head of Developer Relations

Kubernetes 1.33 – What you need to know

: KubeCon London 2025 (KubeCon EU) showed a cloud-native ecosystem shifting from fragmentation to unification of policy, security, observability, and artifact management. Wasm is moving from hype to real-world use, OPA is expanding into FinOps, and signing everything (with SBOMs, attestations, and metadata) is becoming standard. Artifact registries are evolving fast, becoming unified platforms for governance, provenance, and automation across the software supply chain.

There’s something unmistakably electric about being at KubeCon in person. Maybe it’s the hallway chats, the animated late-night debates (helped along by the thump of House of Kube), or the shared momentum in the air – this year felt like a shift in the literal sense. Cloud-native isn’t just evolving; it’s maturing, solidifying, and meeting the pace of a world increasingly hungry for scale and simplicity.

, held at ExCeL London from April 1 to 4, brought together thousands of practitioners to explore the state of Kubernetes and the cloud-native stack. Several clear themes emerged across keynotes, breakout sessions, and late-night conversations.

 and cost optimization, and the ongoing reinvention of 

. Below, we’ve curated the significant highlights, with talk titles, speakers, and links to recordings and resources, plus our take on what they mean for the road ahead.

The conference vibe was exuberant and technically charged. CNCF celebrated its tenth anniversary of Kubernetes with a look forward: how to keep bridging cloud-native innovation with real-world production needs. The 

 introduced a fun twist on professional development: the 

 designation (and yes, now we want them too, at Cloudsmith).

This new CNCF program recognizes attendees completing 

all 13 CNCF certifications plus the LFCS Linux admin exam

, showcasing their cloud-native mastery. If you saw folks wearing unique Kubestronaut beanies or jackets, now you know why. The challenging economic climate also underscored a focus on 

: many hallway conversations compared notes on balancing cloud costs and complexity with on-prem solutions, highlighting that sometimes on-prem isn’t worth the trade-offs in agility.

 ~12,000+ attendees, 229 sessions, and countless community meetups and parties (don’t worry, the famous 

 party returned for those who could snag an invite, sponsored again, of course, by Cloudsmith, along with our friends at Humanitec, Tailscale, Sysdig, and Chainguard). Read on for our curated highlights of sessions relevant; we’ve organized the recap into thematic sections for easy scanning.

The Rise of Wasm in Cloud‑Native Applications

: Wasm is no longer experimental — it’s powering plugins, policy engines, and serverless runtimes in real deployments. Tools like wasmCloud and Spin are leading the way.

 emerged as a star of the show, signaling that 2025 is the year Wasm goes from promise to production in cloud-native systems. A standout session was 

"Was I Right or Wasm I Wrong? A Review of the Wasm Ecosystem”

 by Taylor Thomas (Cosmonic) and David Justice (Microsoft). Both are co-chairs of the CNCF TAG Runtime Wasm working group, and their talk gave a whirlwind tour of how Wasm has evolved from a browser technology to a 

portability, sandboxed security, and fast startup

, making it ideal for cloud and edge deployments. Custom ABIs hampered early server-side Wasm experiments, but the new 

 1.0 on the horizon) unlocks true interoperability. The talk highlighted a parade of projects leveraging Wasm today:

 – a CNCF project enabling a cloud-native Wasm application runtime

 by Fermyon – a developer tool for serverless Wasm apps

 – a universal policy engine using Wasm modules for Kubernetes admission control

 – demonstrating pluggable cluster scheduling logic in Wasm

 – extending proxies with Wasm filters (e.g.,

 – an OSS library running sandboxed functions blazingly fast via hypervisor protection

– we’re experimenting with Wasm via OPA-based policy enforcement, a use case we’re watching closely as these technologies converge.

: Wasm is no longer niche; it’s becoming a 

 for extensibility in many systems. Thomas and Justice’s candid discussion of the new 

 (with its strengths and weaknesses) helped the audience grasp 

 and what’s coming next. Keep an eye out for 

 with async support and eventually a stable WASI 1.0 (perhaps 2026) that promises no breaking changes.

 of Wasm content for those hungry for more on Wasm. 

 (Cosmonic) dived into wasmCloud’s road to standards in a talk cheekily titled 

“Wasm Whiplash: wasmCloud's Wild Ride To Standards.”

 And if you blinked, you might have missed a 

 by Joonas Bergius on mixing WebAssembly into Linkerd service mesh (

“Meshin’ With WebAssembly: Taking Linkerd Beyond Containers,”

 10 minutes of intriguing mashup). All these point to WebAssembly’s growing momentum: expect Wasm components powering plugins, microservices, and cloud functions in your architecture soon.

: Security was the dominant theme at KubeCon 2025. Signing, provenance, and secure-by-default registries were front and center. Tools like TUF, Notary, ORAS, and Kyverno are key to verifying that every artifact in your cluster is trusted.

 throughout KubeCon 2025. Nearly every keynote and many sessions hammered on safeguarding the software supply chain and production clusters. Multiple talks provided 

hands-on insights into tools and practices

 for artifact integrity, provenance, and vulnerability management.

“TUF-en Up Your Software Supply Chain”

 by Marina Moore (Edera) and Kairo de Araujo (Eclipse Foundation). This talk focused on 

, a CNCF graduate project aimed at secure software distribution (familiar to many via its use in PyPI, Docker Content Trust, and other systems). Moore and de Araujo highlighted how to distribute container images 

 their SBOMs and attestations in a tamper-evident way. They explained TUF’s core design (multi-signature trust, freshness checks, repository consistency guarantees, etc.) to ensure images and metadata remain current and unmodified in transit.

 for end-to-end verification, illustrating how an image’s SBOM and security attestations could be 

 as part of a secure CI/CD pipeline. The key takeaway is that 

As one of our team noted, the specifics of which signing framework you use (TUF, Cosign, Notary v2, etc.) may matter less than 

 and every organization must be proactive in defense.

Where and how you store signed artifacts matters, especially as OCI 1.1 enables registries to store and reference metadata like SBOMs and signatures reliably. Cloudsmith (like others in the ecosystem) is exploring how to surface this metadata securely and natively inside OCI workflows.

“Signed, Sealed, Delivered – Sign and Verify All the Things”

 by Jeremy Rickard (Microsoft). In this talk, Rickard tackled several scary scenarios (image tampering, unvetted YAML in prod, registry compromises) and then showed how to fight back using an arsenal of open-source tools. He wrote a story involving 

 to ensure every artifact in your cluster is trusted (

KubeCon + CloudNativeCon Europe 2025: Signed, Sealed, Delivered - Sign and Ver...

 – enabling OCI registry storage for arbitrary artifacts (not just container images, but also signatures, SBOMs, Helm charts, etc.)

 (v2) – signing artifacts and storing signature metadata in registries

 – GitOps operator (here, ensuring that only signed/approved configs make it to the cluster)

 – a policy engine that can validate images have known good signatures (at admission time)

Rickard walked through an end-to-end demo repository (promised on GitHub) showing how to set up 

production-grade signing and verification

. Attendees left with a clearer idea of how to put these pieces together to 

 were a common refrain across security talks this year.

Other security-related highlights included:

Project Lightning Talk: ORAS – Create and Distribute a Multi-platform Image with Security Posture

 by Feynman Zhou (Microsoft), covered a 5-minute demo of building a container image with SBOM and signing info as first-class OCI artifacts.

Project Lightning Talk: Protect your Kubernetes Clusters with Ratify and Attestations

 by Yi Zha (Microsoft) covered a quick introduction to

, a Kubernetes admission controller that checks image signatures/attestations before allowing deployments.

Project Lightning Talk: Notary – Securing Binary Artifacts with Fine-grained Control

 (also by Yi Zha), likely covering the latest on Notary v2 project status and its integration with tooling.

“Notary Project: The Key to Secure Software Supply Chain”

 by Joaquim Rocha and René Dudfield. A longer form talks about how Notary v2 is evolving as a cornerstone to securing artifacts in registries.

These trends emphasize why securing the registry is so important. Cloud-native platforms are expected to handle many formats, not just containers, but Helm charts, Wasm modules, and more, while maintaining integrity, provenance, and policy enforcement.

: Observability took center stage, pushing to unify metrics, logs, and traces via a standard query language and better tooling. Real-time insight is becoming essential for security, performance, and cost optimization.

Cloudsmith blog

Kubernetes 1.33 – What you need to know

KubeCon London 2025 Recap: Cloud-Native Insights on Security, Wasm, and More