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The Rising Importance of Governance at SwampUP Berlin 2025

On November 12-14, the Docker team was out in numbers at JFrog SwampUP Berlin 2025. We joined technical sessions, put on a fireside chat, and had conversations with attendees there. We’d like to thank the folks at JFrog for having us there and putting on such a great show!

Here’s our takeaways from the event about software supply chain security trends:

Software supply chain attacks reach unprecedented scale leveraging open source packages

An analysis of recent software supply chain attacks by JFrog’s CTO Asaf Karas shed light on how malicious actors leverage AI and software supply chains on their exploits. Recent attacks combine existing techniques, like phishing, in combination with AI prompts that recursively write and execute code in order to compromise hundreds of thousands of systems running popular open source packages. A few examples include Shai Hulud, Red Donkey, and the recent NPM package phishing attack. So far, despite these attacks’ scale, damages have been limited due to the still rudimentary nature of these exploits. Expect more software supply chain attacks as well as more sophistication in the coming year.

New Roles of Governance as a Security Layer

The best way to avoid software supply chain attacks is to not have malicious code entering software supply chains in the first place. That’s where governance comes into play. Taking control of gate points during the software development lifecycle, for example during dependency scanning, build pipelines, and deployments is not enough. It is necessary to block malicious or risky code before it enters the software supply chain. Not only that, but also tools need increased interoperability to detect all potential attack vectors.

Addressing MCP Challenges in AI Development

MCP’s ability to leverage both deterministic and non-deterministic outcomes by connecting an LLM client to many different servers seems to be the main reasons companies are betting on the technology to build applications that deliver value to customers. Moreover, because each server can run independently from one another, it becomes possible to add governance layers on MCP servers, reducing risks of hallucination or unexpected results. Overall, we agree with JFrog’s assessment and look forward to opportunities where Docker and JFrog MCP technologies can work together for a safer and smoother enterprise AI developer experience.

Building on Strong Open Source Foundations Is Core in the AI Era

The fireside chat between Gal Marder, JFrog’s Chief Strategy Officer, and Michael Donovan, Docker’s VP of Product, explored how organizations can protect themselves from risks in unverified open source dependencies. They emphasized the importance of starting with strong foundations: using hardened images, maintaining them throughout their lifecycle, including those that have reached end of life, and ensuring visibility and governance across every stage. Strong third-party integrations are essential to manage this complexity effectively and extend security and trust from development to delivery.

Conclusion: Build strong foundations, keep it consistent, stay ahead

Software development is changing fast as AI becomes part of everyone’s workflow, developers and attackers alike. The best way to stay ahead is to build protection early by starting with strong foundations and keep it consistent across every stage with governance, visibility, and strong partnerships. Only then can teams innovate with confidence and speed as the landscape evolves. Exciting times!

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EC2 Image Builder now supports auto-versioning and enhances Infrastructure as Code experience

Amazon EC2 Image Builder now supports automatic versioning for recipes and automatic build version incrementing for components, reducing the overhead of managing versions manually. This enables you to increment versions automatically and dynamically reference the latest compatible versions in your pipelines without manual updates. With automatic versioning, you no longer need to manually track and increment version numbers when creating new versions of your recipes. You can simply place a single ‘x’ placeholder in any position of the version number, and Image Builder detects the latest existing version and automatically increments that position. For components, Image Builder automatically increments the build version when you create a component with the same name and semantic version. When referencing resources in your configurations, wildcard patterns automatically resolve to the highest available version matching the specified pattern, ensuring your pipelines always use the latest versions. Auto-versioning is available in all AWS regions including AWS China (Beijing) Region, operated by Sinnet, AWS China (Ningxia) Region, operated by NWCD, and AWS GovCloud (US) Regions. You can get started from the EC2 Image Builder Console, CLI, API, CloudFormation, or CDK. Refer to documentation to learn more about recipes, components and semantic versioning.
Quelle: aws.amazon.com

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Announcing a Fully Managed Appium Endpoint for AWS Device Farm

AWS Device Farm enables mobile and web developers to test their apps using real mobile devices and desktop browsers. Starting today, you can connect to a fully managed Appium endpoint using only a few lines of code and run interactive tests on multiple physical devices directly from your IDE or local machine. This feature also seamlessly works with third-party tools such as Appium Inspector — both hosted and local versions — for all actions including element inspection.
Support for live video and log streaming enables you to get faster test feedback within your local workflow. It complements our existing server-side execution which gives you the scale and control to run secure enterprise-grade workloads. Taken together, Device Farm now offers you the ability to author, inspect, debug, test, and release mobile apps faster, whether from your IDE, AWS Console, or other environments.
To learn more, see Appium Testing in AWS Device Farm Developer Guide.
Quelle: aws.amazon.com

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AWS Payments Cryptography announces support for post-quantum cryptography to secure data in transit

Today, AWS Payments Cryptography announces support for hybrid post-quantum (PQ) TLS to secure API calls. With this launch, customers can future-proof transmissions of sensitive data and commands using ML-KEM post-quantum cryptography. Enterprises operating highly regulated workloads wish to reduce post-quantum risks from “harvest now, decrypt later”. Long-lived data-in-transit can be recorded today, then decrypted in the future when a sufficiently capable quantum computer becomes available. With today’s launch, AWS Payment Cryptography joins data protection services such as AWS Key Management Service (KMS) in addressing this concern by supporting PQ-TLS. To get started, simply ensure that your application depends on a version of AWS SDK or browser that supports PQ-TLS. For detailed guidance by language and platform, visit the PQ-TLS enablement documentation. Customers can also validate that ML-KEM was used to secure the TLS session for an API call by reviewing tlsDetails for the corresponding CloudTrail event in the console or a configured CloudTrail trail. These capabilities are generally available in all AWS Regions at no added cost. To get started with PQ-TLS and Payment Cyptography, see our post-quantum TLS guide. For more information about PQC at AWS, please see PQC shared responsibility.
Quelle: aws.amazon.com

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Amazon Athena for Apache Spark is now available in Amazon SageMaker notebooks

Amazon SageMaker now supports Amazon Athena for Apache Spark, bringing a new notebook experience and fast serverless Spark experience together within a unified workspace. Now, data engineers, analysts, and data scientists can easily query data, run Python code, develop jobs, train models, visualize data, and work with AI from one place, with no infrastructure to manage and second-level billing. Athena for Apache Spark scales in seconds to support any workload, from interactive queries to petabyte-scale jobs. Athena for Apache Spark now runs on Spark 3.5.6, the same high-performance Spark engine available across AWS, optimized for open table formats including Apache Iceberg and Delta Lake. It brings you new debugging features, real-time monitoring in the Spark UI, and secure interactive cluster communication through Spark Connect. As you use these capabilities to work with your data, Athena for Spark now enforces table-level access controls defined in AWS Lake Formation.
Athena for Apache Spark is now available with Amazon SageMaker notebooks in US East (Ohio), US East (N. Virginia), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Mumbai), Asia Pacific (Tokyo), Asia Pacific (Singapore), and Asia Pacific (Sydney). To learn more, visit Apache Spark engine version 3.5, read the AWS News Blog or visit Amazon SageMaker documentation. Visit the Getting Started guide to try it from Amazon SageMaker notebooks.
Quelle: aws.amazon.com

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Amazon EMR Serverless now supports Apache Spark 4.0.1 (preview)

Amazon EMR Serverless now supports Apache Spark 4.0.1 (preview). With Spark 4.0.1, you can build and maintain data pipelines more easily with ANSI SQL and VARIANT data types, strengthen compliance and governance frameworks with Apache Iceberg v3 table format, and deploy new real-time applications faster with enhanced streaming capabilities. This enables your teams to reduce technical debt and iterate more quickly, while ensuring data accuracy and consistency. With Spark 4.0.1, you can build data pipelines with standard ANSI SQL, making it accessible to a larger set of users who don’t know programming languages like Python or Scala. Spark 4.0.1 natively supports JSON and semi-structured data through VARIANT data types, providing flexibility for handling diverse data formats. You can strengthen compliance and governance through Apache Iceberg v3 table format, which provides transaction guarantees and tracks how your data changes over time, creating the audit trails you need for regulatory requirements. You can deploy real-time applications faster with improved streaming controls that let you manage complex stateful operations and monitor streaming jobs more easily. With this capability, you can support use cases like fraud detection and real-time personalization. Apache Spark 4.0.1 is available in preview in all regions where EMR Serverless is available, excluding China and AWS GovCloud (US) regions. To learn more about Apache Spark 4.0.1 on Amazon EMR, visit the Amazon EMR Serverless release notes, or get started by creating an EMR application with Spark 4.0.1 from the AWS Management Console.
Quelle: aws.amazon.com