AWS Transform for mainframe now delivers a traceable reimagine workflow

AWS Transform for mainframe now delivers a connected, traceable reimagine experience from assessment through code generation. Previously, modernizing mainframe applications required months of analysis across multiple tools for discovery, reverse engineering, and code generation with manual handoffs between phases. With this launch, enterprises running z/OS COBOL and PL/I workloads can assess their portfolio to identify the discrete business functions, extract business rules, generate development-ready requirements, and produce traceable cloud-native code in a single connected workflow.
The experience starts with a portfolio assessment, where AWS Transform systematically identifies and catalogs discrete business functions. Selected business functions flow directly into the reimagine workflow, creating a connected path from portfolio analysis through code generation. For each business function, AWS Transform generates development-ready requirements with full traceability, flowing directly into Kiro and other IDEs through MCP-based integrations. Teams can generate interactive documentation for any requirement or code directly in the IDE. Every requirement traces back to the source code, so teams can audit any transformation decision back to its origin. This end-to-end approach compresses what previously took years of manual effort into months of automated, evidence-based modernization.
These capabilities are available in all AWS Regions where AWS Transform for mainframe is available. For more information, see the AWS Region table.
To learn more, visit AWS Transform for mainframe or see the AWS Transform for mainframe documentation.
Quelle: aws.amazon.com

Amazon S3 Vectors now supports up to 10,000 similarity search results per query

Amazon S3 Vectors can now return up to 10,000 similarity search results per query, a 100x increase from the previous limit. The higher result limit helps you retrieve a larger, more comprehensive set of candidates during similarity queries. This is especially valuable for applications with multi-stage retrieval pipelines that need to apply additional processing such as reranking, aggregations, or deduplication to produce a more relevant final result set.
To get started with the higher limit, use the latest AWS SDK and update your application code to specify up to 10,000 relevant results (topK nearest neighbors) when making a QueryVectors API request. Query results are now returned across multiple pages, and you can start processing the first page immediately while retrieving additional pages as needed. For queries that return larger result sets, you pay a small data-returned fee based on the total size of results returned. The first 512 KB of data returned per query is free. For full pricing details, visit the S3 pricing page.
S3 Vectors supports retrieving up to 10,000 results per query in all AWS Regions where it is available. To learn more about S3 Vectors, visit the product page and S3 User Guide.
Quelle: aws.amazon.com

AWS Transform now supports model-to-model migration assessment for generative AI workloads

AWS Transform now offers a model-to-model migration custom transformation that assesses your generative AI workloads and produces a comprehensive migration plan for moving from third-party providers to Amazon Bedrock. The AI-powered agent scans your codebase, identifies every AI SDK and model in use, gathers your migration requirements through interactive questions, and maps models to Bedrock equivalents with transparent cost comparisons and production-ready code changes. This managed custom transformation helps organizations consolidate their AI workloads on AWS to gain IAM-based security, VPC endpoint isolation, prompt caching, Amazon Bedrock Guardrails, and unified operational tooling through Amazon CloudWatch.   The transformation supports migrations from OpenAI, Google Gemini, direct Anthropic SDK usage, and open-source models via LiteLLM or Ollama. It handles direct SDK integrations, framework-wrapped patterns such as LangChain and LlamaIndex, agentic architectures including CrewAI and LangGraph, and multi-provider routing layers — preserving your application architecture while swapping only the model layer. The agent includes intelligent cost optimization with tiered model routing recommendations, prompt caching analysis, and model lifecycle awareness that excludes models within 90 days of end-of-life from all recommendations. For some workloads, it recommends Amazon Bedrock’s OpenAI-compatible endpoints as a zero-code-change migration path.
AWS Transform model-to-model migration is available in all AWS Regions where AWS Transform is offered, at no additional charge beyond standard AWS Transform pricing. To get started, install the ATX CLI and run the mke-genai-model-migration custom transformation against your codebase. To learn more, see the AWS Transform Custom Transformations documentation and the announcement blog.
Quelle: aws.amazon.com

Amazon Bedrock Guardrails announces a new API targeting agentic AI workflows

Amazon Bedrock Guardrails now offers the InvokeGuardrailChecks API, a new resourceless API that lets you apply individual safeguards at any point in your agentic AI applications without creating guardrail resources. The API provides granular, per-request control over which safeguards to run at each step of your agent loop, returning numeric severity and confidence scores so you can implement custom thresholds and actions, whether to block, pass, retry, or log based on your specific requirements.
Agentic AI applications operate through iterative loops; planning tasks, calling tools, processing outputs, and iterating again while often executing dozens of steps for a single request. Each step carries a different risk profile, making a one-size-fits-all guardrail difficult to scale. The InvokeGuardrailChecks API addresses this by operating in detect-only mode with no guardrail IDs to track and no versions to manage. You specify which safeguards to run directly in each request, making it straightforward to add, remove, or adjust checks as your workflows evolve.
The API supports content filters (detecting harmful content across categories including hate, violence, sexual, insults, and misconduct), prompt attack detection (identifying jailbreak, prompt injection, and prompt leakage as independent standalone checks), and sensitive information filters (detecting supported PII entity types). Prompt attack detection is exposed as a separate safeguard, giving you the granularity to invoke each supported attack vector independently.
The InvokeGuardrailChecks API is available today in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (London), Europe (Stockholm), Asia Pacific (Tokyo), and Asia Pacific (Sydney).
To learn more, visit the Amazon Bedrock Guardrails technical documentation.
Quelle: aws.amazon.com

Docker Content Trust: Retirement and Migration Guidance

TLDR: Docker Content Trust (DCT) and the Notary v1 service at notary.docker.io are being fully retired (first announced in July of 2025). This blog explains what is changing, who is affected, and how to move to modern alternatives. 

Ten years ago, Docker Content Trust (DCT) gave the container ecosystem one of its first ways to verify the integrity and publisher of an image, built on The Update Framework and the Notary v1 project. The upstream Notary v1 codebase is no longer maintained, more modern signing tools have become the standard, and today a very small number (fewer than 0.05%) of Docker Hub pulls rely on DCT.

Last year we began retiring DCT for Docker Official Images, and now we’re completing that work by fully retiring DCT and the Notary v1 service at notary.docker.io. This post covers what’s changing, who’s affected (for most people, nothing), and the modern alternatives that are available to users.

Why are we retiring Docker Content Trust (DCT)?

DCT relies on the upstream Notary v1 server, the original TUF-based implementation that was first released in 2015, and the project is no longer maintained. In the years since, the ecosystem has standardized on OCI-native signing tools such as Sigstore/Cosign and the Notary Project’s Notation, that store signatures alongside the image in any compliant registry, with no separate trust infrastructure to run. The broader ecosystem has been retiring this approach–Microsoft deprecated DCT support in Azure Container Registry some time ago, and Harbor deprecated Notary v1 support too.

Retiring Notary v1 lets us put our investment behind other modern, standards-based tools (described below) that developers are already adopting, and behind making secure defaults first-class citizens on Docker Hub.

Who is affected by this change?

DCT was opt-in, and normal image pulls (docker pull) would not touch the Notary service, so if you’ve never deliberately turned it on, nothing about your workflow changes. You can stop reading here.

The change matters if you configured DCT for use, which usually shows up in one of a few ways:

You have DOCKER_CONTENT_TRUST=1 set in your environment, shell profile, CI pipeline, or Dockerfile.

Your scripts or automation use docker trust sign, docker trust inspect, or docker trust revoke.

Your Kubernetes admission controllers or deployment policies check for DCT signatures.

You publish images to Docker Hub with DCT signing enabled.

If you have never set DOCKER_CONTENT_TRUST and do not use docker trust commands, this change does not affect you. 

Pathway to retirement: timeline

We’re winding DCT down in stages rather than all at once. The brownouts are brief, scheduled outages, these are dry runs that flush out any pipeline still leaning on the service while there’s time to fix it. Writes go dark before reads, so signing breaks before verification and publishers can get the earliest heads-up.

Date

What happens

Jul 14, 2026

4-hour write brownout

Jul 15, 2026

4-hour write brownout 

Aug 10, 2026

4-hour read brownout 

Aug 12, 2026

4-hour read brownout 

Dec 8, 2026

Full shutdown

Windows run ~4 hours and begin at 8AM Pacific Time.

Note this only touches DCT trust operations; ordinary docker pull and docker push operations will keep working through these windows.

What to do if you are affected: migration guide and alternatives

If any of the cases above describe your setup, here’s how to move off DCT cleanly. The ecosystem has settled on a handful of strong, widely-adopted tools, so this is as much a menu as a manual. The steps run from the quickest unblock to the most complete setup; pick the leading technology that fits your workflow, and go as far down the list as your situation calls for.

Ensuring image pulls succeed

If your only goal is to ensure that image pulls keep working past the shutdown date, disable DCT. This is the fastest path to unblocking your workflows, but it removes tag-level verification. 

# Remove from your current shell session
unset DOCKER_CONTENT_TRUST

# Or explicitly disable it
export DOCKER_CONTENT_TRUST=0

Search your environment for anywhere this variable might be set, including shell profiles, CI/CD configuration, Compose files, and K8s manifests. Once DCT is disabled, all pulls continue to work normally.

Ensuring pulls are repeatable

Tags on image registries can change when an image is updated. Pulling by digest guarantees that you get the exact image content you expect, regardless of whether a tag has been moved or overwritten. Digests are immutable.

# Find the digest of an image you have pulled
docker pull busybox:latest
docker images –digests busybox
# REPOSITORY TAG DIGEST IMAGE ID
# busybox latest sha256:f85340bf… abc123def456

# Pull by digest
docker pull busybox@sha256:f85340bf…

Use digests in dockerfiles for reproducible builds, and in Kubernetes manifests or Compose files to ensure predictable deployments. 

Digest pinning verifies content integrity (you get exactly what you asked for), but it does not by itself prove publisher identity. For that, you need cryptographic signatures, which is where Sigstore/Cosign and Notation come in.

Proving publisher identity

Two mature, actively maintained signing projects have replaced DCT’s signing capabilities in Hub. Both store signatures alongside the image in OCI-compliant registries.

Option A: Sigstore / Cosign

Cosign is part of the Sigstore project and supports identity-based signing using short-lived certificates tied to an OIDC identity. It stores signatures as OCI artifacts in the same registry, alongside the image. 

Sigstore quickstart: https://docs.sigstore.dev/quickstart/quickstart-cosign/ 

Cosign on GitHub: https://github.com/sigstore/cosign 

Option B: Notation

Notation is the CLI for the Notary Project. It uses a certificate-based PKI model and stores signatures as OCI reference artifacts. 

Notation quickstart: https://notaryproject.dev/docs/quickstart/ 

Notation on GitHub: https://github.com/notaryproject/notation

Enforcing verification in production

Signing images is only half the story. To get full security benefits, you need to enforce that only signed images can be deployed.

Kyverno (works with Cosign)

Kyverno can verify Cosign signatures before pulling into a cluster. See the documentation for details. 

Ratify + Gatekeeper (works with Notation)

Ratify can verify Notation signatures before admitting pods. See the Ratify quickstart for setup instructions.

Use Docker Hardened Images as a built-in replacement

If you currently rely on DCT to verify base images from Docker Hub, switching to Docker Hardened Images (DHI) is a free and secure path forward. Every DHI comes with cryptographic signatures, provenance attestations, and SBOMs already built in.

This means the integrity checks you relied on with DCT are guaranteed, and then some. DHI images are minimal by design and continuously rebuilt when new CVEs appear. You are not just replacing a verification mechanism, you are getting a more secure base image to begin with.

Read more here: https://docs.docker.com/dhi/ 

Or view the free catalog at dhi.io

Need help?

A couple of things worth knowing as you plan your move.

If you are a Docker Hub publisher currently signing images with DCT, Docker cannot provide replacement signatures on your behalf. You will need to adopt Cosign or Notation to sign your own images.

If you are a consumer of third-party images that were signed with DCT, contact those publishers directly to determine whether they plan to adopt modern signing.

For questions or issues related to the shutdown, or if you want to work more directly with us on a migration plan, contact Docker support.
Quelle: https://blog.docker.com/feed/