The Software Stewardship Lab launches

Post Syndicated from corbet original https://lwn.net/Articles/1087812/

The Software Stewardship Lab, a
nonprofit organization based in Scotland, has announced
its existence
.

Our current research focuses are:

  • Software supply chain security — We’re working on an observatory that
    allows users to identify and monitor the world’s critical Open Source
    packages in real time, including previously hidden parts of the
    dependency graph.

  • Maintainer burnout — Our report on burnout in Open Source has been
    well-received. We’re working on yearly follow-ups, plus a companion
    report on how AI is affecting maintainer burnout.

Moving forward, the Lab’s experts will direct funding to and supervise Open
Source sustainability researchers doing critical work. We are already
working with universities to teach the next generations of software supply
chain experts.

LightDM lives: version 1.33.0 released

Post Syndicated from jzb original https://lwn.net/Articles/1087759/

Version
1.33.0
of the LightDM
display manager has been released. This is the first
release in four years
: the project had been sponsored by Canonical
but was effectively
unmaintained
in recent years. It has been transferred
to a new community repository
and is now maintained by Joshua
Peisach and Neal Gompa.

The new release includes Qt6 support, code optimizations, and a
list of other fixes that had been in limbo pending a new release.

Stable kernel releases for Friday with a single bug fix

Post Syndicated from jzb original https://lwn.net/Articles/1087743/

Greg Kroah-Hartman has announced the release of the 6.12.102, 6.6.150, 6.1.182, 5.15.215, 5.10.264 stable kernels. This round of
stable kernel releases contains a fix for a single bug, found
by Thomas Lamprecht
, that affected several of the kernels released yesterday in
response to a security
vulnerability
(CVE-2026-68480)
that could allow data leakage through speculative execution.

The 6.12.102 release adds the backported security fix for CVE-2026-68480 to the 6.12
series. As always, users are advised to upgrade.

Security updates for Friday

Post Syndicated from jzb original https://lwn.net/Articles/1087742/

Security updates have been issued by AlmaLinux (compat-libtiff3, fence-agents, firefox, freerdp, frr, gimp, gstreamer1-plugins-bad-free, java-25-openjdk, kernel, kernel-rt, ldns, libgcrypt, libXfont2, nodejs:22, nodejs:24, p11-kit, pipewire, resource-agents, sg3_utils, thunderbird, and yelp), Debian (async-http-client, jq, kernel, linux-6.1, linux-6.12, redis, and udisks2), Fedora (abrt, chromium, coreutils, curl, freeipa, gst-devtools, gst-editing-services, gstreamer1, gstreamer1-doc, gstreamer1-plugin-libav, gstreamer1-plugins-bad-free, gstreamer1-plugins-base, gstreamer1-plugins-good, gstreamer1-plugins-ugly-free, gstreamer1-rtsp-server, ImageMagick, kernel, libXfont2, php, python-gstreamer1, samba, tcpreplay, and trafficserver), Mageia (firefox, nss, rootcerts, python-django, and thunderbird), Oracle (freerdp, gimp, gpsd, kernel, kernel-uek, and osbuild-composer), Red Hat (buildah and container-tools:rhel8), Slackware (libXfont2 and p11-kit), and SUSE (amazon-ecs-init, azure-storage-azcopy, bind, bouncycastle, cockpit-repos, cockpit-subscriptions, dnsdist, ffmpeg-4, hawk-apiserver, nodejs22, nodejs24, OpenImageIO, openssl-1_1, openssl-3, perl-Mojo-JWT, php8, rsyslog, sssd, and wireshark).

Unveiling good and bad behaviors on the Agentic Internet

Post Syndicated from Jin-Hee Lee original https://blog.cloudflare.com/good-and-bad-agentic-behaviors/

The Internet isn’t a single lane of traffic. For a long time, the rule of thumb in web security was that bots are bad, while humans are good. Of course, we’re far past this generalization. Humans can be fraudulent, and bots can be helpful at different levels. Site owners actively want some automated traffic to interact with our sites to make the Internet functional and discoverable.

To complicate things further, the line between "human" and "bot" is blurring more and more. Now, we have a type of “hybrid” traffic where a single session shifts from human to agentic and back again. (Think of a user browsing a store, and then handing off the checkout process to an automated shopping assistant.)

So, how do website owners manage this kind of complexity? What matters here is assessing behaviors. Is this behavior abusive? Malicious? What’s the risk presented here, and can I trust this visitor based on their actions? Solving this requires moving beyond static, point-in-time checks. It requires analyzing continuous behaviors to evaluate Trust.

In this post, we’ll share an inside look into the strategy of the Web Integrity & Trust team (covering the bots and fraud problem spaces) around detecting and analyzing good and bad behaviors, providing tools to help site owners tackle emerging challenges in the shifting Agentic Internet. We’ll also share findings around agentic traffic since the launch of Precursor, and a simulation where you can see how your own cursor movements would be assessed as human or bot — plus some exciting launch updates to expect in the near future.

Defining Risk and Trust

Let’s talk about the distinction between Risk and Trust, the way we discuss it within the teams at Cloudflare who work in bot detection. These are often viewed as polar opposites of a continuum. At Cloudflare, we look at them as independent, but reciprocal, values. Trust is the essential ingredient in making informed decisions on what to do about your traffic. 

Risk is how likely something like a request or action is to be harmful, and it’s often ephemeral. Trust, however, is built up over time, and it’s based on reputation.

We can illustrate this with an example from real life: say that you’re enjoying some evening television at home, when suddenly, you hear the doorbell being rung repeatedly. Besides being annoying, this behavior is strange. Frantic doorbell rings late at night are alarming.

You check through your door camera and see that the person ringing your doorbell is your best friend who lives next door. Of course, you trust your best friend, and we’d bet you would let them in.

In this example, it wouldn’t be enough for you to say, “Reject anyone who rings my doorbell at night” or “Reject anyone who rings my doorbell more than 10 times.” Again, Trust is the essential ingredient.

Going back to traffic on the Internet, the strategy as we build products in the bots and fraud space focuses on building an entire ecosystem based on Trust. And our goal is to provide the incentives and primitives for site owners to use to incentivize behavior that makes the Internet safer for everyone: starting with blocking malicious activity at the bottom, to encouraging participation in a safer Internet at the top.

Good behaviors, rooted in transparency

Starting at the top: what counts as good behavior? We can draw clear examples from the Verified bots and agents within BotBase. Last month, we announced an updated pragmatic taxonomy for the good bots we track in our system, boiling down the definition of “Verified” to two things: 1) you declare yourself honestly, and 2) you don’t abuse the trust you’ve earned. 

Transparency between a site owner and a bot operator allows for a symbiotic relationship: site owners can note what behaviors and data uses they want to allow on their websites, and bot operators can be granted access more easily. The transparency allows for Trust in the relationship; if you have nothing to hide, declaring who you are should reduce friction from the sites who want to allow your behaviors.

BotBase is not meant to only make declarations of “who is good”. It is intended to be a directory of all known bots and agents, and provide the facts. Compared to our previous Bots Directory, which only included known good bots, BotBase is also capable of tracking less-than-good bots and agents. Why? Because our systems track and validate behavior for known good actors, meaning we have the tools to identify when these expectations aren’t met. If you abuse trust on the Cloudflare network, you should not be easily allowed, so you will be unverified.

Bad behaviors: blatant, stealthy, and everything in between

A few weeks ago, we announced Precursor, a continuous client-side system to detect even subtly inhuman bot traffic that can fly under the radar when assessing network signals alone. When a customer enables Precursor, the JavaScript detection is CDN-injected, so it doesn’t require sitting at the computer and figuring out where or how to rerun these detections. What's more, Precursor evaluates user behavior continuously throughout the session, so no more free hall passes for abusive traffic that found a way to pass client and browser-side checks just once.

Applying our Risk and Trust framework to these client-side detections, we can point out that CAPTCHAs or one-time hurdles are Risk-based, meaning they lack context. On the other hand, verification using behavioral tells is Trust-based, since it can capture more context clues from the full user session. Precursor is the tool for us to analyze this behavior. To sum it up, Precursor is so powerful because it:

  1. Provides Trust-based detection over the entire user session.
  2. Drives up the cost for bot developers to replicate human behavior over a multipage timeline.

By making it economically disadvantageous for bot developers to outrun these detections, we win the adversarial game.

Now, what have we learned since we’ve launched? Looking at just a 24-hour period at the time of writing this blog, we can see 206 million Precursor evaluation events, across 73,438 zones on the Cloudflare network.

We can see patterns in the data that reveal things that we had suspected when launching the detection, but can now validate across tens of thousands of domains:

  • Suspicious behavior often happens mid-session, which point-in-time detection wouldn’t catch.
  • Behavior often shifts from human to agentic and back over a session. In these cases, it’s important to understand the intent so that site owners don’t block user flows that they actually want.
    • This highlights the importance of a bot classification system that allows website owners to handle traffic by use case, purpose, and data use. This is precisely why we prioritized taxonomy updates for BotBase.

For those curious to learn more on how Precursor actually works, we shared a sneak peek — how the signals we analyze showed us that to err is human — in our announcement blog post. Today, we’re going a step further: giving anyone on the Internet an interactive demo simulating how Precursor would trace your cursor movements.

Precursor Trace is live now, sharing how we’d assess your cursor movements using (part of) Precursor’s detection mechanism. Here, you can see whether you’re accelerating or correcting yourself, the rhythm and texture of your cursor movement, and more — all things you’ve probably never thought about as a real human being interacting with a computer. Try it out!

Adaptive Intelligence is coming soon

Cloudflare’s bot detection engines can produce different outcomes when assessing if a given request is automated or not. For requests that are deemed to be automated, the assessment can be 1) definitely automated, based on proven, deterministic methods or fingerprints of bots, or 2) likely automated, based on predictive scoring from Cloudflare’s Bots ML.

Historically, Bots ML has been updated in versions, meaning we announced each new model version as a product launch. This pacing doesn’t work when bots adapt on the scale of hours or even minutes.

Adaptive Intelligence, a completely new detection engine, is different from anything we’ve built before in the Bots ML space. The model itself is adaptive. It has learned from everything we’ve seen in the past, but more importantly, it will continue to learn and self-adjust based on what it sees. Adaptive Intelligence will upgrade itself based on a wide range of traffic patterns we identify, from good to bad behaviors, and customers will no longer need to upgrade to a formal new model version to have the latest predictive bot detections working for them. 

All Bot Management customers will have access to Adaptive Intelligence in the near future — stay tuned for the launch announcement coming soon.

Moving beyond determinism to influence bot behavior

So far, we’ve focused on Cloudflare’s side of things: strategy, detection, and taxonomy. All of this allows Cloudflare to equip website owners with the tools they need to set the traffic policies they want on their sites. Zooming in on the website owner side, we want to take this chance to discuss some advanced mitigations that allow website owners themselves to influence bot behavior.

With more blatant mitigation techniques, we face something that we’ve nicknamed the “Bot Antibiotic Problem.” Always sending bots a deterministic response (like a 403 block) makes it easy for a malicious developer bot to probe, observe, and reverse-engineer your defenses.

We know this, so we’re designing mitigations specifically made for throttling bots — with different approaches for malicious bots vs. benign bots. We can break them down into three approaches:

Approach 1: Unpredictability and Random Actions. Applying random responses (between block, challenge, or allow) to suspected automated traffic breaks a bot's automated retry logic and fingerprinting.

Approach 2: AI Labyrinth, a defensive response that traps unauthorized bots in an endless maze of AI-generated web pages. You can waste malicious bots' compute and crawl budgets by using misdirection. Site owners will be given three options within AI Labyrinth, depending on their preference:

  • Maze: Generates an endless web of linked pages for bots to follow.
  • Summary: Feeds crawlers an LLM-generated summary of a page that looks real but is entirely useless as AI training data.
  • Poison: Serves deliberately fake content (like fake prices or inventory) to a bot, polluting the data it collects for AI training.

Approach 3: Queuing for Good Bots. Not all agentic traffic is bad; queuing manages throughput for legitimate automated traffic (like user-directed shopping agents) without denying them service entirely.

These advanced, bot-specific mitigations are set to roll out closer towards the end of the year, and will be available for the website owner to choose how strict they want their mitigations to be.

We also know that a great defense is a predictive one — one that self-learns and course-corrects without needing multiple security experts on a call to reactively set a fix that accounts for the latest stealth attack. This might look like having a system of “disposable” rules, in which the ruleset is dynamic in nature. This is by design: if attacks constantly evolve, the defenses should, too. That’s why we’re working to keep both detections and mitigations a step ahead.

Establish the Trust ecosystem that works for you

Anyone and everyone can take steps to define how automated agents interact with their infrastructure. 

A few things to try:

By moving away from static, point-in-time checks and embracing continuous trust evaluation, we reduce the game of whack-a-mole with bot operators. If you’re not already using Cloudflare’s bot detection, check it out and establish the Trust ecosystem that works for you.

Introducing Radar Researcher: An AI tool for exploring Internet data in plain language

Post Syndicated from Lai Yi Ohlsen original https://blog.cloudflare.com/introducing-radar-researcher/

Since launching in 2020, Cloudflare Radar has provided one-of-a-kind insight into Internet traffic on a global scale. The open data that Radar publishes from our global network is used by a variety of experts in different domains: human rights advocates, journalists, academic researchers, network operators, and more.

Today, as part of Agents Week, Radar is beta-launching a new tool: Radar Researcher. 

Now, if you want to answer a question with Radar’s data, you can just ask in plain language and get an answer back with real, interactive charts, all built on Cloudflare's developer platform. You no longer need to find the right page, choose the right filters, or read the API documentation to write a query. 

Why we built Radar Researcher

Over the past six years, the Radar team has worked to provide clear, accessible, reliable data visualizations of complex datasets. This is not without its challenges: our user base is vast and ranges from curious novices to technical network experts. Even so, we publish as much insight into the Internet as seen from our network as we can: DNS queries from our public resolver 1.1.1.1, HTTP traffic from across our global network, network quality data collected from Cloudflare Speed Test, and so much more. We also believe in the power of open, public data, which is why everything you see in Radar’s data visualizations is available via our free API. Every year we make more and more of the Internet visible. 

But as Radar has evolved, so has the technology for working with data. AI tools are lowering the barrier to expertise: you no longer need to know a dataset's structure or vocabulary to get useful answers from it. Subject-matter experts become collaborators rather than gatekeepers.

Radar has always wanted to do more for users who shouldn't need to master our entire catalog and its vocabulary to fully appreciate our insights. (See our recent blog post on how the Internet responded to the 2026 FIFA World Cup.) 

We also know that many of our users are in a hurry. For example, a journalist writing about the latest Internet outage likely does not have the time to search through each page for the perfect graph to tell their story. This is why we built Radar Researcher.

Meet Radar Researcher

Radar Researcher is built to make accessing data as easy as describing what you’re looking for. All information is provided via Radar’s API, which ensures that the underlying LLM relies on real data, and can answer your questions quickly and in the same format as you would articulate them to a human.

It's available on every Radar page. Open the Radar Researcher panel from the header, and it docks beside whatever you're looking at, so you can keep browsing while you chat. Here are some examples of how you can use it: 

  • Get an answer in plain language, with real charts: Ask a question and get the same interactive visualizations you see across Radar, plus a short explanation.
  • Choose your depth: Pick a concise, direct answer or a fuller, multi-topic report.
  • Keep the analysis going: After each answer, it suggests useful follow-up questions.
  • Pick up where you left off: Conversations are saved in a searchable history you can pin and revisit, and you can share any conversation with a link. (Shared links expire automatically after 30 days.)
  • Audit the reasoning: Check how the LLM interpreted your question, which datasets it looked up and queried, and how it worked through the results to reach its answer.
  • Ask your way: Type, use voice input, or launch it straight from Radar's search bar.

Turn any chart into a conversation

Researcher doesn't only start from a blank prompt. Anywhere you see a chart on Radar, an Explain with AI action opens the assistant with that exact visualization already in hand. 

Instead of asking you to describe what you're looking at, it captures the exact visualization you're viewing and starts the conversation there. As you can see below, Radar Researcher takes the visualization in context, then analyzes the data and responds with an explanation.

Under the hood, the assistant hands the model three things at once: a screenshot of the chart (a vision-capable model reads the image directly), the exact data behind it from Radar's API, and the parameters of your current view, including location, date range, and any filters you've applied. The screenshot enables Radar Researcher to see what you see. The raw data keeps every number it cites precise instead of estimated from pixels, and the filters mean the explanation is about your exact view, not a generic version of the chart.

Use Case No. 1: What’s Internet quality like in Portugal?

Say you're curious how good the home Internet is in a given country. You can just ask:

Instead of manually calling the API, choosing parameters, or hunting through Radar's pages, Radar Researcher interprets the question and does the work for you. You can follow each step in the screenshot: it reads what you asked, queries Radar's Internet quality API, then analyzes the results and answers in plain language. And rather than a wall of numbers, it renders the same interactive charts you'd see on the Radar Internet Quality section.

From here you might ask a natural follow-up, like comparing Portugal to a neighbor, and Radar Researcher fetches fresh data for each one. It even suggests useful next steps of its own, such as how Portugal stacks up against Spain or the country's most common Internet outages. By letting you find what you're looking for in plain language, we hope applying Radar's data to your own analysis is easier than ever.

Use Case No. 2: Investigating an Internet disruption

Radar Researcher is also built with technical experts in mind. If we know one thing about engineers, researchers, and network operators, it’s that automation is key to their workflows. Looking at the same data with manual actions can be cumbersome and limiting: how do you ensure you're always looking at the most relevant data? 

Consider a real event. In early 2026, Iran went through a series of government-directed Internet shutdowns, including one that lasted nearly three months before traffic partially returned in May. Reporting a story like that means pulling several views and refreshing them as the situation develops. To investigate it, you can just ask Radar Researcher:

This time the question is open-ended, so Radar Researcher runs a short investigation. It looks up the outage events Cloudflare Radar recorded for Iran and gathers the matching traffic data, then explains the timeline in plain language: Iran's HTTP traffic index collapsed from around 0.58 on January 7 to effectively zero by January 9, began a partial recovery around January 17, and returned close to pre-shutdown levels by January 27. It presents this as an HTTP traffic index chart with the outage window annotated directly on the line, plus a table of the recorded outages that also flags a second shutdown starting February 28.

From there it suggests where to take the investigation next, such as how the traffic compared with neighboring countries.

How we built it

Radar Researcher is built entirely on Cloudflare's own developer platform. It's a showcase of the same tools any developer can use to build an AI agent.

At its core is a Cloudflare Worker running the Cloudflare Agents SDK. Each conversation is a stateful Durable Object with its own SQLite database, so your chat history, titles, and streaming responses persist. They even survive you leaving the page mid-answer, because generation continues server-side and resumes when you reconnect.
The "brain" is Workers AI, running open models like Kimi K2.7. Rather than betting on a single model, we run an ordered fallback chain across three different model families. If one model is momentarily at capacity, the request transparently cascades to the next, so an incident at any single provider doesn’t take Researcher down. Every call routes through AI Gateway for logging, cost tracking, caching, and safety guardrails.

The most interesting part is how Researcher reaches Radar's data. Instead of hand-writing a tool for every one of Radar's hundreds of endpoints, we connect the agent to the unified Cloudflare MCP server using Code Mode. The model gets just three tools (search, execute, and docs) and writes code to query the Radar API directly. It searches the OpenAPI spec to find the right endpoint, then executes a small snippet that fetches live data. Because the full API spec lives on the MCP server, nothing about Radar's API is hard-coded into the prompt. As Radar adds datasets, Researcher can use them with no code changes.

This is visible to you, too: every answer includes an expandable trace of the model's reasoning and the exact tool calls it made to gather the data, so you can always see how it got there.

From Markdown to real Radar charts

There was one problem to solve along the way. Language models answer in Markdown: text, tables, and bullet points. But we didn't want walls of numbers; we wanted the same rich, interactive charts you see everywhere else on Radar. Worse, when a model tries to write data directly into its answer, it tends to round, summarize, or truncate it, which is exactly what you don't want from a data tool.

Our solution keeps the data out of the model's prose entirely. When Radar Researcher fetches data, its code returns a small envelope that pairs the API path with the result. Then, instead of pasting numbers, the model emits a lightweight chart specification that simply references that same path:

Radar's frontend matches the chart's dataFrom to the fetched result and renders it with the exact same visualization components used across the rest of the site. The chart is always faithful to the API, and Radar Researcher can draw on our whole visual vocabulary: time series and stacked areas, donuts, bar charts, maps, histograms, etc.

A few small touches

Not everything needs the big model. Small, fast models handle the side tasks: one writes a short title for each new conversation, and another suggests the follow-up questions under each answer. Both run off to the side, so they never slow down your actual reply.

Radar Researcher also gets a little context about the here and now. We pass it the current date and time, plus the same high-level details about your connection that you'll find on Radar's IP page, such as your IP location and network. That way, when you ask something like "how's traffic here?", it can tailor the answer to where you are.

Everything reaches you through Radar's frontend (also a Worker) over a service binding, with per-IP rate limiting and shareable conversations stored in R2. Every layer, from compute to inference to gateway to storage to data, is Cloudflare.

Powered by agents — and ready for them

Radar Researcher shows how we use an AI agent to help people explore our data. But there's another side to the agent story: what happens when the agent isn't ours, but a general-purpose assistant running in your browser, acting on your behalf?

Today, an agent that wants to use a website mostly has to scrape the page and guess at how it works, which is brittle, slow, and error-prone. WebMCP is an emerging web standard that fixes this: a page can register a small set of well-defined tools that any browser agent can discover and call directly, instead of reverse-engineering the DOM (Document Object Model).

We've added WebMCP support to Radar, using both flavors the standard offers:

  • The imperative API lets us register tools in JavaScript that call straight into the same code that powers the UI, so an agent can drive Radar directly. Filter to a country, region, continent, or ASN; change the date range; search pages, sections, and entities (domains, ASNs, IPs, and more); jump to any section.
  • The declarative API turns Radar's existing HTML forms into tools by annotating them with a couple of attributes, so an agent can scan a URL with the URL Scanner, look up a domain's report, or test a website's post-quantum TLS key exchange support.

Every one of these simply drives functionality already available in the user interface, and it's pure progressive enhancement: in a browser without WebMCP, the tools just aren't there and nothing changes.

It’s also a case of practicing what we preach. Radar’s own URL Scanner evaluates how agent-ready a site is, and one of those checks looks for exactly this kind of WebMCP integration. By implementing it ourselves, Radar now passes its own agent-readiness check, and, more importantly, becomes a site that both people and agents can navigate with ease.

Try it out 

Radar Researcher is available in beta today on Cloudflare Radar. Look for the Researcher button in the header on any page. It's built entirely on Cloudflare's developer platform, and it's just getting started: we'll keep expanding the datasets it understands, sharpening its analysis, and adding new ways to interact with data.

Like all Cloudflare products, Radar is built with the user in mind. We want to know what’s useful. Please let us know what you think on social media at @CloudflareRadar (X), noc.social/@cloudflareradar (Mastodon), and radar.cloudflare.com (Bluesky), or by email at [email protected]

Tell us what works, what doesn’t, the insights you’re finding, what still feels beyond reach, and everything in between. Your feedback will help us improve and make Radar Researcher even better for our full launch. We can’t wait to see what you do with it! 

Announcing Cloudflare Ambassadors, Community Engineers, and another $1M in open-source funding

Post Syndicated from Kristian Freeman original https://blog.cloudflare.com/community-program-refresh/

As a platform for helping build a better Internet, Cloudflare helps turn ideas into real products and experiences around the world. Across communities and backgrounds, developers build with Cloudflare using the tools they love, shaping what comes next for the Internet while inspiring, collaborating with, and teaching others.

The community is where some of Cloudflare’s best moments happen. Students show their friends how to deploy Workers for the first time. Discord users answer questions from other developers via working code samples, instead of links to documentation. Open-source contributors build novel solutions to solve their own problems, then share them with the world. Organizers host events that give builders from all backgrounds the space to start building their dream project.

All of these represent a community at its best: people helping other people build.

This spirit of community is an exciting and vital part of helping to build the Internet. Those who step up to educate and support others, or to invent, build, or maintain tools shared across the ecosystem, make lasting contributions to the health and potential of the Internet.

We want to have their backs.

That's why today we’re announcing an improved community program, designed to better support, recognize, and empower the people getting involved, while working with them to shape what comes next.
The program has two main tracks:

  1. Cloudflare Ambassadors: Bringing Cloudflare to their own communities.
  2. Cloudflare Community Engineers: Contributing to open-source projects that improve the Internet.

We’re launching a new home for the program where you can learn more and get involved: cloudflare.com/community.

Cloudflare Ambassadors

Cloudflare Ambassadors are people who bring Cloudflare into their own communities. You can probably think of people in the communities you value who share a genuine passion for a product or technology. It’s inspiring and we love to see it. When that enthusiasm includes the tools we’re building here at Cloudflare, it’s especially exciting for us.

Following our annual application process (more below), we’ll announce the year’s Cloudflare Ambassadors cohort. Selected Ambassadors will receive support, resources, and benefits to help their community thrive and bring their ideas to life. Ambassadors can serve for up to two years, giving them meaningful time to build momentum while helping us support more communities over time.

What Ambassadors do and what we provide

Being an Ambassador might mean organizing a local event, leading a student group, creating spaces where builders can learn together, publishing tutorials or sharing content online, or being the person others turn to when they want to understand what’s possible with Cloudflare. 

Ambassadors will take the lead on events in their communities, whether on campus, through local organizations, or across their city. When hosting meetups, hackathons, workshops, or talks, they will be able to apply for support in the form of credits, marketing assets, technical resources, and more.

We’ll also give them a visible role in Cloudflare’s online community spaces, including Discord, so that other developers know who they are, and that they’re here to help.

Applications are open now, and will be accepted through September 6. Those selected as Ambassadors will be informed of their selection by October 5.
Apply to become a Cloudflare Ambassador

A great example of the enthusiasm we’re looking for comes from Sruthi Pereddy, a Computer Science major at University of Michigan and a current intern on Cloudflare’s Recruiting Ops team. Sruthi’s work within Cloudflare has created a drive to share and explore more with others:

“Whether it’s hackathons, startup venture funds, or coursework, I want to show my peers that Cloudflare is a go-to developer platform for whatever they’re building,” Pereddy says. “Students are ready to build, but often feel constrained by resources. I’m excited to bridge that gap and make sure they have the infrastructure to turn their ideas into reality from day one.”

Cloudflare Community Engineers

Some community work happens in person, but a great deal of community work also happens in code. Much of Cloudflare’s Developer Platform is built on open-source work, or is open-source, like workerd and quiche. Open-source contributors, especially maintainers, do wonderful work and embody so much passion and determination. We’re eager to support them, especially since their work can sometimes feel thankless. So we’re doubling down on our efforts to build stronger incentives and directly support the maintainers doing this important work.

Last year, we announced our sponsorship of the web framework TanStack. TanStack creator Tanner Linsley says that sponsorship has had a major impact.

“Cloudflare’s sponsorship has given us room to keep investing in foundational open-source work that’s hard to tie to a single product or launch, maintaining the core libraries, improving docs and tooling, supporting contributors, and putting real time into bigger bets like TanStack Router and Start,” Linsley says. “It’s also helped us make sure TanStack apps have a really solid path onto Cloudflare’s platform. More than anything, that support buys stability, which is kind of everything when you’re building open source for the long haul.”

Today, we’re expanding on our previous open-source investments by introducing Cloudflare Community Engineers. Earlier this year, we announced a $1M fund as part of our acquisition of VoidZero to support the Vite community. We’re committing an additional $1M in funding to sponsor and support open-source projects over the next two years, with eligible Community Engineers receiving grants from the fund to support their continuing work in open source.

The Community Engineer program does not have a maximum term. Open source work doesn’t neatly fit into annual cycles. Some projects require maintenance for years, while other times, contributors do the work that is needed at exactly the right moment. This program is intended to support that.

To begin, we’ll focus on developers working on things in the orbit of our own open-source projects — projects like Astro, Agents SDK, EmDash, Hono, and Vinext. We’ll also grant our Community Engineers a special designation in Cloudflare’s Discord server and other online spaces.

Applications for Community Engineer grants will open at a later date.

Making our Discord better as it grows

Since we launched Cloudflare’s Discord server in 2020, almost 100,000 Cloudflare users have joined. Our Discord server has become one of the main places where developers ask questions, share projects, and provide valuable feedback. But of course, the more a Discord community grows, the more effort is required to keep it healthy and approachable.

To address this, a new Discord committee will help to maintain and grow our Discord community, with Cloudflare Ambassadors joining Cloudflare staff on the committee.

This is not about being on hand to perform moderation and admin tasks. We’ve been building tools and automations to help us do that with far less human intervention. Our new automated protections against spam and malicious links are starting to relieve this burden, allowing our Developer Relations team to help manage things where some human insight is needed.

In fact, we’ll be open-sourcing and sharing those tools soon because we think every Discord server could benefit from less spam and malicious content.

The committee will help provide a useful connection to those building and managing products at Cloudflare. They’ll be able to steer people and conversations to domain experts and convene conversations and sessions with internal teams and makers around the community. They’ll be much more focused on content and opportunities than on the type of Discord administrivia that can otherwise swallow so much time and energy.

We want our Discord to be easier to use, contribute to, and trust. It should be a place where builders find each other, help each other, and shape the future of the platform together. We believe this is the way.

Ready, set, go!

To learn more about the community program, and to apply for a role, visit the new community site at cloudflare.com/community.

Applications to join the 2026-27 Cloudflare Ambassadors cohort have now officially opened. Be sure to apply by September 6.

And don’t forget to join the conversation in the Cloudflare Discord.

Unifying Workers AI and AI Gateway into a single AI control plane

Post Syndicated from Michelle Chen original https://blog.cloudflare.com/workers-ai-gateway-unification/

AI Gateway and Workers AI first started as distinct products, but over time, we noticed our users were converging. With AI Gateway, you can proxy requests to any model provider and get built-in observability, logging, access, and security. On Workers AI, we host models on the GPU infrastructure that we manage, exposing an API endpoint you can leverage to access inference-as-a-service. 

The architecture of these products looks different, but to an end user, it achieves the same goal: connecting you to models with a sophisticated control plane. Today, we're excited to share our plans on how these products converge into one unified path, so you can connect to any model provider (including Workers AI), while managing things like observability, billing, security, and logging from a single control plane.

It’s the next step toward some big plans we have — read on to learn what a unified control plane means for the future of model routing.

Merging the binding and API

We've been hinting at the fact that these products are becoming more unified through our entrypoints: the Workers binding and the REST API. We have an AI binding that you can use to call AI Gateway and Workers AI. There's no concept of a separate AI Gateway and Workers AI binding: it all goes through the same path. We shipped the idea of a “default” gateway a few months ago, so that if you have never set up an AI Gateway before, you could still automatically inherit the AI Gateway observability and logging. Of course, you can still specify your own gateway if you'd like to split up applications into multiple projects.

Here's what the binding call looks like, if you are calling Workers AI via AI Gateway:

We’ve also announced a single unified REST API — the /ai/ endpoint that allows you to make similar calls to Workers AI via AI Gateway.

Doing this allows us to unify the entrypoints to AI Gateway and Workers AI, so you don't need to make a choice between which product to use first: it all comes with batteries included.

Automatic observability and control for all Workers AI users

One of the most immediate benefits of this convergence is that you no longer need to explicitly create an AI Gateway before you start getting visibility into your inference traffic. If you've never set up a gateway before, just pass default as the gateway ID in your binding or REST API calls, and AI Gateway will create it automatically on the first authenticated request.

With this, every request is logged with full request and response payloads, token counts are tracked per model, and you get cost attribution without any dashboard setup. If you later outgrow the default gateway — if you want custom caching rules or to split traffic by application — you can create a named gateway and point your requests at it with a single parameter change.

Here's how it looks in the binding. Before, you called Workers AI directly:

Now, add a third argument to route through AI Gateway and get full observability:

Head to the Cloudflare AI Gateway dashboard and you'll see every request: latency breakdowns, token usage, error rates, and the exact prompts and responses. For teams debugging model behavior or auditing AI output, this is a huge upgrade from flying blind.

New: using AI Gateway credits for Workers AI

A new thing we're launching today is the ability to use AI Gateway credits for Workers AI. Before, you could only use AI Gateway credits on external model providers (e.g., OpenAI, Anthropic) but you couldn't apply your AI Gateway credits to Workers AI usage just yet. We've finally enabled our systems to allow unified billing for Workers AI. This means that you can load a wallet full of credits, and then choose to spend that across OpenAI, Anthropic, Workers AI, or any provider that we support.

Since we're now offering pre-paid billing for Workers AI and want to encourage users to use this new path, we're also offering elevated rate limits on Workers AI models if you use AI Gateway unified billing. Please refer to the developer docs for up-to-date information regarding rate limits, as well as how to request a higher rate limit.

Coming soon: model-first routing

With all your inference traffic flowing through a single control plane, we can start making smarter decisions about how to serve each request starting with the model you want, not the provider you have to manage. Provider-first routing forces you to think about infrastructure: "Which provider do I call? What if they're down?" Model-first routing flips that. You think about what you need — a capable reasoning model, a fast summarizer, a cheap embedding model — and the control plane handles provider selection, failover, and load balancing.

Today, if you want to call a model, you have to know which provider hosts it. If that provider is down or rate-limiting you, your application breaks. We're moving toward a world where you specify the model, and AI Gateway handles the rest. 

This way, you can request Kimi K2.7 Code and not care whether it comes from Workers AI, Moonshot's own API, or another provider that hosts the same weights. If Workers AI has capacity, you get the benefit of our managed infrastructure. If Workers AI is at capacity, the gateway transparently load balances you to another provider that can serve the same model. You can still choose to stick to a single provider if you’d like, but model-first routing enables you to get more flexibility if you care about resiliency. We work with vetted providers, so the quality of model outputs remains top priority, and will also be able to respect requirements such as Zero Data Retention (ZDR).

This also means better resiliency by default. If one provider's version of a model is having issues, traffic shifts to another without application-level retries or complex fallback logic in your Workers. The gateway treats model availability as a routing problem. We hope to pilot this in the coming months for all AI Gateway and Workers AI users.

Next: smart routing

The next evolution of routing goes beyond simple failover. We're building intelligent routing that understands what you're asking for and picks the right model for the job without any configuration required.

Instead of specifying a model, you can let the gateway decide. Under the hood, a classifier running on Workers AI reads your prompt and predicts what kind of task it is (coding, research, summarization, general Q&A), how complex it is, and how much context matters. A heuristic scorer then maps that to the best model from a curated pool. For teams that want control, you can still specify exact models. For everyone else, the zero-config path means you get better economics and performance without maintaining your own routing logic. We are currently piloting this internally, and we’ll be actively testing and iterating in the next few weeks before release. 

Get started today

If you're already using Workers AI, the easiest way to try this out is to start routing your existing calls through a default gateway. You'll immediately get request logging, token tracking, and cost attribution without changing anything else about how you call the models.

If you're already using AI Gateway, adding Workers AI to the mix is as simple as calling a Workers AI model. Load up your AI Gateway wallet and you'll get unified billing across every provider we support, plus elevated rate limits on Workers AI models.

Set up your first gateway, browse the Workers AI model catalog, and get started building today.

Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/runtime-instances-persistent-compute-for-production-ai-agents-on-amazon-bedrock-agentcore/

When you move AI agents from prototype to production, the infrastructure challenges multiply. Your agents need to persist state across multi-step workflows that run for hours or days. They need to coordinate with other agents, share context, and sometimes access GPUs for specialized tasks. Amazon Bedrock AgentCore runtime microVMs provide a fully managed environment for invocations that can run for up to 8 hours and support stateful workflows through managed session storage. Some workloads also benefit from dedicated, larger-capacity environments — for example, when agents need to run continuously for multiple days, access GPUs or the underlying OS, or run multiple collaborating agents on the same host.

Today, I’m happy to announce runtime instances, a new complementary compute option in Amazon Bedrock AgentCore Runtime that gives your agents persistent, managed infrastructure purpose-built for complex agent workloads.

What you get
Runtime instances provides AWS-managed EC2 infrastructure where you deploy multiple agents in a single runtime, each with their own dependencies and artifact types. Your agents can collaborate on the same host within shared sessions that persist for up to 14 days. The service supports GPU acceleration for compute-intensive tasks, session stop/restart to save costs during idle periods, and containerized deployments for teams that want to ship independently. For knowledge that needs to survive beyond a session, runtime instances pairs naturally with Amazon Elastic Block Store (Amazon EBS) and AgentCore Memory, which gives your agents long-term recall across sessions and environments.

Before today, if you wanted to keep your agents running for days or they needed GPU access, or multi-agent coordination, you had to build and manage that infrastructure yourself. You provisioned EC2 instances, configured networking, set up session management, handled scaling, and stitched together monitoring. Runtime instances handles all of that for you while integrating with the same AgentCore APIs, identity controls, and observability you already use with AgentCore Runtime microVMs.

A few things that should make agent developers smile: your agents can call each other as tools within a shared session, iterating autonomously until the job is done. You bring any framework (CrewAI, LangGraph, LlamaIndex, Strands) and any model. Packaging is minimal, a @app.entrypoint decorator and a zip file or container image. And if your workflow spans days, hibernate Monday night and resume Wednesday morning with everything intact.

Runtime microVMs and runtime instances are complementary compute options that you can use independently or together through the same AgentCore runtime APIs. A lightweight orchestrator agent on runtime microVM can coordinate and dispatch work to specialized worker agents running on instances. The orchestrator handles API calls, task routing, and result aggregation using runtime microVM’s fast scaling, while workers on Instances perform compute-intensive tasks like code compilation, security scanning, or GUI automation that require persistent state and direct OS access.

Let me show you how it works
I built two agents for this demo: a code writer agent that generates Python code from natural language descriptions, and a code reviewer agent that analyzes the generated code for bugs, security issues, and style improvements. Both agents share the same file system, so the reviewer can read whatever the writer produces without any data transfer or API calls between them.

Here is the code writer (simplified, no error handling):

writer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a senior Python engineer. "
        "Given a task, return ONLY a single Python code block — no prose."
    ),
)

@app.entrypoint
def handler(event, context):
    task = event.get("task") or event.get("prompt")
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    session_dir = SHARED_DIR / session_id
    session_dir.mkdir(parents=True, exist_ok=True)

    code = str(writer(task))
    (session_dir / "code.py").write_text(code)

    return {"agent": "writer", "wrote": str(session_dir / "code.py"), "code": code}

Here is the code reviewer agent (simplified, no error handling):

reviewer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a strict Python code reviewer. "
        "Given code, return 3 bullet points: bugs, style, suggestions."
    ),
)

@app.entrypoint
def handler(event, context):
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    code_path = SHARED_DIR / session_id / "code.py"
    code = code_path.read_text()
    review = str(reviewer(f"Review this code:\n\n{code}"))

    return {"agent": "reviewer", "read": str(code_path), "review": review}

Each agent is a Python application using Strands Agents with an @app.entrypoint decorator and a model of its choice. I package each one as a zip file. For this demo, I use the AWS Management Console. You can also use the AgentCore CLI, the AWS Command Line Interface (AWS CLI) or infrastructure as code.

Step 1: Create a capacity provider.

A capacity provider defines the EC2 infrastructure your agents run on. In the AgentCore console, I select Runtime in the left navigation, then select the Capacity providers tab and Create capacity provider.

ACI Create Capcity Provider 1

I give it a Name, select Linux (64-bit ARM) as the Operating system, and choose c7g.2xlarge as the Allowed instance types. This gives me 8 vCPUs and 16 GiB of memory, enough for both agents to run comfortably side by side.

Further down, I configure the VPC, subnets, and security groups for network access. Under Storage configuration, I keep the default gp3 volume. Under Service access, I select Create a new service role and let the console create the infrastructure role that manages EC2 instances on my behalf.

I select Create capacity provider and wait a few seconds. The status moves to Active.

ACI Create Capacity Provider 2

ACI Create Capacity Provider 3

Note the capacity provider configuration summary: operating system, instance type, subnets, security group, instance profile, and infrastructure role. Once created, only the description can be edited, so verify your settings before you proceed.

ACI Create Capcity Provider 2

Step 2: Create a runtime and deploy the first agent.

Back on the Runtime page, I select Create runtime. I give it a Name, select Instances as the Compute type, and choose the Capacity provider I created in the previous step.

ACI Create Runtime 1

Under Agent source, I select S3 Source, then Upload to S3. I choose my agent zip file (ACIDemoWriter.zip), set the Language runtime to Python 3.13, and specify agent.py as the Agent entry point. This is the file that contains my @app.entrypoint decorated function. Under Permissions, I select Create default role to let the console provision the IAM role my agent needs.

ACI Create Runtime 2

I select Create runtime and wait for the status to become Ready.

I repeat the same process for my code reviewer agent. I create a second runtime, select the same capacity provider, upload my reviewer agent zip file, and wait for it to become Ready. Both agents now share the same underlying EC2 infrastructure.

AgentCore Runtime Instances - Agent ReadyThe console shows me a View invocation code section with ready-to-use Python, TypeScript, and JavaScript snippets to invoke my agent programmatically. But for this demo, I use the built-in test feature. I select Test on the writer agent’s page.

AgentCore Runtime Instances - Show invocation codeStep 3: Invoke agents and observe collaboration.

The Runtime playground opens. At the top, I see three fields: Runtime agent, Endpoint, and Session ID. The console generates a session ID automatically. I take note of it because I will reuse it with the reviewer agent.

In the Input field, I type a JSON payload asking the writer agent to generate code:

{"prompt": "write a fibonacci suite"}

I select Run. After a few seconds, the Output panel shows the agent’s response. The writer agent generated a Python module with two implementations of a Fibonacci sequence (a list-based function and a generator) and wrote it to /tmp/agentcore-session/ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2/code.py. Notice the session ID in the file path. That directory is the shared file system for this session.

AgentCore Runtime Instances - Invoke code writer agent

Step 4: Invoke the reviewer agent in the same session.

Now I switch the Runtime agent dropdown to ACIDemoReviewer. The important part: I paste the same session ID (ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2) in the Session ID field. This is what connects the two agents.

I type a simple prompt:

{"prompt": "review the code"}

I select Run. The reviewer agent reads the file the writer produced from the shared session directory and returns a detailed code review. It finds no critical bugs but suggests adding type hints, input validation, and simplifying the edge case handling.

AgentCore Runtime Instances - Invoke code reviewer agentThe two agents never exchanged messages or called each other’s APIs. They collaborated through the shared file system that runtime instances provide within a session. You can extend this pattern to any number of agents: a test agent that runs the code, a documentation agent that generates README files, a security agent that scans for vulnerabilities, all sharing the same working directory.

Key details
Here are a few things to know as you get started:

  • Supported OS: Linux (ARM64 and x86_64) at launch.
  • Session persistence: Sessions persist for up to 14 days.
  • Runtimes: Python 3.11-14 with native code support. Container images also supported.
  • GPU: Support for GPU-accelerated instance types.
  • Integration: Uses the same AgentCore APIs, identity, observability, and policy controls as AgentCore Runtime.
  • Pricing: Standard EC2 pricing plus a management fee for AgentCore orchestration.
  • Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland)

To get started, visit the runtime instance in Amazon Bedrock AgentCore documentation and create your first capacity provider.

— seb

Automate certificates with ACME support in AWS Certificate Manager

Post Syndicated from Anthony Harvey original https://aws.amazon.com/blogs/security/automate-certificates-with-acme-support-in-aws-certificate-manager/

Customers tell us that managing TLS certificates at scale is one of their biggest operational concerns. The Certification Authority Browser Forum (CA/Browser Forum) has mandated a phased reduction in maximum certificate validity for public certificates. By March 2027, the maximum validity drops to 100 days. By March 2029, it lasts for 47 days. For an organization managing 1,000 certificates, the final transition means roughly 30 renewal events every day. Renewal and rotations of renewed certificates at that cadence isn’t something manual processes or ticket-driven workflows can sustain at scale.

We recently announced Automated Certificate Management Environment (ACME) protocol support in AWS Certificate Manager (ACM). With this launch, you can use the ACME clients your teams already know, including popular open source tools like certbot, cert-manager, acme.sh, and win-acme, to automate public certificate issuance and renewal for your infrastructure. Customers that are using third-party certificate authorities (CAs) can point their existing ACME-compatible clients at ACM instead of their current CA, with minimal reconfiguration. This applies whether it’s running on Amazon Web Services (AWS), on premises, or in a hybrid environment. Certificates created through ACME are registered in ACM, giving you a unified view of your entire certificate inventory.

This post covers how the feature works, how to get started, and the controls and best practices to help you manage certificate issuance at scale.

Background

ACME is an open source protocol that automates the process of verifying domain ownership and issuing certificates and has become a standard mechanism for certificate automation. While ACM has long provided managed certificate issuance and renewal for AWS-integrated services such as Elastic Load Balancing (ELB), Amazon CloudFront, and Amazon API Gateway, many customers also need to automate certificates for their own infrastructure, including servers they manage in their data centers, Kubernetes clusters, Internet of Things (IoT) fleets, and hybrid environments. Until now, those customers had to turn to external providers. This launch brings the ACM automation model to that same infrastructure, using the standard ACME protocol with AWS managed certificate endpoints.

How it works

The feature introduces a new centrally provisioned and managed resource type: the ACME endpoint. Each endpoint is an AWS resource with a unique ACME directory URL and AWS Identity and Access Management (IAM)-based access controls. You create and manage endpoints through the ACM API or AWS Management Console, and point your existing ACME clients at the endpoint URL. Certificates issued through your endpoint are automatically registered with ACM, appearing in your certificate inventory alongside certificates created by the RequestCertificate and ImportCertificate API calls.

The architecture separates into two planes. In the control plane, PKI administrators use ACM APIs to create ACME endpoints, pre-approve the domains an endpoint is allowed to issue for, and generate external account binding (EAB) credentials. In the data plane, ACME clients register with an endpoint using EAB credentials and request certificates for domains the administrator has already validated. This architecture is how we provide customers the ability to scale. Instead of each client proving domain ownership on every request, a principal with appropriate ACM permissions (typically your PKI administrator) validates domains once at the endpoint level, and then application owners don’t need DNS credentials to get a certificate.

Adding to the data plane, EABs control client access to the endpoints. Each EAB is bound to an IAM role that controls what certificate operations the ACME client can perform, and credentials you generate in ACM are distributed to authorized ACME clients. An ACME client authorized for one endpoint can’t use a different endpoint. This creates security boundaries between environments. For example, a client authorized for your development endpoint can’t obtain certificates from your production endpoint.

Figure 1 shows the ACME request flow through ACM. An ACME client authenticates to an ACME endpoint using EAB credentials. The endpoint routes certificate orders to Amazon Trust Services for issuance. Issued certificates are registered in ACM inventory, where Amazon EventBridge and AWS CloudTrail provide expiration alerting and audit logging.

Figure 1: An ACME architecture and workflow

Figure 1: An ACME architecture and workflow

Getting started

Getting started with the new ACME feature in ACM is straightforward. Use the following steps to create your first ACME-generated certificate.

Prerequisites

  • An AWS account with permissions to create and manage ACM resources
  • An ACME client installed on your infrastructure (for example, Certbot, cert-manager, acme.sh, or others)
  • AWS Command Line Interface (AWS CLI) installed on your device (see this blog post for the console equivalent)
  • Amazon Route 53 hosted zone for your domain, or the ability to create a CNAME record with your DNS provider

Step 1: Create an ACME endpoint

Before you can use ACME clients with ACM, you need to create an ACME endpoint. This endpoint provides the URL that your ACME clients will use to request certificates.

  1. Run the following command from the AWS CLI to create an ACME endpoint:
    aws acm create-acme-endpoint \
      --authorization-behavior PRE_APPROVED \
      --certificate-authority '{"PublicCertificateAuthority":{"AllowedKeyAlgorithms":["EC_prime256v1"]}}

  2. Note the endpoint Amazon Resource Name (ARN) from the response.
    {"AcmeEndpointArn": "arn:aws:acm:us-east-1:123456789012:acme-endpoint/11111111-2222-3333-4444-555555555555"}

  3. Run the following command to retrieve the endpoint URL, replacing the ARN with your endpoint ARN:
    aws acm describe-acme-endpoint \
    --acme-endpoint-arn arn:aws:acm:us-east-1:123456789012:acme-endpoint/11111111-2222-3333-4444-555555555555

  4. Save the output of the ACME EndpointUrl:
    {
        "AcmeEndpoint": {
            "AcmeEndpointArn": "arn:aws:acm:us-east-1:123456789012:acme-endpoint/11111111-2222-3333-4444-555555555555",
            "EndpointUrl": "https://acm-acme-enroll.<region>.api.aws/6666666-7777-8888-9999-000000000000/directory",
            "Status": "ACTIVE",
            "AuthorizationBehavior": "PRE_APPROVED",
            "Contact": "REQUIRED",
            "CertificateAuthority": {
                "PublicCertificateAuthority": {
                    "AllowedKeyAlgorithms": [
                        "EC_prime256v1"
                    ]
                }
            },
            "CreatedAt": "2026-07-14T18:23:58.876000-04:00",
            "UpdatedAt": "2026-07-14T18:23:58.876000-04:00"
        }
    }
    

Step 2: Pre-approve a domain

Before ACME clients can request a certificate, the administrator validates the domain using DNS once at the endpoint level. Use DomainScope to control exactly which certificate patterns are allowed:

  • Enabling only ExactDomain restricts clients to that specific name,
  • Subdomains enabled allows names like api.example.com,
  • Wildcards enabled allows *.example.com.

Leave a scope disabled to block that pattern outright, even if an otherwise-valid ACME request asks for it. For a production endpoint, consider enabling only ExactDomain and Subdomains and leaving Wildcards disabled for a stricter posture.

aws acm create-acme-domain-validation \
--acme-endpoint-arn arn:aws:acm:us-east-1:123456789012:acme-endpoint/11111111-2222-3333-4444-555555555555 \
--domain-name example.com \
--prevalidation-options '{"DnsPrevalidation":{"DomainScope":{"ExactDomain":"ENABLED","Subdomains":"ENABLED","Wildcards":"DISABLED"},"HostedZoneId":"Z1234567890ABC"}}'

If your domain is hosted in Route 53, specifying HostedZoneId lets ACM create the required CNAME record automatically. If your domain is hosted elsewhere, omit it and create the provided CNAME record manually with your DNS provider. Validation typically completes within a few seconds after the record is in place.

You will receive the following response back:

{
    "AcmeDomainValidationArn": "arn:aws:acm:us-east-1:123456789012:acme-endpoint/1111111-2222-3333-4444-555555555555/acme-domain-validation/6666666-8888-9999-0000-11111111111"
}

Step 3: Generate EAB credentials

EAB credentials authenticate your ACME clients to your endpoint. Generate a unique set of credentials for each client or environment to maintain security boundaries.

  1. Run the following command to generate your EAB credentials, adjusting your expiration to fit your organization’s risk profile:
    aws acm create-acme-external-account-binding \
        --acme-endpoint-arn arn:aws:acm:region:111122223333:acme-endpoint/00000000-0000-0000-0000-000000000000 \
        --role-arn arn:aws:iam::111122223333:role/AcmeIssuanceRole \
        --expiration '{"Value": 7, "Type": "DAYS"}'

  2. Note the response from a successful invocation of the command
    {
        "ExternalAccountBinding": {
            "AcmeExternalAccountBindingArn": "arn:aws:acm:region:111122223333:acme-endpoint/00000000-0000-0000-0000-000000000000/acme-external-account-binding/1234567-1234-1234-1234-123456789012",
            "AcmeEndpointArn": "arn:aws:acm:region:111122223333:acme-endpoint/00000000-0000-0000-0000-000000000000",
            "RoleArn": "arn:aws:iam::123456789012:role/service-role/AcmAcmeIssuanceRole-XXXXXXXX",
            "ExpiresAt": "2026-07-21T18:47:50.641000-04:00"
        }
    }
    

  3. Run the following command to retrieve the credentials. You’ll need these values for your ACME client configuration the next step.
    aws acm get-acme-external-account-binding-credentials \
        --acme-external-account-binding-arn arn:aws:acm:region:111122223333:acme-endpoint/00000000-0000-0000-0000-000000000000/acme-external-account-binding/22222222-2222-2222-2222-222222222222

  4. Save the KeyId and MacKey for the next step.
    {
        "KeyId": "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx",
        "MacKey": "xxxxxxxx-xxxxxxxxxx-xxxxxxxxxxxxxxx"
    }

Step 4: Configure your ACME client

With your endpoint URL and EAB credentials ready, you can now configure your preferred ACME client. The following examples show configuration for two popular clients. As a reminder, the server information was retrieved in step 1, part 4 as the EndpointUrl.

acme.sh:

acme.sh --issue --server https://acm-acme-enroll.us-east-1.api.aws/123457-1234-1234-123456789012/directory \
    --eab-kid <KeyId> --eab-hmac-key <MacKey> \
    --email <EMAIL> \
    -d <DOMAIN> \
    --dns --yes-I-know-dns-manual-mode-enough-go-ahead-please

Certbot:

certbot certonly --standalone --non-interactive --agree-tos \
  --email <EMAIL> \
  --server https://acm-acme-enroll.us-east-1.api.aws/1234567-1234-1234-123456789012/directory \
  --eab-kid <KeyId> \
  --eab-hmac-key <MacKey> \
  -d <DOMAIN>

After the initial registration, your ACME client handles renewals.

Enterprise controls

Other ACME alternatives can provide certificates but don’t give the same amount of control and governance for customers that need to scale their certificate environment. The following controls are available to help reduce risk across your organization.

Domain validation

Customers managing large numbers of domains told us they need a way to prevent unauthorized certificate issuance across their domain space. Domain validation gives you this control. For each domain you validate, you enable the certificate patterns it should be allowed to issue, whether it’s ExactDomain, Subdomains, or Wildcards. For example, if you validate internal.example.com and enable only Wildcards, an ACME client can request *.internal.example.com but a request for internal.example.com itself or api.internal.example.com is rejected. This enforcement happens at the endpoint level, before requests reach the ACM certificate authority, and you can validate multiple domains under a single endpoint, each with its own scope.

Centralized certificate visibility

Certificates issued through your ACME endpoints are registered with ACM. You can use the aws acm list-certificates command to see all your issued certificates.

IAM authorization, CloudTrail audit logging and observability

Endpoint management operations are authorized through IAM and logged to CloudTrail. You can use IAM policies to control which principals can create endpoints, generate EAB credentials, and manage domain constraints.

Best practices

For customers implementing ACME certificates for the first time, consider the following best practices for your organizations.

Segment endpoints along organizational or environment boundaries

The endpoint serves as a useful method of isolation for larger organizations. A large enterprise can create one endpoint per organizational boundary (business unit, subsidiary, or environment) instead of a single shared endpoint company-wide. Each endpoint has its own pre-approved domains and its own set of EABs, so a compromised credential in one business unit has no path to certificates in another.

However, weigh this against your operational overhead as well. A reasonable starting point is one endpoint per environment (dev, staging, andprod) within a business unit, expanding to per-business-unit endpoints only where compliance or organizational requirements call for it.

Manage EAB credentials securely

Anyone holding a validKeyIdandMacKeyfor an endpoint can obtain certificates for any domain pre-approved on that endpoint, so these credentials deserve the same handling you’d give an access key.

  • Avoid hard coding theMacKeywhere possible by using a secret store such as AWS Secrets Manager. Distribute it only to the ACME clients that you authorize to use the endpoint.
  • Set the expiration of the EAB to an acceptable level. While EAB supports long-lived credentials, not all scenarios require an indefinitely long EAB.
  • When creating the role for each EAB, adhere to concept of least privilege. Creating a role per EAB, rather than sharing a role across all bindings, can help reduce risk in your AWS environment.
  • Audit CreateAcmeExternalAccountBinding and GetAcmeExternalAccountBindingCredentials calls in CloudTrail separately. Because retrieving the actual key material is a distinct API call from creating the binding, alerting on retrieval events is a stronger signal of real credential distribution than binding creation alone.

Automate how EABs are associated with clients at runtime

Generate a unique set of EAB credentials for each client or environment rather than sharing one binding across multiple ACME clients. As you begin to scale with multiple endpoints, usesome of the following patterns to reduce operational toil.

  • Name each EAB and its bound IAM role after the client it belongs to (team, application, environment), so the binding’s purpose is obvious from DescribeAcmeExternalAccountBinding output alone, without cross-referencing a spreadsheet.
  • Store each client’s KeyId and MacKey under a secrets path scoped to that client (for example, a Secrets Manager path per team and environment), and let the client’s provisioning pipeline retrieve its own credentials.
  • In Kubernetes, use one ClusterIssuer or namespace-scoped Issuer per EAB rather than one shared issuer across teams. This keeps the client-to-EAB association explicit in cluster config, and lets you revoke one team’s access without touching anyone else’s.
  • For ephemeral infrastructure (build agents, autoscaled fleets), provision EAB credentials as part of your infrastructure-as-code or continuous integration and deployment (CI/CD) pipeline instead of a one-time manual handoff, so credential lifecycle tracks infrastructure lifecycle.

Monitor your deployment of ACME

ACME’s power is through automation, and organizations should monitor their ACME usage for anomalies.

  • Alarm on issuance failures, not just successes. At 45-day certificate validity, a silent renewal failure gives you far less runway to react than the months of time you might be used to with longer-lived certificates.
  • Test renewal automation before you depend on it. Force a manual renewal against a non-production endpoint and confirm your client, monitoring, and on-call runbooks behave as expected, before the CA/Browser Forum’s shortened validity windows turn a failed renewal into a disruptive event for your organization.

Availability and pricing

ACME support in AWS Certificate Manager is available today in all commercial AWS Regions and will be available in AWS GovCloud (US), the China Regions, and the AWS European Sovereign Cloud partitions at a later date. See the ACM pricing page for more information on ACME pricing.

Conclusion

The phased reduction in certificate validity can’t easily be solved without automation. ACME support in ACM gives you that automation through a standard protocol and standard tooling, while keeping the visibility and governance controls your security teams rely on from ACM.

To get started, see the AWS Certificate Manager documentation or follow the getting started guide.

If you have feedback about this post, submit comments in the Comments section below.


Anthony Harvey

Anthony Harvey

Anthony is a Senior Security Specialist Solutions Architect for AWS in the worldwide public sector group. Prior to joining AWS, he was a chief information security officer in local government for half a decade. With his public sector experience, he has a passion for figuring out how to do more with less and leveraging that mindset to enable customers in their security journey.

Chandan Kundapur

Chandan Kundapur

Chandan is a Principal Product Manager on the AWS Certificate Manager (ACM) team. With over 15 years of cybersecurity experience, he has a passion for driving PKI product strategy.

Scaling Autonomous Operations with AWS DevOps Agent and ServiceNow

Post Syndicated from Jack Hwang original https://aws.amazon.com/blogs/devops/scaling-autonomous-operations-with-aws-devops-agent-and-servicenow/

This post is co-written with Govind Menon, Head of MCP Product at ServiceNow.

Introduction

Enterprise teams managing applications on AWS often rely on ServiceNow as their IT service management (ITSM) system for incident tracking, change management, and configuration management. When incidents occur, engineers must context-switch between AWS, third party observability tools and ServiceNow, manually correlating data across those sources before updating ServiceNow incident records. This fragmented workflow delays resolution, increases mean time to resolution (MTTR), and introduces the risk of missed signals.

AWS DevOps Agent is a frontier agent that resolves and proactively helps prevent incidents, continuously improving reliability and performance of applications in AWS, and hybrid environments. In this post, we demonstrate how to integrate AWS DevOps Agent with ServiceNow using the Model Context Protocol (MCP) and ServiceNow Action Fabric, enabling autonomous incident investigation and resolution workflows that are governed by ServiceNow and that execute and record authorized actions directly on the application.

By the end of this post, you will be able to:

  • Configure AWS DevOps Agent as an MCP client connecting to ServiceNow MCP Server created in the MCP Server Console
  • Authenticate securely via OAuth 2.0 between AWS DevOps Agent and ServiceNow
  • Enable dynamic discovery of ServiceNow tools exposed through Action Fabric and governed through the ServiceNow MCP Server Console
  • Automate root cause analysis directly within ServiceNow incidents

Integrating ServiceNow MCP Server with AWS DevOps Agent

The integration between ServiceNow MCP Server and AWS DevOps Agent connects ITSM workflows with automated incident response through the Model Context Protocol (MCP), an open standard for AI agent-to-tool communication.

ServiceNow MCP Server Console lets you create a ServiceNow MCP Server and configure the tools it exposes, capabilities such as incident management, CMDB queries, and change requests as discoverable tools. The console governs what the agent can see and do through tool-level scoping, access control lists, and role masking. It is the access channel for ServiceNow Action Fabric, the application’s governed action layer: ServiceNow does not merely store the agent’s output, it controls and executes the actions the agent is authorized to perform.

AWS DevOps Agent acts as an MCP client that dynamically discovers available ServiceNow tools at runtime. You can create tools based on existing capabilities, such as ServiceNow NowAssist Skills.

When a ServiceNow incident triggers AWS DevOps Agent, the following happens:

  1. Correlates telemetry from Amazon CloudWatch, deployment data, and code changes
  2. Discovers available ServiceNow tools through the ServiceNow MCP Server
  3. Queries ServiceNow for related incidents, change records, and CMDB context
  4. Identifies root cause by correlating AWS telemetry with ServiceNow operational data
  5. Writes findings, root cause analysis, and mitigation plans directly into the ServiceNow incident
  6. Executes governed actions on the application (for example, creating a change request) through the tools the ServiceNow MCP Server Console exposes, where authorized

Security is built into every interaction. Communication uses OAuth 2.0 authentication with scoped

Permissions. The ServiceNow MCP Server Console governs which tools the agent can access and what actions it can perform, with every invocation authenticated, authorized at the tool and skill level, and recorded in an auditable trail that ServiceNow AI Control Tower can observe.

AWS DevOps Agent connecting to ServiceNow via MCP Server with OAuth 2.0

Figure 1: Integration architecture showing AWS DevOps Agent connecting to ServiceNow via MCP Server

Prerequisites

Before you begin, make sure you have access to and understanding of the following:

  • An AWS account with permissions to create AWS Identity and Access Management (IAM) roles:
  • Created AWS DevOps Agent Space role and Web app role
  • Access to AWS DevOps Agent
  • A ServiceNow instance with admin access
  • ServiceNow MCP Server configured and accessible ServiceNow MCP Server configured and accessible on an AI Native subscription (Foundation, Advanced, or Prime) or via the standalone MCP add-on

Step 1: Configure the ServiceNow MCP Server and its Tools in the MCP Server Console

As first step, configure the ServiceNow instance to expose capabilities through the MCP Server:

  1. Navigate to the MCP Server Console in the ServiceNow Instance
  2. Create a new MCP Server (or select the MCP server provisioned).

MCP Server Console in a ServiceNow instance

Figure 2: MCP Server Console in ServiceNow Instance 

  1. Add Tools for the capabilities the agent needs (for example, incident read and update, CMDB query, change request creation), and scope each with ACLs and role masking so the agent can perform only authorized actions.

Tool selection in the ServiceNow MCP Server Console

Figure 3: Tool selection in ServiceNow MCP Server

  1. Configure inbound authentication for the MCP Server.

Create Inbound Integration dialog with OAuth Client Credentials grant

Figure 4: Create Inbound Integration – OAuth Client Credentials grant

Step 2: Create and configure a DevOps Agent Space

Create an AWS DevOps Agent Space in your AWS account to define the scope of resources the agent will monitor and investigate:

  1. Access the AWS DevOps Agent console
  2. Choose Create Agent Space and provide a name and description, and configure the required IAM roles (automated or manual setup)

Create Agent Space workflow in the AWS DevOps Agent console

Figure 5: Creating an Agent Space in the AWS DevOps Agent console

Agent Space name and IAM role configuration

Figure 6: Agent Space Name and IAM role configuration

  1. Confirm creation of AWS DevOps Agent Space.

Step 3: Register ServiceNow MCP Server in the AWS DevOps Agent console

Register your ServiceNow MCP Server connection to enable tool discovery in the AWS DevOps Agent console.

  1. Navigate to Capability Providers in the AWS DevOps Agent console. Under MCP Server, select Add source, then Register New MCP Server.
  2. Enter your ServiceNow MCP Server endpoint URL:https://<instance>.service-now.com/sncapps/mcp-server/mcp/<server_label>

Register MCP Server dialog with ServiceNow endpoint URL

Figure 7: Entering the ServiceNow MCP Server endpoint URL

  1. Select OAuth Client Credentials as the authorization flow. Enter the Client ID, Client Secret, and Exchange URL (https://<instance>.service-now.com/oauth_token.do) from Step 1.

OAuth Client Credentials form with Client ID, Client Secret, and Exchange URL

Figure 8: OAuth Client Credentials configuration for the ServiceNow MCP Server

  1. Submit the registration. AWS DevOps Agent validates the connection and discovers available tools. Select the tools to add to your Agent Space.

Selecting discovered ServiceNow MCP tools to add to the Agent Space

Figure 9: Selecting ServiceNow MCP tools to add to the Agent Space

  1. Confirm the MCP Server is associated and tools are connected.

Putting It All Together: End-to-End Test

Once the setup is complete, we need to make sure the connection is working.

  1. Navigate to Operator Access in the AWS DevOps Agent Space.
  2. Open a new chat window, and type “Can you show me all the incident in the past week from ServiceNow”
  3. Make sure the Agent calls the ServiceNow tools and shows the right results.

Testing the ServiceNow MCP connection by querying recent incidents

Figure 10: Test the ServiceNow MCP connection from AWS DevOps Agent

You can also configure your environment so that the creation of an incident in ServiceNow automatically triggers the AWS DevOps Agent. To set up this integration, follow the AWS documentation to establish the connection between AWS DevOps Agent and your ServiceNow instance. Then, create a Business Rule in ServiceNow. This enables incident creation to seamlessly trigger the DevOps Agent without manual intervention.

Once this setup is complete, here’s how the workflow comes together: when an incident is created, the DevOps Agent automatically investigates and adds relevant context such as root cause analysis, related changes, and affected resources directly back into the incident record. This means that by the time your Operations or SRE team picks up the incident, they already have the context they need to begin resolution, significantly reducing triage time and accelerating mean time to recovery (MTTR).

ServiceNow console showing investigation kick off

Figure 11: AWS DevOps Agent initiating an automated investigation on the ServiceNow incident

ServiceNow console investigation complete

Figure 12: AWS DevOps Agent mitigation plan posted to the ServiceNow incident

Clean up

To avoid incurring ongoing costs, clean up your resources when you are done using the integration. For details on pricing, visit the AWS DevOps Agent pricing page.

When you are done using the integration, clean up your resources:

  1. Delete your Agent Space from the AWS DevOps Agent console
  2. Remove the ServiceNow MCP Server connection from your settings
  3. Delete the IAM roles created for the Agent Space
  4. (Optional) Disable the MCP Server configuration in your ServiceNow instance

Conclusion

For organizations running workloads on AWS and managing operations through ServiceNow, incident response has long meant toggling between systems and racing to document findings before context fades. The integration between AWS DevOps Agent and ServiceNow through MCP and Action Fabric alleviates that gap. The agent investigates autonomously, correlates telemetry with operational context, and documents root cause and mitigation directly in the incident record, compressing resolution times from hours to minutes.

And because the connection is built on MCP, an open protocol for agent-to-tool communication, what you configure today continues to expand as your ServiceNow workflows evolve. New tools exposed through Action Fabric are discovered and available to the agent immediately. To get started, visit the AWS DevOps Agent product page and ServiceNow MCP Server Console page.

Arunsingh Jeyasingh Jacob

Arunsingh Jeyasingh Jacob

Arunsingh Jeyasingh Jacob is a Senior Solutions Architect at AWS. He’s passionate about solving business and technology challenges as an AWS customer advocate, with his recent interest being AI strategy. When not at work, Arun enjoys listening to podcasts, going for short trail runs, and spending quality time with his family.

Govind Menon

Govind Menon

Govind Menon is the Head of MCP Product at ServiceNow. He is a Carnegie Mellon alum with a passion for building impactful products. Outside work, he orchestrates annual mystery trips for friends and hosts a one-day adaptations of CBS’s Survivor in San Francisco.

Jack Hwang

Jack Hwang

Jack Hwang is an Associate Solutions Architect at AWS, where he works with ISVs to design and optimize their workloads on AWS with a passion for AI innovation. Outside of work, Jack enjoys going for a run and spending time with his cat, Casper.

Route Amazon Bedrock Guardrails interventions to Amazon Security Lake

Post Syndicated from Dhananjay Karanjkar original https://aws.amazon.com/blogs/security/route-amazon-bedrock-guardrails-interventions-to-amazon-security-lake/

Security teams investigating AI-related incidents need guardrail intervention data alongside their existing security telemetry. Routing Amazon Bedrock Guardrails violations to Amazon Security Lake makes this possible. With this integration, you can query guardrail events alongside identity, network, and application security data in a single layer. When a guardrail blocks a prompt injection attempt or redacts sensitive data, that intervention carries investigative value comparable to a failed sign-in or a network intrusion alert. Amazon Bedrock publishes this telemetry to Amazon CloudWatch metrics and model invocation logs for operational monitoring. By using Security Lake, organizations can extend this telemetry into their security data lake for unified correlation.

In this post, I show you how to build an automated pipeline that transforms Amazon Bedrock Guardrails intervention events into Open Cybersecurity Schema Framework (OCSF) records and delivers them to Security Lake as a custom source. You can query the data using Amazon Athena or any Security Lake subscriber.

Use case

Consider a financial services organization deploying Amazon Bedrock across multiple business units. Each unit uses guardrails to enforce content policies (blocking harmful content), topic policies (preventing off-topic queries about competitors), sensitive information policies (redacting personally identifiable information (PII) such as account numbers), and prompt injection detection.

The security team needs to:

  • Identify which user accounts trigger the most guardrail interventions and whether those accounts also have unusual AWS Identity and Access Management (IAM) activity
  • Determine if prompt injection attempts correlate with specific source IP addresses that also appear in Amazon Virtual Private Cloud (Amazon VPC) Flow Logs
  • Track the organization-wide trend of guardrail violations across all business units and compare it against the baseline from 30 days ago

With guardrail events routed to Security Lake, a single Athena query covers all three.

Solution overview

The pipeline architecture routes Amazon Bedrock security events to Security Lake as OCSF-compliant records. The same infrastructure—subscription filter, AWS Lambda transformation, Parquet writer, Amazon Simple Storage Service (Amazon S3) partitioning—supports multiple event types by changing the filter pattern and OCSF mapping:

Guardrail interventions (this post) DETECTION_FINDING 2004
Model invocation API calls API_ACTIVITY 6003
Agent guardrail traces DETECTION_FINDING 2004
Token consumption anomalies DETECTION_FINDING 2004

This post demonstrates the guardrail interventions implementation as a working example. The solution captures Amazon Bedrock model invocation logs that contain guardrail trace data and filters for intervention events. It transforms matching events into OCSF-compliant Detection Finding records (class_uid 2004) and delivers them to Security Lake as Parquet files. Guardrail interventions are detection events: the guardrail detected and blocked prohibited content, so OCSF class 2004 (Detection Finding) under the Findings category is the appropriate classification.

Architecture

The following diagram shows the end-to-end pipeline from guardrail intervention to Security Lake ingestion.

Figure 1: Guardrail intervention routing

Figure 1: Guardrail intervention routing

The data flow consists of the following steps:

  1. An application calls Amazon Bedrock (InvokeModel or Converse API) with a guardrail attached.
  2. Amazon Bedrock evaluates the guardrail and logs the invocation (including guardrail trace data) to a CloudWatch Logs log group using model invocation logging. The subscription filter matches log entries where the guardrail action is INTERVENED (blocked or masked content).
  3. The subscription filter delivers matching records to a Lambda function (OCSF Transform).
  4. The Lambda function transforms each intervention event into an OCSF Detection Finding record (class_uid 2004), batches records, and converts them to Zstandard (zstd)-compressed Apache Parquet format. It writes the Parquet file to the Amazon S3 Security Lake bucket using the required partition path (ext/BedrockGuardrails/region=/accountId=/eventDay=/). If the Lambda function fails to process a record, the message routes to an Amazon Simple Queue Service (Amazon SQS) dead-letter queue for later analysis and redrive.
  5. Security Lake manages the ingested Parquet data in the S3 bucket.
  6. AWS Glue crawler detects new partitions and catalogs the Parquet files for query access.
  7. SOC analysts query guardrail violation data alongside other security sources using Athena.

OCSF mapping

The following table shows how Amazon Bedrock Guardrails intervention fields map to OCSF Detection Finding (class_uid 2004) attributes.

OCSF field Source Example value
class_uid Static 2004 (Detection Finding)
category_uid Static 2 (Findings)
severity_id Derived from policy type 3 (Medium) for content/topic; 4 (High) for prompt injection
activity_id Static 1 (Create)
time Invocation log timestamp 1721001600000
cloud.provider Static AWS
cloud.region Invocation log region us-east-1
cloud.account.uid Invocation log accountId 123456789012
actor.user.uid Invocation log identity.arn arn:aws:sts::123456789012:assumed-role/AppRole/session
finding_info.title Derived from policy type ContentPolicy Intervention
finding_info.desc Guardrail trace action/topic Blocked: HATE content detected on INPUT
resource.uid Model ARN arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-sonnet-4-6-20250514-v1:0
resource.type Static AwsBedrock:Model
metadata.product.name Static Amazon Bedrock Guardrails
metadata.product.vendor_name Static AWS
metadata.version Static 1.3.0
unmapped.guardrail_id Guardrail trace guardrailId my-content-guardrail
unmapped.guardrail_arn Guardrail trace guardrailArn arn:aws:bedrock:us-east-1:123456789012:guardrail/abc123
unmapped.guardrail_version Guardrail trace guardrailVersion 3
unmapped.guardrail_content_source Guardrail trace INPUT or OUTPUT
unmapped.guardrail_policy_type Guardrail trace ContentPolicy, TopicPolicy, SensitiveInformationPolicy, WordPolicy, ContextualGroundingPolicy, PromptAttack

Prerequisites

The following prerequisites are needed to deploy the reference implementation. Before you begin, clone the repository:

git clone https://github.com/aws-samples/sample-bedrock-guardrails-security-lake.git
cd sample-bedrock-guardrails-security-lake

Verify you have the following:

  • An AWS account with AWS Cloud Development Kit (AWS CDK) bootstrapped in the target AWS Region
  • Security Lake enabled in the target Region
  • Python 3.12 or later
  • Node.js 20 or later (for AWS CDK CLI)
  • An existing Amazon Bedrock guardrail (or create one during deployment)
  • Model invocation logging enabled on Amazon Bedrock (with guardrail trace data enabled)

Implementation

The reference implementation deploys three CloudFormation stacks: SecurityLakeSourceStack, TransformPipelineStack and MonitoringStack. The following commands deploy the stacks in dependency order:

cdk deploy SecurityLakeSourceStack \
  -c security_lake_bucket=<your-security-lake-bucket> \
  -c source_location=ext/BedrockGuardrails \
  -c security_lake_enabled=true

cdk deploy TransformPipelineStack \
  -c security_lake_bucket=<your-security-lake-bucket> \
  -c source_location=ext/BedrockGuardrails

cdk deploy MonitoringStack \
  -c security_lake_bucket=<your-security-lake-bucket> \
  -c source_location=ext/BedrockGuardrails

Enable model invocation logging

Model invocation logging captures the guardrail trace data you need. Turn on full request and response logging to a CloudWatch Logs log group. Configure textDataDeliveryEnabled to capture text request and response bodies, which include the guardrail trace output when a guardrail is attached to the invocation.

Register Security Lake custom source

Register BedrockGuardrails as a custom source with Security Lake using the DETECTION_FINDING event class. Security Lake creates the Amazon S3 prefix and IAM role for your source. The stack configures the AWS Glue crawler role for partition discovery.

Create the subscription filter

Create a CloudWatch Logs subscription filter on your model invocation log group with the filter pattern { $.output.guardrailAction = “INTERVENED” }. This captures only the events where a guardrail blocked or modified content, not the successful pass-through events. This reduces Lambda invocations and cost.

Transform to OCSF and write Parquet

The Lambda function performs three operations: parse the CloudWatch Logs event, transform each intervention to an OCSF Detection Finding record (class_uid 2004), and write batched records as Parquet files. The files are written to the Security Lake S3 bucket using the required partition path (ext/BedrockGuardrails/region=<region>/accountId=<accountId>/eventDay=<YYYYMMDD>/).

The transformation maps guardrail trace fields to OCSF attributes as described in the OCSF mapping table. Severity is set to High for prompt injection interventions and Medium for content, topic, or sensitive information interventions. For a concrete before-and-after example, see the sample invocation log and corresponding OCSF output in the companion repository.

Scaling considerations: At low intervention volumes (tens of events per hour), direct Lambda writes produce acceptably sized Parquet files. For higher volumes, consider buffering through Amazon Data Firehose with its native Parquet conversion and 5-minute buffering interval to produce fewer, larger files that optimize Athena query performance.

Multi-account deployment: The partition scheme (accountId=<account>) already supports multi-account environments. Deploy the subscription filter and transform pipeline in each workload account where model invocation logging is enabled. Each pipeline writes cross-account to the delegated-administrator Security Lake bucket. Distribute the pipeline using CloudFormation StackSets across the organization.

Query violations in Athena

After deployment, guardrail violations typically appear in your Security Lake tables within 5–10 minutes, depending on the AWS Glue crawler schedule. You can then run cross-service correlation queries. The following example identifies users who trigger both prompt injection interventions and unusual IAM activity:

WITH guardrail_violators AS (
    SELECT actor.user.uid AS user_arn, COUNT(*) AS violation_count
    FROM "amazon_security_lake_glue_db_us_east_1"."amazon_security_lake_table_us_east_1_bedrockguardrails"
    WHERE eventDay >= '20260701'
      AND unmapped.guardrail_policy_type = 'PromptAttack'
    GROUP BY actor.user.uid
),
iam_failures AS (
    SELECT actor.user.uid AS user_arn, COUNT(*) AS failure_count
    FROM "amazon_security_lake_glue_db_us_east_1"."amazon_security_lake_table_us_east_1_cloud_trail_mgmt_2_0"
    WHERE eventDay >= '20260701'
      AND status_id = 2
    GROUP BY actor.user.uid
)
SELECT g.user_arn, g.violation_count, i.failure_count
FROM guardrail_violators g
JOIN iam_failures i ON g.user_arn = i.user_arn
ORDER BY g.violation_count DESC;

You can also track violation trends by policy type over time to establish baselines and detect spikes. The following query shows the 30-day trend:

SELECT eventDay,
       unmapped.guardrail_policy_type AS policy_type,
       COUNT(*) AS violation_count
FROM "amazon_security_lake_glue_db_us_east_1"."amazon_security_lake_table_us_east_1_bedrockguardrails"
WHERE eventDay >= '20260623'
GROUP BY eventDay, unmapped.guardrail_policy_type
ORDER BY eventDay, violation_count DESC;

The OCSF mapping has been validated against schema version 1.3.0, and the Security Lake AWS Glue crawler correctly detects the partitioned Parquet files for querying.

Alternative for teams not yet using Security Lake: If your organization hasn’t adopted Security Lake, you can query guardrail intervention events directly in CloudWatch Logs Insights using the same subscription filter log group. CloudWatch Logs Insights supports cross-log-group queries, so you can correlate guardrail events with other CloudWatch log sources without the OCSF transformation step. Security Lake adds value when you need to join with non-CloudWatch sources in a single query layer. Examples include Amazon VPC Flow Logs, Amazon Route 53 DNS logs, and third-party findings.

Clean up

To avoid ongoing charges, destroy the stacks in reverse dependency order:

cdk destroy MonitoringStack --force \
  -c security_lake_bucket=<your-security-lake-bucket> \
  -c source_location=ext/BedrockGuardrails

cdk destroy TransformPipelineStack --force \
  -c security_lake_bucket=<your-security-lake-bucket> \
  -c source_location=ext/BedrockGuardrails

cdk destroy SecurityLakeSourceStack --force \
  -c security_lake_bucket=<your-security-lake-bucket> \
  -c source_location=ext/BedrockGuardrails \
  -c security_lake_enabled=true

Conclusion

In this post, you learned how to route Amazon Bedrock Guardrails intervention events to Amazon Security Lake as OCSF-compliant Detection Finding records. This integration extends guardrail telemetry from Amazon CloudWatch into your security data lake. Security analysts can then run cross-service correlation of AI intervention events with IAM, network, and application telemetry.

The pipeline filters for intervention events only, keeping costs low while capturing the security-relevant signals. The records use OCSF event class 2004 (Detection Finding), which integrates with supported Security Lake subscribers such as Amazon OpenSearch Service and third-party SIEM tools.

Clone the reference implementation and adapt the OCSF mapping and subscription filter to your organization’s guardrail configuration.

References

If you have feedback about this post, submit comments in the Comments section below.


Dhananjay Karanjkar

Dhananjay Karanjkar

Dhananjay is a Senior Lead Consultant at AWS Professional Services, specializing in agentic AI systems, multi-agent orchestration, and generative AI security. He holds two US patents and serves as a Responsible AI Champion, with a background spanning financial services, enterprise consulting, and enterprise-scale AI delivery. When not architecting AI solutions, he trains for triathlons, paints oil portraits, and is an avid reader.

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