How Google is preparing for a post-quantum world

The National Institute of Standards and Technology (NIST) on Tuesday announced the completion of the third round of the Post-Quantum Cryptography (PQC) standardization process, and we are pleased to share that a submission (SPHINCS+) with Google’s involvement was selected for standardization. Two submissions (Classic McEliece, BIKE) are being considered for the next round. We want to congratulate the Googlers involved in the submissions (Stefan Kölbl, Rafael Misoczki, and Christiane Peters) and thank Sophie Schmieg for moving PQC efforts forward at Google. We would also like to congratulate all the participants and thank NIST for their dedication to advancing these important issues for the entire ecosystem.This work is incredibly important as we continue to advance quantum computing. Large-scale quantum computers will be powerful enough to break most public-key cryptosystems currently in use and compromise digital communications on the Internet and elsewhere. The goal of PQC is to develop cryptographic systems that safeguard against these potential threats, and NIST’s announcement is a critical step toward that goal. Governments in particular are in a race to secure information because foreign adversaries can harvest sensitive information now and decrypt it later.  At Google, our work on PQC is focused on four areas: 1) driving industry contributions to standards bodies;  2) moving the ecosystem beyond theory and into practice (primarily through testing PQC algorithms); 3) taking action to ensure that Google is PQC ready; and 4) helping customers manage the transition to PQC. Driving industry contributions to a range of standards bodies In addition to our work with NIST, we continue to drive industry contributions to international standards bodies to help advance PQC standards. This includes ISO 14888-4, where Googlers are the editors for a standard on stateful hash-based signatures. More recently, we also contributed to the IETF proposal on data formats, which will define JSON and CBOR serialization formats for PQC digital signature schemes. These standards, collectively, will enable large organizations to build PQC solutions that are compatible and ease the transition globally.Moving the ecosystem beyond theory and into practice: Testing PQC algorithmsWe’ve been working with the security community for over a decade to explore options for PQC algorithms beyond theoretical implementations. We announced in 2016 an experiment in Chrome where a small fraction of connections between desktop Chrome and Google’s servers used a post-quantum key-exchange algorithm, in addition to the elliptic-curve key-exchange algorithm that would typically be used. By adding a post-quantum algorithm in a hybrid mode with the existing key-exchange, we were able to test its implementation without affecting user security. We took this work further in 2019 and announced a wide-scale post-quantum experiment with Cloudflare. We worked together to implement two post-quantum key exchanges, integrated them into Cloudflare’s TLS stack, and deployed the implementation on edge servers and in Chrome Canary clients. Through this work, we learned more about the performance and feasibility of deployment in TLS of two post-quantum key agreements, and have continued to integrate these learnings into our technology roadmap.  In 2021, we tested broader deployment of post-quantum confidentiality in TLS and discovered a range of network products that were incompatible with post-quantum TLS. We were able to work with the vendor so that the issue was fixed in future firmware updates. By experimenting early, we resolved this issue for future deployments.Taking action to ensure that Google is PQC readyAt Google, we’re well into a multi-year effort to migrate to post-quantum cryptography that is designed to address both immediate and long-term risks to protect sensitive information. We have one goal: ensure that Google is PQC ready. Internally, this effort has several key priorities, including securing asymmetric encryption, in particular encryption in transit. This means using ALTS, for which we are using a hybrid key-exchange, to secure internal traffic; and using TLS (consistent with NIST standards) for external traffic. A second priority is securing signatures in the case of hard-to-change public keys or keys with a long lifetime, in particular focusing on hardware, especially hardware deployed outside of Google’s control. We’re also focused on sharing the information we learn to help others address PQC challenges. For example, we recently published a paper that includes PQC transition timelines, leading strategies to protect systems against quantum attacks, and approaches for combining pre-quantum cryptography with PQC to minimize transition risks. The paper also suggests standards to start experimenting with now and provides a series of other recommendations to allow organizations to achieve a smooth and timely PQC transition. Helping customers manage the transition to PQCAt Google Cloud, we are working with many large enterprises to ensure they are crypto-agile and to help them prepare for the PQC transition. We fully expect customers to turn to us for post-quantum cloud capabilities, and we will be ready. We are committed to supporting their PQC transition with a range of Google products, services, and infrastructure. As we make progress, we will continue to provide more PQC updates on Google core, cloud, and other services, and updates will also come from Android, Chrome and other teams. We will further support our customers with Google Cloud transformation partners like the Google Cybersecurity Action Team to help provide deep technical expertise on PQC topics. Additional References:Google Cloud Security Foundations GuideGoogle Cloud Architecture Framework Google infrastructure security design overviewRelated ArticleRead Article
Quelle: Google Cloud Platform

Amazon Connect unterstützt jetzt die Verzweigung von Flows basierend auf Lex-Zuversichtlichkeitsbewertungen

Amazon Connect ermöglicht Ihnen jetzt die weitere Personalisierung der Self-Service-Kundenerfahrung mithilfe von Amazon Lex-Absichts-Zuversichtlichkeitsbewertungen als Verzweigung in Ihren Flows. Amazon Lex ermöglicht Kunden die Erstellung intelligenter Chatbots, mit denen ihre Amazon Connect-Flows in natürliche Unterhaltungen verwandelt werden können. Durch die Verzweigung von Flows basierend auf Lex-Zuversichtlichkeitsbewertungen können Sie Ihren Kunden schneller die richtigen Lösungen für ihre Probleme anbieten. Bei einer hohen Zuversichtlichkeitsbewertung können Sie Kunden beispielsweise sofort eine Self-Service-Option präsentieren, statt zusätzliche Informationen anzufordern oder sie an einen Kundendienstmitarbeiter weiterzuleiten. Diese neue Funktion kann über den Flow-Block „Kontaktattribute prüfen“ eingerichtet werden.
Quelle: aws.amazon.com

Amazon Pinpoint führt Journey-Planung zur präziseren Kommunikationsübermittlung ein

Journeys in Amazon Pinpoint gestatten es Kunden jetzt, einen Zeitplan für Channel-Mitteilungen basierend auf dem Wochentag und Tag im Jahr zu definieren. Darüber hinaus wurden zwei neue Journey-Versandlimits in Amazon Pinpoint hinzugefügt, mit denen Kunden das an Benutzer versandte Mitteilungsvolumen steuern können. Amazon Pinpoint-Journeys sind Mehrschrittkampagnen, die Benutzer basierend auf ihren Aktionen oder Attributen auf Kommunikationspfade leiten. Journeys können mehrere Channel verwenden, darunter SMS, E-Mail, Push und Voice. Journeys sind für Kunden gedacht, die Anwendungsfälle für die Benutzerbindung haben und gezielte Mitteilungen senden möchten, die zu hochwertigen Benutzeraktionen führen.
Quelle: aws.amazon.com

Share WordPress.com Blog Posts to Telegram With WordPressDotCom Bot

Ever wished you could share new blog posts to a Telegram channel automatically?If so, then great news! Starting today, you can follow any WordPress.com blog within Telegram by using WordPressDotCom Bot. Now, you can easily get real-time updates from your favorite bloggers in specific channels, group chats, or DMs, right in the app. All with just a few clicks.

Sharing WordPress.com Blog Posts Made Easy

Whether you want to follow your favorite blogs or share your own posts, the WordPress.com Bot has you covered.Start a new channel and invite readers to follow you there. Or automate sharing with an existing channel and add value for your friends and followers. Here are some ideas for inspiration:

Keep up-to-date with writers you don’t want to miss.Share the latest shots from your family photo blog with friends and loved ones.Spark conversation about your hobbies and interests in a private channel.

How to Use the WordPress.com Bot for Telegram

There are two ways to set up WordPressDotCom Bot: 

Visit https://t.me/WordPressDotComBot and click on Send Message. Or invite the bot to a channel or group by adding a new member and searching for WordPress.com

From there, here are a handful of commands you can use:

/follow [url] -This will follow the blog specified in the url/unfollow  [url] -This will unfollow the blog specified in the url. No more notifications of new posts will be sent./following – This will show a list of all blogs followed./reset – This stops following all blogs via the Telegram bot.

Best of all, WordPress.com Bot shows off your post thumbnails. Take a look at this preview example:

Try WordPress.com Bot for Telegram Today

WordPress.com Bot is free to use. Try it now.  
Quelle: RedHat Stack

AI Booster: how Vodafone is supercharging AI & ML at scale

One of the largest telecommunications companies in the world, Vodafone is at the forefront of building next-generation connectivity and a sustainable digital future.  Creating this digital future requires going beyond what’s possible today and unlocking significant investment in new technology and change. For Vodafone, a key driver is the use of artificial intelligence (AI) and machine learning (ML), enabling predictive capabilities in enhancing the customer experience, improving network performance, accelerating advances in research, and much more. Following 18 months of hard work, Vodafone has made a huge leap forward in advancing its AI capabilities at scale with the launch of its “AI Booster” AI / ML platform. Led by the Global Big Data & AI organization under Vodafone Commercial, the platform will use the latest Google technology to enable the next generation of AI use cases, such as optimizing customer experiences, customer loyalty, and product recommendations. Vodafone’s Commercial team has long focused on advancing its AI and ML capabilities to drive business results. Yet as demand grows, it is easier said than done to embed AI and ML into the fabric of the organization and rapidly build and deploy ML use cases at scale in a highly regulated industry. Accomplishing this task means not only having the right platform infrastructure, but also developing new skills, ways of working, and processes. Having made meaningful strides in extracting value from data by moving it into a single source of truth on Google Cloud, Vodafone had already significantly increased efficiency, reduced data costs, and improved data quality. This enabled a plethora of use cases that generate business value using analytics and data science. The next step was building industrial scale ML capability, capable of handling thousands of ML models a day across 18+ countries, while streamlining data science processes and keeping up with technological growth. Knowing they had to do something drastically different to scale successfully, along came the idea for AI Booster. “To maximize business value at pace and scale, our vision was to enable fast creation and horizontal / vertical scaling of use cases in an automated, standardized manner. To do this, 18 months ago we set out to build a next-generation AI / ML platform based on new Google technology, some of which hadn’t even been announced yet. “We knew it wouldn’t be easy. People said, ‘Shoot for the stars and you might get off the ground…’ Today, we’re really proud that AI Booster is truly taking off, and went live in almost double the markets we had originally planned. Together, we’ve used the best possible ML Ops tools and created Vodafone’s “AI Booster Platform” to make data scientists’ lives easier, maximise value and take co-creation and scaling of use cases globally to another level,” says Cornelia Schaurecker, Global Group Director for Big Data & AI at Vodafone. AI Booster: a scalable, unified ML platform built entirely on Google CloudGoogle’s Vertex AI lets customers build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified platform. Built upon Vertex AI, Vodafone’s AI Booster is a fully managed cloud-native platform that integrates seamlessly with Vodafone’s Neuron platform, a data ocean built on Google Cloud. “As a technology platform, we’re incredibly proud of building a cutting-edge MLOps platform based on best-in-class Google Cloud architecture with in-built automation, scalability and security. The result is we’re delivering more value from data science, while embedding reliability engineering principles throughout,” comments Ashish Vijayvargia, Analytics Product Lead at VodafoneIndeed, while Vertex AI is at the core of the platform, it’s much more than that. With tools like Cloud Build and Artifact Registry for CI/CD, and Cloud Functions for automatically triggering Vertex Pipelines, automation is at the heart of driving efficiency and reducing operational overhead and deployment times. Today, users simply complete an online form, and then, within minutes, receive a fully functional AI Booster environment with all the right guardrails, controls, and approvals. Not long ago it could take months to move a model from a proof of concept (PoC) to launching live in production. By focusing on ML operations (MLOps), the entire ML journey is now more cost-effective, faster, and flexible, all without compromising security. PoC-to-production can now be as little as four weeks, an 80% reduction.Diving a bit deeper, Vodafone’s AI Booster Product Manager, Sebastian Mathalikunnel, summarizes key features of the platform: “Our overarching vision was a single ML platform-as-a-service that scales horizontally (business use cases across markets) and vertically (from PoC to Production). For this, we needed innovative solutions to make it both technically and commercially feasible. Selecting a few highlights, we: completely automated ML lifecycle compliance activities (drift / skew detection, explainability, auditability, etc.) via reusable pipelines, containers, and managed services; embedded security by design into the heart of the platform;capitalized on Google-native ML tooling using BQML, AutoML, Vertex AI and others;accelerated adoption through standardized and embedded ML templates.”For the last point, Datatonic, a Google Cloud data and AI partner, was instrumental in building reusable MLOps Turbo Templates, a reference implementation of Vertex Pipelines, to accelerate building a production-ready MLOps solution on Google Cloud.  “Our team is devoted to solving complex challenges with data and AI, in a scalable way. From the start, we knew the extent of change Vodafone was embarking on with AI Booster. Through this open-source codebase, we’ve created a common standard for deploying ML models at scale on Google Cloud. The benefit to one data scientist alone is significant, so scaling this across hundreds of data scientists can really change the business,” says Jamie Curtis, Datatonic’s Practice Lead for MLOps.  Reimagining the data scientist & machine learning engineer experience With the new technology platform in place, driving adoption across geographies and markets is the next challenge. The technology and process changes have a considerable impact on people’s roles, learning, and ways of working. For data scientists, non-core work now is supported by machines in the background—literally at the click of a button. They can spend time doing what they do best and discovering new tools to help them do the job. With AI Booster, data scientists and ML engineers have already started to drive greater value and collaborate on innovative solutions. Supported by instructor-led and on-demand learning paths with Google Cloud, AI Booster is also shaping a culture of experimentation and learning. Together We Can Eighteen months in the making, AI Booster would not have happened without the dedication of teams across Vodafone, Datatonic, and Google Cloud. Googlers from across the globe were engaged in supporting Vodafone’s journey and continue to help build the next evolution of the platform. Cornelia highlights that “all of this was only possible due to the incredible technology and teams at Vodafone and Google Cloud, who were flexible in listening to our requirements and even tweaking their products as a result. Alongside our ‘Spirit of Vodafone,’ which encourages experimenting and adapting fast, we’re able to optimize value for our customers and business. A huge thank you also to Datatonic, who were a critical partner throughout this journey and to Intel for their valuable funding contribution.” The Google & Vodafone partnership continues to go from strength to strength, and together, we are accelerating the digital future and finding new ways to keep people connected. “Vodafone’s flourishing relationship with Google Cloud is a vital aspect of our evolution toward becoming a world-leading tech communications company. It accelerates our ability to create faster, more scalable solutions to business challenges like improving customer loyalty and enhancing customer experience, whilst keeping Vodafone at the forefront of AI and data science,” says Cengiz Ucbenli, Global Head of Big Data and AI, Innovation, Governance at Vodafone. Find out more about the work Google Cloud is doing to help Vodafone here, and to learn more about how Vertex AI capabilities continue to evolve, read about our recent Applied ML Summit.Related ArticleAccelerating ML with Vertex AI: From retail and finance to manufacturing and automotiveHow businesses across industries are accelerating deployment of machine learning models into production with VertexAI.Read Article
Quelle: Google Cloud Platform