• Sumo Logic Inc. has recently developed features that forecast application, cloud, infrastructure consumption, and resource demands based on previous data.
• Hundreds of businesses use the cloud-based data analytics software from Sumo Logic to gain insights into the state of their IT infrastructure.

Sumo Logic Inc., the creator of an analytics-based platform for application performance management and observability, has recently developed features that forecast application, cloud, infrastructure consumption, and resource demands based on previous data.

It is also increasing its support for the OpenTelemetry set of tools, application programming interfaces, and software development kits for instrumenting, generating, collecting, and exporting telemetry data.

Hundreds of businesses use the cloud-based data analytics software from Sumo Logic to gain insights into the state of their IT infrastructure. Its software includes log management, cloud monitoring, software container management, microservices, and cloud security.

Full-stack predictions

Sumo claims to be the first and only full-stack observability platform that delivers predictive analytics for metrics, events, logs, and traces, the essential data components of observability. The service is designed to reduce resource constraints and unanticipated system burdens while mitigating the uncertainty that fluctuating cloud usage introduces to capacity management.

According to the company, Predict for Metrics employs linear and autoregressive models to make predictions by leveraging historical data points to forecast future trends. Metrics query language operators let users view anticipated numbers and integrate them into Sumo Logic dashboards.

Sumo Logic already has predictive capabilities for logs, the company’s native data type. Erez Barak, the company’s General Manager and Vice President of engineering, said, “Our origin was in logs, so that made it easier for us to foray into the prediction world. Since we have that baseline of log data, we can establish a baseline for metrics as well.”

Barak stated that the company has obtained an acceptable level of accuracy in internal evaluations and with early adopters. Although Sumo Logic employs aggregated and anonymous data to enhance the quality of its overall predictions, he stated that, “we would never have a situation where one customer’s data is used for another customer.”

The feature may also be used to predict how many Sumo Logic credits will be utilized, saving users from unforeseen costs. It may also do analytics on application performance management trace data to forecast the load on an application or its underlying microservice, allowing customers to better estimate how much CPU, memory, and storage space to supply across Amazon Web Services Inc.

Organizations can now also determine which resources, such as those provisioned for AWS DynamoDB or Provisioned Memory for AWS Lambda functions, will run out of capacity.

Aboard the OpenTelemetry Express

Sumo Logic announced this week at the KubeCon + CloudNativeCon event that it has streamlined the process of getting customers up and running on its platform and added support for the Windows operating system to its Distribution for OpenTelemetry Collector.

More than 30 applications are currently using OpenTelemetry for database, server, and infrastructure monitoring, according to the company. Sumo Logic Distro for OT is a product that can capture telemetry data from MacOS, Linux, and Windows platforms using a singular collector.

OpenTelemetry is acquiring traction as a means for organizations to standardize their observability practices and monitor metrics, logs, and traces collectively instead of separately. Despite the fact that most observability vendors support OpenTelemetry to some extent, Barak claimed that some need open data to be combined with proprietary data types, necessitating users to maintain multiple back ends and increasing the risk of vendor lock-in.

On the other hand, Sumo Logic claims it offers a method for ingesting OpenTelemetry data via a single installation, which helps reduce the various manual data integration steps to a single workflow that can be executed in less than five minutes. The unified agent also facilitates the consolidation of observability onto a single platform.

Barak said, “We’re leaning into this by taking our most important workflows and making sure they work out of the box with OpenTelemetry data. In the last six to 12 months we’re seeing more and more customers saying their future is OTel. That has become their No. 1 driver of strategy and tool consolidation and also for pulling in developers.”

Open Telemetry is the second fastest-growing project in the Cloud Native Computing Foundations ecosystem, after Kubernetes, as measured by the number of developer contributions.

• Massive advances in AI technology are being driven by the emergence of accelerated computing, which is forcing businesses across industries to rethink their products and business models fundamentally.
• According to Deloitte, generative AI has opened up a wide range of new market applications and can significantly increase business productivity.

Deloitte Touche Tohmatsu Ltd., an information technology consulting company, announced recently that it is launching a new practice to assist businesses in leveraging generative artificial intelligence, the hottest trend in the sector.

Because it can create new content based on brief text descriptions, this trend has recently taken over the news. Generative AI is powered by a new generation of chatbots and virtual assistants, most notably OpenAI LP’s ChatGPT, which can converse, create artwork, write code, and more.

Businesses are eager to adopt the generative AI trend and learn how the technology can improve their operational efficiency and financial performance. Still, with so much discussion surrounding the subject, figuring out where to begin can take time. Deloitte believes it can assist in this area by giving enterprise leaders the deep AI industry experience they require to develop their generative AI strategies.

According to Deloitte, generative AI has opened up a slew of new industry applications and has the potential to greatly boost company efficiency. While developing, implementing, and operationalizing new applications based on fundamental AI models, many people require assistance.

The new practice at Deloitte will be dedicated to assisting businesses in implementing both custom-built solutions and those provided by third parties. The Generative AI Market Incubator, a group of engineers devoted to fast designing and launching generative AI pilot applications, is one of its major components.

In order to train and improve foundation models, Deloitte also established a research and development team that will collaborate with its alliance partners, the company said. Due to Deloitte’s early adoption of generative AI and its acquisitions of startups like HashedIn Technologies and Intellify Inc., both teams are said to have extensive experience in AI and the cloud.

Additionally, the new practice will collaborate with the Deloitte AI Academy. Its purpose is to train thousands of people in various new AI skills, including model development and prompt engineering, and to close the talent gap in AI.

Deloitte cited its most recent AI Dossier report, which outlines some situations in which generative AI can be used to good effect almost immediately. They include, among other things, supply chain optimization, fraud detection, and smart factories. With the help of its new practice, clients will be helped as they deploy applications in these fields and as they navigate ethical, legal, and policy issues.

Holger Mueller, an analyst with Constellation Research Inc., argued that Deloitte made a wise decision by taking this action because businesses will require assistance in implementing and maintaining the most recent AI tools.

Mueller added, “Of course, that help is going to come from the traditional system integrators like Deloitte, which is starting with an incubator. It is land grab time for the AI services category as many things are in flux.”

The cliche that “generative AI is transforming the way we work” was echoed by Jason Girzadas, managing principal of businesses, global, and strategic services at Deloitte U.S. and the firm’s incoming Chief Executive. Deloitte is prepared to assist its clients as they “develop and deploy new and innovative AI-fueled solutions,” he said, as businesses look to adopt the trend.

• Developers can save a lot of time and money by using pre-trained foundation models instead of having to start from scratch when training a language model.
• The first is a generative LLM for information extraction, open-ended question and answer, classification, text generation, and summarization.

Amazon Web Services Inc. has recently expanded its reach into artificial intelligence software development by releasing several new tools for generative AI training and deployment on its cloud platform.

The business described new services in a post on the AWS Machine Learning blog, including the capacity to build and train foundation models, which are extensive, pre-trained language models that lay the groundwork for particular natural language processing tasks.

Deep learning techniques are generally used to train foundation models on enormous volumes of text data, enabling them to become adept at understanding the subtleties of human language and produce content nearly indistinguishable from that written by humans.

When training a language model, developers can save time and money using pre-trained foundation models instead of starting from scratch. A foundation model for text generation, sentiment analysis, and language translation is the Generative Pre-trained Transformer (GPT) from OpenAI LLC.

LLM Choices

Bedrock’s brand-new service makes foundation models from various sources accessible through an API. The Jurassic-2 multilingual large language models from AI21 Labs Ltd., which produce text in Spanish, French, German, Portuguese, Italian, and Dutch, and Anthropic’s PBC’s Claude LLM, a conversational and text processing system that follows moral AI system training principles are included. Users can use the API to access Stability AI Ltd. and Amazon LLMs.

According to Swami Sivasubramanian, Vice President of database, analytics, and machine learning at AWS, foundation models are pre-trained at the internet scale. They can therefore be customized with comparatively little additional training. He used the example of a fashion retailer’s content marketing manager, who could give Bedrock as few as 20 examples of effective taglines from past campaign examples with relevant product descriptions. Bedrock will then automatically generate effective social media posts, display ad images, and web copy for the new products.

In addition to the Bedrock announcement, AWS is releasing two new Titan large language models. The first is a generative LLM for information extraction, open-ended question and answer, classification, text generation, and summarization. The second LLM converts text prompts into numerical representations, including the meaning of the text and helps build contextual responses beyond paraphrasing.

No mention of OpenAI, in which Microsoft Corp. is a significant investor, was made in the announcement. Still, given the market’s demand for substantial language models, this shouldn’t be a problem for Amazon.

Although AWS is behind Microsoft and Google LLC in bringing its LLM to market, Kandaswamy argued that this shouldn’t be considered a competitive disadvantage. He said, “I don’t think anyone is so behind that they have to play catchup. It might appear that there is a big race, but the customers we speak with, other than very early adopters, have no idea what to do with it.”

Hardware Boost

Additionally, AWS is upgrading its hardware to provide training and inference on its cloud. New, network-optimized EC2 Trn1n instances now offer 1,600 gigabits per second of network bandwidth, or about a 20% performance increase, and feature the company’s exclusive Trainium and Inferentia2 processors. Additionally, the business’s Inf2 instances, which use Inferentia2 for inferencing of massively multi-parameter generative AI applications, are now generally available.

CodeWhisperer, an AI coding companion that uses a foundation model to produce code suggestions in real-time based on previous code and natural language comments in an integrated development environment, is another product whose availability has been announced. The tool is accessible from some IDEs and supports Python, Java, JavaScript, TypeScript, C#, and ten other languages.

Sivasubramanian wrote, “Developers can simply tell CodeWhisperer to do a task, such as ‘parse a CSV string of songs’ and ask it to return a structured list based on values such as artist, title and highest chart rank.” CodeWhisperer produces “an entire function that parses the string and returns the list as specified.” He said that developers who used the preview version reported improvement of 57% in speed with a 27% higher success rate.

As many players attempt to capitalize on the success of proofs of a concept like ChatGPT, the LLM landscape will likely remain dispersed and chaotic for the foreseeable future. As Google’s Natural Language API has in speech recognition, it’s unlikely that any one model will come to dominate the market, according to Kandaswamy.

He said, “Just because a model is good at one thing doesn’t mean it’s going to be good with everything. It’s possible over two or three years everybody will offer everybody else’s model. There will be more blending and cross-technology relationships.”

• According to Dean Guida, founder and CEO of the 34-year-old Infragistics, Slingshot was introduced about a year ago after five years of development
• The company offers templates for personalized work plans utilizing well-known programs like Intuit Inc.’s QuickBooks and Google Analytics, and workspace creation is a drag-and-drop proposition.

Infragistics Corporation, a manufacturer of development tools, has announced the inclusion of data cataloging capabilities in its Slingshot collaboration software.

According to the company, non-technical users can use the catalog to unify data across an organization and generate visual dashboards. The Slingshot data catalog is compatible with numerous data sources, such as the most prevalent databases, spreadsheets, cloud data warehouses, and software-as-a-service applications.

Dean Guida, Founder and CEO of the 34-year-old Infragistics, said the device was created with security and collaboration in mind. He added, “We connect to content stores like SharePoint, Dropbox, and OneDrive and inherit their permissions. You can pre-define who has access or people can request access on demand.” He further said that works in progress may be selectively shared with clients and business partners without jeopardizing access credentials and at no extra cost.

In The Works for Five Years

According to Guida, Slingshot was released around a year ago after five years of work. Infragistics, which produces a variety of application design and development tools used by more than 2 million developers, introduced Slingshot about a year ago. Within the next 12 months, the company is projected to have between 500,000 and 700,000 active consumers.

According to Guida, the platform and the data catalog are designed for simplicity. He added, “Data catalogs have always been for IT people and data architects. We built this for business people so you can search across your company and discover all the data without having to wait for a business analyst to create a dashboard for you.” Instead of using the standard descriptions available in the source data, users can curate the catalog and apply their field names.

According to Guida, a selection of prefabricated connectors are available, and new ones can be developed in approximately two months. Additionally, Infragistics is developing an application program interface that clients can use to create their own connectors.

Creating an Easy Workspace

The company offers templates for personalized work plans utilizing well-known applications like Intuit Inc.’s QuickBooks and Google Analytics, and workspace creation is a drag-and-drop proposition. Guida said, “If you do content and search engine optimization, we’ll bring in all the digital ad information and website analytics that you need to get started quickly.”

Additionally, the company developed machine learning models that recommend dashboards and team member assignments. Further, machine learning summarizes written discussions, guide follow-up assignments, and constructs an enterprise-wide knowledge graph.

Slingshot is offered on a freemium premise, with three complimentary workspaces and monthly fees of USD 12 and USD 24 for the standard and enterprise editions, respectively.

• With Charmed Kubeflow on AWS, users can now quickly launch and manage their machine learning workloads.
• In 2022, 35% of organizations were expected to adopt AI, according to IBM’s Global AI Adoption Index.

Canonical Ltd., an Ubuntu software provider, released its machine learning operations toolset Charmed Kubeflow on Amazon Web Services Inc.’s cloud marketplace.

Charmed Kubeflow is available as a software application on AWS, simplifying the deployment and management of machine learning workloads for businesses. The software is an enterprise-grade variant of Kubeflow, an open-source MLOps toolkit designed to work with Kubernetes, the ubiquitous container orchestration software for application containers. It provides various utilities that make operating artificial intelligence on Kubernetes simpler.

Canonical says Charmed Kubeflow on AWS helps enterprises experiment with machine learning processes. It occurs when an increasing number of organizations demonstrate an outsized interest in artificial intelligence and machine learning. According to IBM Corporation’s Global AI Adoption Index, 35% of businesses will adopt AI in 2022. With the proliferation of generative AI initiatives such as ChatGPT, interest in the technology is proliferating.

Charmed Kubeflow on AWS, according to Canonical, is designed for businesses looking to launch their AI and machine learning initiatives because it is simple to deploy and offers unlimited computing power to experiment without limitations.

Charmed Kubeflow creates a trustworthy application layer for model development, iteration, and production deployment by automating machine learning workflows. Additionally, it offers complete visibility into those workloads so teams can evaluate any difficulties and precisely plan their infrastructure expansion needs.

Users can deploy their models on end devices after the complete experimental phase. Simultaneously, Charmed Kubeflow will guard against cyberattacks with frequent scanning, patching, and updates to the most recent version of the machine learning libraries in use. Users may move artifacts from the Charmed Kubeflow appliance to an AWS or data center deployment for production-grade deployments.

According to Aaron Whitehouse, senior director of public cloud enablement at Canonical, Charmed Kubeflow is the best platform for businesses looking to experiment with machine learning for the first time. He said, “The Charmed Kubeflow appliance on AWS gives companies a great way to test out machine learning possibilities quickly and easily, with a clear pathway to a scalable hybrid/multi-cloud deployment if those pilot projects are successful.”

A fully managed version of Charmed Kubeflow on AWS is now accessible through the AWS Marketplace for businesses needing infrastructure support.

• With the money from this round, NetBox Labs will be able to scale the creation of NetBox and NetBox Cloud, two open-source products.
• The platform has gained over 12,000 stars on GitHub, thousands of enterprise installations, and hundreds of technology integrations, proving to be a huge success.

NetBox Labs Inc., a network management and automation startup, aims to take off after closing a USD 20 million round of funding recently.

Raj Dutt, the CEO of Grafana Labs Inc., Mango Capital, IBM Corp., Two Sigma Ventures, the Founder Collective, and Entrée Capital all contributed to the Series A round, which Flybridge Capital organized.

The open-source NetBox platform, developed by NetBox Labs, is intended for infrastructure resource management and modern network automation. It provides feature-rich and API functionality for managing IP addresses, data center infrastructure, and other networking tasks. The more than seven-year-old project provides the building blocks for automated testing, monitor updates, drive device provisioning, and more while assisting businesses in reducing their reliance on jumbled spreadsheets.

The platform has gained over 12,000 stars on GitHub, thousands of enterprise installations, and hundreds of technology integrations, proving to be a huge success. Numerous users and businesses, including Dartmouth College and Chewy Inc., use it as a “network source of truth” to power automation.

NS1 Inc., a networking company that IBM recently acquired, was spun off into NetBox Labs, which recently debuted as a standalone business. Kris Beever, the past CEO of NS1, has taken over the same position at NetBox Labs, that he had co-founded with Jeremy Stretch, the chief maintainer. In order to reduce the administrative burden of hosting and managing NetBox instances, it has developed the NetBox Cloud platform, a managed version of NetBox. It also boasts additional compliance, dependability, and security features.

Stretch said, “NetBox has become a linchpin technology enabling organizations to accelerate their automation journeys and take back control of their networks.”

Beevers added, “NetBox is unequivocally the dominant network source of truth on the market today, and we are committed to making sure it’s the first choice for every networking professional on the planet, whether they are an open source or commercial user.”

It’s encouraging to see NetBox Labs attracting funding, according to Holger Mueller of Constellation Research Inc., as networking continues to be one of the areas with the highest business costs. “With layers and layers of networking technologies and infrastructure baked over each other, the cleanup is risky and offers little reward. New solutions that can reduce complexity, lower the cost of operations and reduce the operational management burden are key, and it’s no surprise that the innovation here is coming from startups.” He said.

With the money from this round, NetBox Labs will be able to scale the creation of NetBox and NetBox Cloud, two of its open-source products.

A company whose technology has such wide adoption even before it has raised Series A funding, according to David Aronoff, general partner of Flybridge Capital, presents a rare opportunity. He said, “NetBox Labs is such a company. We believe its role as a network source of truth is critical to the future of network automation and will fundamentally change the industry.”

• Sony Semiconductor Solutions will make an undisclosed investment in Raspberry Pi and deliver its edge AI sensing platform AITRIOS to millions of developers who utilize the popular computing platform as part of the agreement.
• According to Sony Semiconductor Solutions President and CEO Terushi Shimizu, the relationship with Raspberry Pi will expand AITRIOS and deliver its robust edge AI capabilities to a worldwide developer community across many sectors, resulting in new and innovative AI-powered edge applications.

Sony Semiconductor Solutions Corp. recently announced that it is expanding its connection with Raspberry Pi Ltd., the manufacturer of the pocket-sized computing platform of the same name, with an aim to provide developers better access to edge-hosted artificial intelligence tools.

Sony Semiconductor Solutions will make an undisclosed investment in Raspberry Pi and deliver its edge AI sensing platform AITRIOS to millions of developers who utilize the popular computing platform as part of the agreement.

The Raspberry Pi is a palm-sized, PC-like computer with an exceedingly compact form factor. It was initially conceived as a learning medium for computer science students to comprehend programming, coding, and computing. However, it has also gained popularity as a prototyping platform for software developers, particularly those creating so-called Internet of Things devices at the network edge.

This is the community that Sony’s partnership with Raspberry Pi aims to reach. AITRIOS is a framework for developing AI models that are powered by cameras and other sensors that function in low-power conditions at the edge.

It is aimed at developers creating AI that operates on cameras, applications for AI-driven sensing, and camera manufacturers who wish to incorporate AI capabilities into their hardware. One of the difficulties associated with AI-powered cameras is that they must be able to process a substantial quantity of data at the edge. Sending data to the cloud for processing results in unacceptable latency, hence the requirement for a robust edge processing platform such as AITRIOS.

Terushi Shimizu, President and Chief Executive of Sony Semiconductor Solutions, claims that the collaboration with Raspberry Pi will improve the capabilities of AITRIOS and make its potent edge AI capabilities available to a global developer community across numerous industries, leading to the development of fresh and intriguing AI-powered edge applications.

• The study also reveals that 82% of organizations are affected by attack methods that target credentials and permissions.
• It was discovered that 71% of organizations’ on-premises networks had vulnerabilities that put the security of their crucial cloud assets at risk.

According to a recent report from cybersecurity company XM Cyber Inc., while 75% of security exposures do not put organizations at risk, a small number of exposures can endanger more than 90% of critical exposures.

These key findings were highlighted in Navigating the Paths of Risk: The State of Exposure Management, XM Cyber’s second annual research report. The report, created in partnership with the Cyentia Institute, discovered that modern security teams are confronted with an overwhelming volume of exposures to validate and analyze, even though only some uncovered exposures lead to critical assets.

The study examined more than 60 million exposures in over 10 million entities, both on-premises and in the cloud, and discovered that the average organization has 11,000 exploitable security exposures per month, rising to 250,000 for larger businesses. The statistics show that exposure remediation must be more effective to stay ahead of the attack wave.

It is a little surprising to learn that 75% of exposures along attack paths result in “dead ends”. Dead ends pose a low risk because they cannot and do not affect critical assets. Only 2% of security exposures were discovered to be situated near “choke points”—structures where several attack paths collide—on their way to important assets. The report makes the case that organizations can reduce risk to the lowest possible level by concentrating efforts on addressing exposures at these choke points and distributing as little remediation work as possible among security and IT teams.

Vice president of Research at XM Cyber, Zur Ulianitzky, said, “Security teams are inundated with increasing volumes of alerts and attackers are actively exploiting this. As illustrated by our research, the vast majority of security alerts are benign and do not lead to critical assets.”

Threat actors, according to Ulianitzky, are not working any harder than necessary, and the majority of them succeed with straightforward and concise attack paths. He said, “By diligently focusing remediation efforts on first and foremost eliminating the 2% of exposures which provide attackers with seamless access to critical assets, organizations can significantly reduce their risk without adding any additional strain to security teams.”

The need for strong security controls in both cloud and on-premises environments is another finding of the report. It was discovered that 71% of businesses had vulnerabilities in their on-premises networks that put the security of their crucial cloud assets at risk.

The study also shows that 82% of organizations are impacted by attack methods that target credentials and permissions. Attackers take advantage of trusted administrative services and identities to carry out attacks, but many organizations ignore attack paths that use credentials and permissions.

According to Mike Parkin, Senior Technical Engineer at the cyber risk management firm Vulcan Cyber Ltd., a few key conclusions can be drawn from the XM Cyber report. First, only a tiny percentage of exploitable vulnerabilities result in serious compromise.

He said, “Even when only a few of them could be considered significant, it doesn’t mean we can discount even those minor breaches. A threat actor in the environment can still do considerable damage, even if they don’t have immediate access. If they can gain persistence on a low-value target, they have a chance down the line to escalate when a better opportunity presents itself.”

According to Parkin, “the second significant finding reinforces something we, in the cybersecurity community, have been saying for a while, namely that misconfigurations and compromised credentials are still a major risk.”

• The business will increase its investment in product development, speed up its go-to-market plans, and maintain its commitment to providing top-notch customer service with the help of the new funding.
• Over the course of the following year, the company plans to increase its 40-person workforce to 70 employees using the new funding.

A new USD 20 million round of funding has been secured by Strivacity Inc., a startup that aids in managing application login and signup requests for developers.

The investment was described as a Series A2 funding round when it was announced recently. Ten Eleven Ventures joined the round, which SignalFire led. Jack Huffard, a founding member of the cybersecurity firm Tenable Holdings Inc. and Mandiant CEO Kevin Mandia, also made contributions.

Developers can easily add a user account creation form to their applications using the platform that Strivacity offers. Usually, a sizable amount of custom code is needed to complete the task. The startup claims that its platform’s templates and other features speed up the process and lessen the need for manual programming.

Application developers must build a database to store user account information in addition to creating a signup form for customers. That task will also be made easier, according to Strivacity. It offers features for self-service password resets and tools that developers can use to manage user account data.

The platform has the ability to handle login requests in addition to user signups. According to the startup, developers can implement password-based and passwordless login features in applications using its platform. Additionally, it supports single sign-on, a technology that enables employees to sign into several corporate applications simultaneously.

Strivacity has developed a security engine with artificial intelligence to thwart hacking attempts. The engine creates a diagram of how a user typically interacts with an application and spots access requests that don’t fit the pattern. An account may require the user to log in again if it is accessed via an insecure network or at an odd time of day.

Chief Executive Officer and Co-founder Keith Graham said, “We believe simple sign-in journeys are the modern digital doorway to grow customer relationships. That’s why we built Strivacity. We’re 100% focused on creating surprisingly simple customer sign-in journeys, and this new investment will accelerate our strategy and the opportunity ahead of us.”

Over the course of the following year, the company plans to increase its 40-person workforce to 70 employees using the new funding. The new hires will hasten feature development and aid in the company’s go-to-market expansion.

A few months before Strivacity’s funding round, another startup to simplify application access management, Strata Identity Inc., raised USD 26 million. To handle sign-in requests, many applications employ a class of software known as an identity and access management, or IAM system. A platform provided by Strata makes migrating applications from antiquated IAM systems to more modern cloud-based substitutes simpler.

• A developing technology called quantum networking may one day enable data transmission with higher security than is currently possible.
• Data can only move a relatively small distance when transmitted as light over a network before errors start to appear.

According to a recent report, Amazon Web Services Inc. has partnered with De Beers PLC to produce diamonds for quantum networks.

According to a recent Bloomberg report, a De Beers subsidiary named Element Six is involved in the partnership. The subsidiary has opened a facility in Oregon that can produce millions of artificial diamonds annually, meant for networking purposes. Element Six will work with an AWS division called the Center for Quantum Networking, which debuted last year, as part of the partnership.

A developing technology called quantum networking may enable data transmission with higher security than is currently possible one day. AWS intends to integrate the technology into its infrastructure, according to Bloomberg. For tasks like connecting cloud data centers together, quantum networking hardware might be used.

In a data center network, information is encoded to light and then transmitted over fiber optic cables to its final location. Quantum networks also use light to transmit data. However, they go about it in a way that makes it much more difficult for hackers to eavesdrop on data traffic secretly.

The observer effect, a phenomenon, causes observing subatomic particles to change some of their characteristics. The state of the photons passing through the fiber optic cables of a quantum network is altered when a hacker observes those photons. Therefore, the ensuing interference enables the network operator to identify a breach right away.

Quantum networks also make it difficult for hackers to copy traffic, which is another advantage. This lowers the possibility of data exfiltration, further enhancing security. Nevertheless, one of the biggest challenges to putting the technology into practice is how challenging it is to copy information in quantum networks.

Data can only move a relatively small distance when transmitted as light over a network before errors start to appear. The reason is that as light signals carrying data travel farther and farther from their source, they weaken.

Conventional networks have a repeater component as a solution to that problem. To prevent errors, it gathers information-carrying light pulses before they begin to fade, makes a new copy, and then sends the copy in place of the original. However, in quantum networks, copying data in this way is impossible, making it impossible to use conventional repeaters.

Quantum networks are unsuitable for connecting geographically dispersed systems because long-distance data transmission calls for repeaters. This limits the technology’s applicability to cloud service providers. The new alliance between AWS and De Beers aims to overcome this difficulty.

As part of the collaboration, the cloud behemoth is creating a novel repeater operating in quantum networks. According to reports, AWS intends to produce components using artificial diamonds grown by De Beers’ Element Six division. A significant step toward creating commercial-grade quantum networks would be the creation of a working quantum repeater.

According to Bloomberg, Antia Lamas-Linares, AWS Center for Quantum Networking head, expects the technology to be used in “years rather than decades.”

Competitors of AWS are also investing in cutting-edge networking technologies. Microsoft Corp. purchased the networking startup Lumenisity Ltd. in December of last year. The startup has created a new fiber optic cable to transmit data more securely and quickly than existing hardware.

Lumenisity’s cables differ from regular fiber optic links as they are hollow. Before being acquired by Microsoft, the startup claimed that data could move through the hollow core 50% more quickly than with standard network hardware. Additionally, the technology enables traffic protection using quantum encryption algorithms, which are more secure than the software used today.