• The Google Cloud Consulting portfolio will initially feature more than a dozen professional services, according to the Alphabet Inc. division.
• Optimizing current cloud environments is the subject of a second group of Google Cloud Consulting services.

The professional service products of Google LLC’s cloud division are being reorganized into a new, centralized portfolio dubbed Google Cloud Consulting.

It follows a year in which Google Cloud increased its financial commitments to expand its professional services business significantly. With the help of the firm’s services, companies may more easily adopt Google Cloud products, train staff members to use them, and create new apps.

Lee Moore, Vice President of Google Cloud Consulting, reported, “The Google Cloud Consulting portfolio provides unified services capability, bringing together offerings, across multiple specializations, into a single place. This includes services from learning to technical account management to professional services and customer success.”

According to the company, the introduction of the Google Cloud Consulting portfolio has two primary objectives.

Its first goal is to make it simpler for businesses to hire experts from the search engine giant. The company claims that customers will have access to a thorough explanation of each service in Google’s catalog and illustrations of how to utilize it. The objective is to streamline the procurement procedure for business customers.

According to the search engine titan, Google Cloud Consulting will make it simpler for partners to collaborate with its professional service teams. Google has a vast ecosystem of associates dedicated to providing consulting services to its clients. A business launching a complex cloud initiative could hire both Google and a third-party consulting firm to assist.

According to the Alphabet Inc. division, the Google Cloud Consulting portfolio will initially feature more than a dozen professional services. The products fall under various categories.

Some services are designed to make it easier for businesses to implement Google Cloud products. The process of implementing Google Workspace is made more accessible by one such service. Another is made to make it simpler to transfer on-site corporate records to BigQuery, the search engine giant’s cloud-based data repository.

Optimizing current cloud environments is the subject of a second group of Google Cloud Consulting services. For instance, company employees can work with an organization to determine whether its cloud environment has weak security settings.

Training options are also part of the Google Cloud Consulting portfolio. It can give administrators at a company the know-how to set up and control Google Cloud environments. More specialized topics are also covered in the training programs, such as how to create applications for artificial intelligence using the Vertex AI toolkit.

Moore stated, “As we look forward to 2023, we’ll continue to expand the service catalog and focus on making it even easier to find and transact these services and further streamline the experience of engaging with our services.”

• According to The Times, Google’s project needs a clearer timeline, making it unclear when the features might go live.
• The Times reports that Google plans to debut Magi next month before adding more new features in the following months.

When its core business is under the most serious threat in years, Google LLC is reportedly rushing to introduce new features and capabilities in its search engine powered by artificial intelligence.

According to reports, the company is developing a brand-new, AI-powered search engine and is considering updating its current search technology with AI features.

The changes are Google’s response to Samsung Electronics Co. Ltd.’s suggestion that it might stop using Google Search and switch to Microsoft Bing as its default mobile search engine, the New York Times reported recently.

According to The Times, Google could lose Samsung and suffer a loss of revenue of more than USD 3 billion annually. The suggestion allegedly caused widespread “panic” within Google; as a result, forcing the company to scramble to keep up with the surge in demand for technologies like ChatGPT.

As details obtained by the Times from internal emails, Google’s response is to update its search engine as part of a project called “Magi.” According to reports, Google has 160 employees working in “sprint rooms” to develop new AI-powered Google Search features.

Since December of last year, when executives first understood the significance of OpenAI LP’s ChatGPT and how it might present a problem for search, Google is said to have been in a frenzy. When Microsoft Corp. revealed plans to integrate ChatGPT with Bing in February, the threat to Google’s decades-long search market dominance only grew. Sundar Pichai, the CEO of Google, responded by pledging soon to update Google Search with new AI chat features.

A new service that will try to predict what users are looking for before they search is one of the new features Google is developing as part of a “more personalized” experience. According to The Times, Google’s project needs a clearer timeline, making it unclear when the features might go live.

A Chrome feature called “Searchalong” that would scan the website the user is reading and provide contextual information is among the other new features rumored to be in the works. The business is also developing a chatbot that can provide code snippets in response to questions about software engineering. A second chatbot would aid in music discovery. More experimental features, including “GIFI” and “Tivoli Tuto,” are also being developed, allowing users to ask Google Image Search to create images and communicate with a chatbot in a different language.

However, it should be noted that many of these features are only partially original. For instance, There is an existing image generation function in Slides, and Tivoli Tutor sounds a lot like Duolingo Inc.’s learning app.

Google’s apparent panic and haste to enhance the capabilities of its search engine, according to analyst Charles King of Pund-IT Inc., shows how flawed the ad-based search model has grown. He said, “Once upon a time, a search engine’s value was based on the quality of results it delivered, but today it’s likely that the top five or ten results you see for any given search will consist of sponsored ad links from some commercial entity.”

As a result, all internet users could gain from improved search capabilities. King said he would be surprised if Google couldn’t produce new AI-based tools that are at least on par with Microsoft’s, if not superior to them.

King said, “That said, the history of the tech industry is littered with stories of once-unstoppable firms that were undermined by more nimble and advanced competitors. Remember when Microsoft Explorer dominated the browser market to the point that the company was successfully challenged on anti-trust grounds? Then along came Google Chrome. Maybe this is just the latest tale of ‘what goes around, comes around'”.

According to Constellation Research Inc.’s Holger Mueller, who is more upbeat, Google’s plan to create a brand-new search engine based on generative AI makes sense because incremental innovation may only go so far in developing next-generation search. The analyst said, “At the same time, the coming reported updates are a good move as they can hedge against Microsoft Bing’s new AI capabilities. Though Google will in any case need to be cautious, as the verdict is still out on whether or not generative AI can really improve search experiences.”

According to The Times, Google plans on introducing Magi in the coming month before following up with more features in the fall. According to this timeline, more information about Magi might be made public on May 10 during Google I/O 2023. According to reports, Google intends to first make Magi’s features available to 1 million test subjects before making them available to 30 million users by the end of the year. Magi will initially only be made available in the United States.

Google declined to respond to the Times’ claims in a statement directly but claimed that it has been incorporating AI capabilities into Google Search for years through features like Lens and multisearch, among others.

A Google spokesperson said, “We’ve done so in a responsible and helpful way that maintains the high bar we set for delivering quality information. Not every brainstorm deck or product idea leads to a launch, but as we’ve said before, we’re excited about bringing new AI-powered features to Search, and will share more details soon.”

• According to the company based in Mountain View, California, Capitola’s platform increases the productivity of brokerage firm employees by eliminating repetitive manual tasks.
• The platform employs AI to monitor the insurance-related risks in terms of the company’s assets.

Capitola Insurance Services LLC, a startup that uses artificial intelligence to simplify commercial insurance transactions, has raised USD 15.6 million in funding.

Munich Re Ventures led the Series A round, which was announced recently. Lightspeed Venture Partners, a previous Capitola investor, also took part. In 2021, the year it was founded, the startup raised a five-million-dollar seed round.

Businesses do not purchase insurance directly from insurers but rather through a broker, an intermediary. The broker assists in identifying a policy that will meet the company’s needs. It then locates an insurer willing to take on that policy.

According to the company based in Mountain View, California, Capitola’s platform provides a software platform that helps simplify commercial insurance brokers’ lives. According to the company, its platform increases the productivity of brokerage firm employees by eliminating repetitive manual tasks.

Businesses frequently purchase insurance for multiple assets, such as servers and manufacturing equipment. Each of these items poses a different level of danger. A commercial insurance provider will only cover an asset if the risk level of that asset is aligning with its internal policies.

One of the most difficult aspects of insurance brokers’ work is finding an insurer willing to cover a company’s assets. Capitola claims that its platform can make the job easier. The platform employs AI to monitor the insurance-related risks in terms of the company’s assets. Then it matches those risks with insurers willing to underwrite policies at that risk level.

When deciding on an insurer, a broker considers several factors. Capitola claims that its platform provides data that enables brokers to perform such evaluations more quickly.

A commercial insurance policy has an attachment point specifying the maximum loss the insurance company will cover. This metric influences the monthly fee that a business must pay for its insurance policy. Capitola’s platform includes an analytics tool that enables users to compare between different attachment points and their associated monthly payments to find the best combination.

It also offers access to other data, such as how much a broker can make from selling a particular insurance policy. The data is visualized in charts by the company to facilitate user analysis.

Capitola claims that its platform can assist brokers in selling new policies and renewing existing ones. When a policy is about to expire, it alerts brokers and allows them to request a renewal from the insurer underwriting that policy. It also includes features that allow you to switch to a new insurance provider if necessary.

Capitola’s platform also includes additional productivity features. It includes a task assignment tool that allows managers to coordinate who oversees which aspects of a commercial insurance transaction. The employees of the broker’s office can generate a proposal automatically, when a business plans to buy a fresh policy or renew an existing one.

Co-founder and CEO Sivan Iram said, “The insurance industry has seen many technological advancements over the years, but very little attention has been given to the insurance professionals and the tools they use. Our platform brings together brokers and underwriters, removing many of the operational inefficiencies around manual processes and repetitive tasks to allow them to focus on what they do best.”

Capitola plans to use a part of the recent USD 15.6 million round to expand its presence in the United States. It also intends to add new features, such as market intelligence tools for brokers.

• 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.

• Hyper-automation is a term used to describe the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and other forms of intelligent automation to automate complex business processes end-to-end.
• Due to the growing interest in this technology phenomenon, Gartner anticipates that by 2024, businesses will have implemented at least three of the twenty software solutions that enable hyper-automation.

We were taught that steam engines were deployed to automate the textile industry during the First Industrial Revolution towards the end of the 18th century. The Second Industrial Revolution brought electricity and the internal combustion engine, while the Third Industrial Revolution brought digitalization and process automation around the end of the 20th century.

The Fourth Industrial Revolution is now here, bringing in robotics, the Internet of Things (IoT), and Artificial Intelligence (AI). It is a process that is compelling developed nations to engage in reindustrialization to regain their self-sufficiency in consumer goods production. Hyper-automation, also known as digital process automation (DPA) or intelligent process automation (IPA), is one of the most significant technological developments of the next several years in this new setting.

Hyper-automation is a term used to describe the integration of advanced technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and other forms of intelligent automation to automate complex business processes end-to-end.

This process is the next phase of automation, where the focus is on automating repetitive and manual tasks and creating a fully automated system that can handle complex workflows, unstructured data, and decision-making processes.

Hyper-automation combines multiple automation technologies and tools to create a cohesive system that can work seamlessly across different functions and departments. By automating complex processes, hyper-automation can help organizations reduce costs, improve operational efficiency, enhance customer experience, and gain a competitive advantage in the market.

Hyper-automation Outperforms RPA in Every Way

Although hyper-automation tries to automate the entire process, i.e., in sizeable enterprise-level transformation, RPA employs software bots to automate specific activities. In essence, it aids in an organization’s digital transformation process.

RPA is enhanced by hyper-automation in terms of:

• The tools used are task-based and constructed on individual bots for RPA, whereas technology sequencing is used for hyper-automation.
• Internal workings: Hyper-automation is a network of technologies, platforms, and systems, unlike RPA, which is platform-specific.
• The end result: While hyper-automation produces intelligent, flexible, and efficient processes, RPA produces efficient processes.
• Future potential: Whereas automation can only be used for specialized, isolated use cases, hyper-automation can automate practically every aspect of a business.

RPA, AI, low-code application platforms (LCAP), and virtual assistants can all be used to quickly identify, evaluate, and automate as many processes as it is very practical. Gartner predicts that by 2024, businesses will have implemented at least three of the twenty software solutions that facilitate hyper-automation, due to the increasing interest in this technology phenomenon.

How Can Hyper-automation Transform Business in 2023

You might wonder what makes hyper-automation such hype. Well, it has the potential to revolutionize business operations tremendously. According to CRM Consultant, GlobeNewswire estimates that the present value of the worldwide hyper-automation market is USD 549.3 million, with a predicted CAGR of 22.79% to reach USD 2,133.9 million by 2029. Here are seven ways how hyper-automation will transform business in 2023:

Faster and more accurate decision-making

Hyper-automation will enable businesses to make faster and more accurate decisions by automating the analysis of large amounts of data. AI and machine learning algorithms can analyze data and provide insights to decision-makers, enabling them to make more informed decisions. This will improve operational efficiency, reduce errors, and increase the speed of decision-making.

Hyper-automation guarantees an implementation path devoid of errors. In addition, it provides superior analytic solutions that can be utilized to gain insights and comprehend organizational trends on a broader scale. Executives can monitor and distinguish what is successful and what is not.

Improved customer experience

Hyper-automation automates complicated business operations. Hyper-automation will transform the way businesses interact with customers. With the help of AI-powered chatbots, companies can provide personalized and efficient customer service around the clock. This will lead to improved customer satisfaction, loyalty, and retention.

To improve CX, you can also obtain insights into customer behaviors and analyze agent interactions, the number of complaints, and the frequency of first-time problem resolution, among other things.

Reduced costs

Hyper-automation will enable businesses to automate repetitive tasks like data entry, invoice processing, and report generation. This will reduce the need for human intervention, thereby lowering labor costs. Additionally, automation reduces errors and increases efficiency, lowering operational costs.

According to Gartner, combining hype-automation technologies with redesigned operating processes will reduce costs by 30% by 2024.

Accelerate the pace of innovation

Hyper-automation serves as the key driver for digital transformation. Low-code platforms allow businesses to design and improve processes, replace outdated systems without fear of data loss, and deploy new apps within weeks.

Establish a centralized truth source

The new hybrid cloud standard has made system integration a requirement for digital transformation. The concept of hyper-automation is predicated on the integration of software and processes. This results in seamless exchanges between on-premise equipment and data storage. Consequently, this architecture enables systems to connect and communicate easily, resulting in enhanced data accessibility through consolidation, even in a highly diverse environment.

Improved compliance

Hyper-automation will help businesses comply with regulations and standards by automating compliance-related tasks like data privacy and security. This will reduce the risk of non-compliance, fines, and reputational damage.

Competitive advantage

By utilizing hyper-automation, businesses can obtain a competitive advantage by responding to changing market conditions and innovating more rapidly than their competitors. From basic automation to enhanced artificial intelligence and machine learning, organizations must reach hyper-automation and deep learning.

Conclusions

With the increased usage of low-code/no-code tools, digital twins, and mass-market robotic process automation, the hyper-automation strategy is predicted to reach new heights and impact the corporate world in 2023. By merging AI with automation, businesses can focus on more inventive solutions that reliably satisfy ever-changing market demands.

• 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.

• According to Google, the Assured OSS collection includes the most well-known Python and Java packagers and popular artificial intelligence and machine learning tools.
• Google states it garnered an immensely positive response after releasing Assured OSS for public access the last year.

Google Cloud is making its Assured Open Source Software service generally available for Java and Python ecosystems to help enhance the security of the most popular open-source software.

Assured OSS, which was just announced and is free for consumers to use, enables organizations to utilize the same OSS packages that Google utilizes in its own developer workflows. Users can get extra security precautions that Google offers with those packages, enhancing their own security.

Given that the bulk of software programs and services in use today are based on open-source software, it might be an appealing offer. Even proprietary software applications rely on various open-source parts, but the security of these offerings from the community raises serious concerns. 17% of all security incidents in 2022 began with an attack on the open-source software supply chain, per the Mandiant M-Trends report. If hackers discover a flaw in an open-source component, it could be exploited by any application that employs it.

According to Google, organizations will gain a more secure open-source software supply chain by relying on Google’s comprehensive library of Assured OSS packages. With an Assured Software Bill of Materials offered in forms compliant with industry standards, they will better comprehend the components of the packages they employ. Because Google is continuously scanning and patching the components they utilize for vulnerabilities, their overall risk will also be decreased.

According to Google, the Assured OSS collection includes the most well-known Python and Java packages and popular artificial intelligence and machine learning tools such as TensorFlow, Pandas, and Scikit-Learn. The OSS packages are routinely scanned, analyzed, and fuzz-tested for vulnerabilities, are verifiably signed by Google, and are distributed from a company-protected artifact registry. ACCORDING TO GOOGLE, assured OSS has already demonstrated its value as it was the first to identify and resolve 48 percent of all newly discovered vulnerabilities in the first 250 Java applications it offered through the program.

Holger Mueller at Constellation Research Inc. reported that all the latest software is practically written with an open-source component, and its format indicates that it is open to all types of risks. “For many enterprises, checking software for bugs and vulnerabilities is an arduous and sometimes even impossible task. So it’s great to see that Google is letting others benefit from its own checks and due diligence,” Mueller added.

Google states it garnered an immensely positive response after releasing Assured OSS for public access the last year. Tech Fellow and N.A Managing Director of Citibank, Jon Meadows, mentioned that his company has been among the earliest adopters of this initiative. “Both Citi and Google see untrusted and unverified open source dependencies as a key risk vector. Assured OSS can help reduce risk and protect open-source software components commonly used by enterprises like us,” he added.

Organizations that want to begin using Assured OSS can use this self-service onboarding form. Then, they can attach the Assured OSS packages to their software development infrastructure in any desired environment, such as Artifact Registry, Artifactory, Nexus, and others.

Melinda Marks, an ESG analyst, stated that a reliable, secure open-source package is crucial for companies in the fast-growing cycles. “Without proper vetting and verification or metadata to help track OSS access and usage, organizations risk exposure to potential security vulnerabilities and other risks in their software supply chain. By partnering with a trusted supplier, organizations can mitigate these risks and ensure the integrity of their software supply chain to protect their business applications better,” she added.

• Three years ago, Insight Partners bought the company’s majority of shares. The purchase price was more than USD 780 million.
• One of many businesses using OpenAI’s GPT family of language models to assist cybersecurity teams in their work is Recorded Future.

A cybersecurity tool that uses an OpenAI LP artificial intelligence model to identify threats was just released by Recorded Future Inc.

The software platform by Boston-based Recorded Future enables businesses to monitor hacker activity. The platform, for instance, can be used by a bank to find new malware campaigns that target the financial industry. Recorded Future says that over 50 percent of the Fortune 100 companies use its technology.

Three years ago, Insight Partners bought the majority of the company. It was worth more than USD 780 million in the agreement.

The new tool that the company unveiled recently, Recorded Future AI, is built using a neural network from OpenAI’s GPT series of large language models. The most recent neural network in the GPT series, GPT-4, debuted last month. There are also more than a dozen additional AI models in the product line with various feature sets.

Companies continuously gather information about user activity, applications, and hardware in their networks to identify breaches. In the past, cybersecurity teams manually examined that data to look for fraudulent activity. The goal of Recorded Future AI is to make the task easier.

The business claims that its new tool automatically locates breach indicators in a company’s network and ranks them according to their seriousness. It also identifies weaknesses. For instance, the tool can determine whether a server has a configuration error that enables users to log in without a password.

The Recorded Future AI promises to accelerate several additional tasks as well.

Cybersecurity teams regularly produce reports for executives as part of their work that describes how well the corporate network is protected and where improvements can be made. Analysts must manually collect technical data from various systems in order to create such a report. The process could be sped up by several days with the help of Recorded Future AI’s promise to automate some steps.

Christopher Ahlberg, Co-founder and Chief Executive Officer said, “Now, with Recorded Future AI, we believe we can eliminate the cyber skills shortage and increase the capacity for cyber readiness by immediately surfacing actionable intelligence.”

One hundred terabytes of cybersecurity data were used to train the GPT model that Recorded Future obtained from OpenAI to create the tool. The startup’s eponymous software platform was used to gather the data. The platform offers businesses data on vulnerabilities, cyberattacks, and the servers which hackers utilize to launch malware campaigns.

The tool also uses research from the Insikt Group research group of the startup. The 40,000 analyst notes on online threats that the Insikt Group has produced over the years are included, in particular. Cybersecurity teams employ these analyst notes to describe hacker strategies and disseminate associated technical data.

One of many businesses using OpenAI’s GPT family of language models to assist cybersecurity teams in their work is Recorded Future.

Microsoft Corp. unveiled Security Copilot, a service that uses the most recent GPT-4 model from OpenAI last month. During a breach attempt, the service automatically detects malicious activity and predicts the next moves a hacker is likely to make. Cybersecurity teams can use security Copilot’s data to guide their efforts to address breaches.

• A software-defined wide-area network solution called Netskope Endpoint SD-WAN is based on the SASE platform of the business.
• By providing direct access to apps, policy-based virtual private networks do away with the drawbacks of current remote access networks.

Netskope Inc., a cloud security and networking firm, announced the launch of a new software solution called Netskope Endpoint SD-WAN to give businesses safe and optimal access to endpoint devices in any location.

Secure Access Service Edge, or SASE, is a platform provided by 2012-founded Netskope that enables the discovery, visibility, and granular control of both authorized and unauthorized cloud apps. The business secured a sizable USD 401 million funding round earlier this year. Its flagship Intelligent Security Service Edge provides a converged, cloud-based service intended to transform perimeter security while assisting businesses in staying ahead of cloud, data, and network security challenges.

A software-defined wide-area network solution called Netskope Endpoint SD-WAN is based on the SASE platform of the business. It will combine SD-WAN and Security Service Edge capabilities when it becomes generally available later this year, enabling companies to streamline the connectivity of numerous scattered clients and edge devices while ensuring their high performance.

The business claims it wants to address the shortcomings of traditional networking architectures, particularly the clumsy SD-WAN infrastructures that need to be prepared to handle contemporary apps and lead to bad user experiences, inconsistent policies, and insufficient security. Users will enjoy the same application experience and security level using their laptops or other devices due to Endpoint SD-WAN as they would inside the corporate office.

Parag Thakore, Sr. Vice President of borderless SD-WAN at Netskope, said, “Organizations using outdated SD-WAN and security technology have realized they can’t patch their way to a better network, can’t prioritize or secure what they can’t easily discover, and shouldn’t rely on cumbersome SD-WAN architecture that wasn’t built for a highly distributed, cloud-centric workforce.”

One of Endpoint SD-key WAN’s advantages is that it makes it possible for AI-driven operations, which makes it easier to manage devices and applications with automated troubleshooting and insights into end-user experiences. Moreover, it provides excellent performance for voice, video, and data applications, as well as application-aware prioritization and dynamic path selection to address issues with subpar last-mile performance.

Simultaneous connectivity to numerous public and private data centers further improves user experiences. Policy-based virtual private networks do away with the drawbacks of current remote access networks by providing direct access to apps.

A single architecture that extends context-aware zero trust policy controls across the whole network ensures security by giving any remote user, device, and location easy and secure access to any application environment.

To provide a safe, high-quality connection to their applications from any user, device, and place, businesses are placing high importance on unified edge networking and security initiatives, according to International Data Corp. analyst Brandon Butler.

Brandon Butler stated, “Netskope Endpoint SD-WAN brings the benefits of SD-WAN and edge security tools together in an end-user and IT-friendly client format. These tools can be powerful for enterprises and service providers alike to accelerate edge networking and security transformation.”

• 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.