As Microsoft CEO Satya Nadella emphasized on keeping Azure Cloud secure with integrated end-to-end identity, security and compliance solutions, cybersecurity firm Check Point on Thursday revealed that it identified two major security flaws in Microsoft Azure last year which have now been fixed.

The researchers at Israel-based Check Point discovered that a user on the Azure network could have potentially taken control over the entire server, opening a path to business code theft and manipulation.

The first security flaw was found in Azure Stack and the second security flaw was found in Azure App Service.

“The Azure Stack Flaw would have enabled a hacker to gain screenshots and sensitive information of machines running on Azure. The Azure App Flaw would have enabled a hacker to take control over the entire Azure server, and consequently take control over an enterprises’ business code,” the firm said in a statement.

Check Point said it worked closely with Microsoft to solve these issues, making the cloud more secure. The first security flaw was disclosed by Check Point on January 19 last year while the second security flaw was disclosed on June 27. Full patches for both security flaws in Azure were issued to the public by the end of 2019.

In the Azure Stack flaw, Check Point researchers were able to take screenshots and lift sensitive information of Azure tenants and infrastructure machines. “This security flaw would enable a hacker to get sensitive information of any business that has its machine running on Azure,” the researchers said.

In the Azure App flaw, an attacker could take control over server and business code. Researchers at Check Point were able to prove that a hacker could compromise tenant applications, data, and accounts by creating a free user in Azure Cloud and running malicious Azure functions.

“The end result would be that a hacker could potentially take control over the entire Azure server, and consequently take control over all your business code,” the Check Point report said. The disclosure came as Nadella, during an earnings call on Wednesday, said that now to security, cybercrime will cost businesses, governments and individuals $1 trillion this year.

“We are the only company that offers integrated end-to-end identity, security and compliance solutions to protect people and organizations, spanning identity management, devices, cloud apps, data and infrastructure,” Nadella emphasized.

He said that Azure is the only Cloud that offers consistency across operating models, development environments, and infrastructure stack, enabling customers to bring cloud compute and intelligence to any connected or disconnected environment.

“Azure Stack Edge brings rapid Machine Learning inferencing closer to where data is generated and the new ruggedized Azure Stack form factors provide cloud capabilities in even the harshest of conditions like disaster response,” he explained.

“Our differentiated approach across the cloud and edge is winning customers. The US Department of Defense chose Azure to support our men and women in uniform at home, abroad, and at their tactical edge,” Nadella asserted. There will be 175 zettabytes of data by 2025, up from 40 zettabytes today.

“Processing this data in real-time will be an operational imperative for every organization. Azure Synapse is our limitless analytics service. It brings together big data analytics and data warehousing with unmatched performance, scale and security,” the Microsoft CEO said. (IANS).

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Microsoft’s new tool, part of its Azure cloud platform, wants to make machine learning (ML) a drag-and-drop proposition.

In theory, this is a good thing for employees who aren’t mathematically inclined, or who have no background in programming or machine learning; while every organization would like its teams to become machine-learning experts, that’s simply not feasible. Microsoft’s platform allows employees to select a dataset, select a value to predict, and let the algorithms do their work in the background.

In a move that will no doubt make data scientists and analysts sigh with relief, the tool doesn’t obfuscate its processes behind a shiny-looking dashboard; experts will have the ability to study the algorithms producing the output. 

Microsoft is big on “visual coding,” which is no doubt vital to its broader ambitions as a software provider for corporations of all shapes and sizes. Last year, for instance, it unleashed an update to PowerApps, its low-code building platform, that allowed ordinary employees to put together mobile apps via drag-and-drop and some simple coding. At the time, the company liked to highlight stories of rank-and-file workers using PowerApps to radically change how their organizations worked—for instance, a security professional at Heathrow Airport used the platform to build apps that quickened his colleagues’ workflows (earning himself a promotion in the process). 

But low-code (and no code) has its limits. For one thing, sysadmins and other high-level tech pros often dislike it when employees begin churning out new apps and functions without much oversight and supervision. PowerApps came with an “App Checker” that acted as a front-line debugger, but ultimately it was Microsoft’s intent to create “controlled chaos” within organizations by allowing employees to take a lot of initiative in terms of designing and releasing apps. Not all sysadmins, CTOs, and product leads are onboard with that. 

With artificial intelligence (A.I.) and machine learning, the potential downsides of low-code platforms are higher. It’s not so much a question of an A.I. or ML algorithm unleashing tons of chaos—nobody’s going to create a low-code Skynet anytime soon. Rather, these algorithms are delicate and often temperamental things, and it’s easy to picture Tad from Accounting getting increasingly frustrated over why his machine-learning tool doesn’t work like he thinks it should, or why the output seems so messed up. Data scientists could end up spending as much time fixing their colleagues’ work as they could on their own tasks.

Although A.I. and ML experts remain much in demand (those with substantial experience and a track record of successful projects can easily earn six-figure salaries—if not higher), the rise of no- and low-code tools (combined with companies instituting at least rudimentary A.I. education for workers) could boost the supply of A.I.-knowledgeable workers, which in turn could make it difficult for newer graduates in the subtle arts of A.I. to land desirable gigs. In this scenario, those with specialized skills and experience would end up having the best chance on the market.

That’s a far-off hypothetical, though; for the moment, A.I. and ML remain complex and delicate disciplines, and it seems unlikely that any single no-code tool will disrupt that paradigm for the time being.  

The post Low-Code Machine Learning: Good for Workers, Bad for Companies? appeared first on Artificial Intelligence.

IBM is taking aim at the challenging concept of securely locking-down company applications and data spread across multiple private and public clouds and on-premises locations.

IBM is addressing this challenge with its Cloud Pak for Security, which features open-source technology for hunting threats, automation capabilities to speed response to cyberattacks, and the ability integrate customers’ existing point-product security-system information for better operational safekeeping – all under one roof.[ Learn how server disaggregation can boost data center efficiency and how Windows Server 2019 embraces hyperconverged data centers . | Get regularly scheduled insights by signing up for Network World newsletters. ]

IBM Cloud Paks are bundles of Red Hat’s Kubernetes-based OpenShift Container Platform along with Red Hat Linux and a variety of connecting technologies to let enterprise customers deploy and manage containers on their choice of infrastructure, be it private or public clouds, including AWS, Microsoft Azure, Google Cloud Platform, Alibaba and IBM Cloud.[ Prepare to become a Certified Information Security Systems Professional with this comprehensive online course from PluralSight. Now offering a 10-day free trial! ]

Cloud Pak for Security is the latest of six that are available today, the others being Data, Application, Integration, Automation and Multicloud Management, and they also incorporate containerized IBM middleware designed to let customers quickly spin-up enterprise-ready containers, the company said.

The Cloud Paks are part of a massive Big Blue effort to develop an advanced cloud ecosystem with the technology it acquired with its $43 billion buy of Red Hat in July. The Paks will ultimately include IBM’s DB2, WebSphere, API Connect, Watson Studio, Cognos Analytics and more.

“The infrastructure is evolving in such a way that the traditional perimeter is going away and in the security domain, customers have a plethora of point-vendor solutions and now cloud-vendor security offerings to help manage this disparate environment,” said Chris Meenan, Director, Offering Management and Strategy, IBM Security.

Protecting this fragmented IT environment requires security teams to undertake complex integrations and continuously switch between different screens and point products. More than half of security teams say they struggle to integrate data with disparate security and analytic tools and combine that data across their on-premises and cloud environments to spot advanced threats, Meenan said.

One of the foundational components of Cloud Pak for Security is that it can, from a single containerized dashboard, connect, gather and see information from existing third-party tools and data sources, including multiple security-information and event-management software platforms, endpoint detection systems, threat-intelligence services, identity and cloud repositories, IBM said. Cloud Pak Connectors have been included for integration with security tools from vendors including IBM, Carbon Black (now part of VMware), Tenable, Elastic, BigFix, and Splunk, as well as public-cloud setups from IBM, AWS, and Microsoft Azure. 

The big deal here is that the tool  lets security teams connect all data sources to uncover hidden threats and make better risk-based decisions, while leaving the data where it resides, without needing to move that data into the platform for analysis, Meenan said.

“There’s a ton of security data out there, and the last thing we wanted to do was force customers to build another data lake of information, “ Meenan said. “Cloud Pak lets customer access data at rest on a variety of security systems, search and query those systems all via a common open-source federated framework.”

For example, the system supports Structured Threat Information Expression (STIX), an open-source language used to exchange cyber-threat intelligence. The platform also includes other open-source technology IBM co-developed through the OASIS Open Cybersecurity Alliance.

The open source technology and the ability to easily gather and exchange data from multiple sources should be a very attractive feature for customers analysts said.

“The main takeaway is their ability to federate security-related data from a broad variety of sources, and provide flexible/open access to that,” said Martin Kuppinger, founder and principal analyst at KuppingerCole. “They federate, not replicate, the data, avoiding having yet another data lake. And the data can be consumed in a flexible manner by apps you build on IBM Security Cloud Pak but also by external services. With security data commonly being spread across many systems, this simplifies building integrated security solutions and better tackling the challenges in managing complex attacks. IBM successfully managed to launch this offering with a very broad and comprehensive partner ecosystem – it is not just a promise, but they deliver.”

Once the data is gathered and analyzed the platform lets security teams orchestrate and automate their response to hundreds of common security scenarios, IBM said.  Via the Cloud Pak’s support for Red Hat Ansible automation technology customers can define actions such as segmenting a multicloud domain or locking down a server quickly. Meenan said.

The platform helps customers formalize security processes, orchestrate actions and automate responses across the enterprise, letting companies react faster and more efficiently while arming themselves with information needed for increasing regulatory scrutiny, IBM said.

The Security Cloud Pak is a platform on which Big Blue will develop future applications, Meenan said, “to address new challenges and risks such as insider security threats, all designed in realistic ways for customer to deploy without having to rip and replace anything.”

Kuppinger said the security Pak will have immediate value for larger businesses running their own security operations/cyber-defense centers.

“The biggest challenge for IBM might be education – it is a new approach. However, the offering distinguishes clearly from other approaches, providing obvious benefits and adding value to existing infrastructures, not replacing these. Thus, it is clearly more than yet another product, but something really innovative that adds value.”Join the Network World communities on Facebook and LinkedIn to comment on topics that are top of mind.

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Microsoft’s Ignite event traditionally attracts more from the developer ranks, but the technologies on display are increasingly of relevance to CIOs developing cloud strategies today.

At Ignite 2019 in Orlando last week, Microsoft unveiled a new approach to analytics and data warehousing, Azure Synapse Analytics, and a new way to run Azure data services in anyone’s cloud, Azure Arc. It also talked up a new quantum-computing-as-a-service offering, and showcased some AI technologies that will soon make their way into the company’s cloud services.

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Scaling up

With Azure Synapse Analytics Microsoft takes its Azure SQL Data Warehouse and turns up the volume to handle petabytes of data in its cloud. Some of the features — such as dynamic data masking and column- and row-level security to provide granular access control — are already generally available, while others — notably integrations with Apache Spark, Power BI and Azure Machine Learning — are still in preview. Other capabilities include streaming data ingestion and streaming analytics directly in the data warehouse, and a unified workspace for data prep and management. One of the first companies to use it is Unilever.

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Whereas Azure Synapse is about helping enterprises get all their data in one place, Azure Arc is about helping them spread it around, using a common interface to manage tasks running in the Azure cloud and workloads hosted on premises or in other cloud environments. Microsoft says Azure Arc extends existing management capabilities such as Azure Resource Manager, Azure Cloud Shell or Azure Policy to Linux and Windows servers and Kubernetes clusters running on any infrastructure, whether on premises or in other vendors’ clouds.

Initially, it’s showing how to run Azure SQL Database and Azure Database for PostgreSQL Hyperscale on any Kubernetes cluster or on Azure Kubernetes Service, the idea being that it’s easy for CIOs to spin up additional computing capacity in the cloud when on-premises resources run out.

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For now, the service is in preview: businesses can try it out for free, with no guarantees.

Quantum solace

Azure Quantum was perhaps the most out-there announcement of the week: Microsoft has joined IBM in offering to run quantum computing apps in the cloud, albeit on an experimental rather than commercial basis.

In theory, quantum computing offers an algorithmic shortcut to solving many of the most time-consuming optimization problems — breaking many encryption systems wide open into the bargain. In practice, today’s quantum computers lack power and tend to break down before they’ve completed the job.

Microsoft said that its scientists had developed a way to control up to 50,000 qubits — the basic unit of calculation in a quantum computer — using just three wires and a half-inch-square chip cooled to near absolute zero. That will be useful if anyone ever manages to build a 50,000-qubit computer, but for now it’s just hype: IBM and Google, the leaders in the domain, each demonstrated quantum computers with just 53 qubits last month.

Still, there’s some solace for far-sighted CIOs who can’t afford their own quantum hardware: Hosted services such as Microsoft’s Azure Quantum and IBM’s Q Experience provide affordable insight into what may become the software development environments of the future. And until true quantum computers become more widely available, they and on-premises hardware simulators such as the Quantum Learning Machine from French server maker Atos offer a chance to test the applicability of quantum computing to some of today’s toughest business optimization problems.

Project Cortex

Also not yet available, but more down to earth, is Project Cortex. This is the name Microsoft has given to a new AI service coming to Teams, Outlook and Office that will offer users on-demand information that may be relevant to their needs. For a user confronted by an unfamiliar acronym, that might include a definition, related documents or even contact details for company experts on the topic.

Microsoft already offers such AI-powered features in some of its applications, including the “Insights” feature in Outlook that suggests people to contact or tasks to follow up on, and Project Cortex is about expanding this offering. While it will all happen behind the scenes for the end user, CIOs will need to ensure that access controls are well managed and data appropriately tagged so that Project Cortex can learn who needs — and is allowed — to see what.

The post Microsoft shows off hybrid cloud management and cloud analytics tools at Ignite appeared first on Artificial Intelligence.

Microsoft (NASDAQ:MSFT 148.06 0.51%) has partnered with the UK chipmaker Graphcore to provide cloud-based cutting-edge artificial intelligence solutions that utilize the computing power of specialized chips designed by the British company.

“This [move] is very significant for us because it means anyone on the planet can go to Azure and use our technology to build out their systems,” said Graphcore’s chief executive Nigel Toon. “It’s a massive step for the availability of our technology and a testament to its level of maturity.”

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The Bristol-based $1.5 billion company designs and manufactures specialized chips to run intensive applications that assist machines in face recognition, speech comprehension, parsing of language, autonomous driving, and training of robots. Graphcore’s first commercial product, launched in 2018, was a 16-nanometer PCI Express card known as the C2. It is this product that will launch on the Azure platform, thereby, allowing Microsoft to expand the gamut of its cloud-based services and features for business-critical AI operations.

Technical specification and performance metrics for Graphcore’s chip

The C2 includes two interlinked Colossus IPUs with each containing 16 cores and 23.6 billion transistors. A single chip’s 1,216 IPU can reach over 100 GFLOPS per core (one GFLOP equals one billion floating point operations per second) and is coupled with 300 MB of memory to run up to 10,000 programs in parallel executions. Each chip boasts a memory bandwidth of 45 TB/s resulting in a total bandwidth of 90 TB/s – a theoretical maximum that, according to Graphcore, is 100 times higher than that of HBM2 graphics chip memory.

The C2 is compatible with Graphcore’s modified Poplar, a scalable graph programming framework for AI and machine learning. It boasts an integration with Google’s (NASDAQ:GOOGL 1309.15 1.00%) TensorFlow framework and provides a full training runtime in relation to the Open Neural Network Exchange – an ecosystem for interchangeable AI models. According to Graphcore, a preliminary compatibility with Facebook’s (NASDAQ:FB 193.15 -0.02%) PyTorch is anticipated to arrive in Q4 2019 with full compatibility arriving in early 2020.

The benchmarks published by Graphcore indicate that the C2 matches or exceeds the performance of the top AI chips from Nvidia (NASDAQ:NVDA 209.79 0.58%) and Google using algorithms written specifically for those rival platforms (you will find the full benchmarks here). Note that the code written explicitly for Graphcore’s hardware may be even more efficient. As a reference, the company trained one Bidirectional Encoder Representations from Transformers (BERT) variant – the BERT Base – in just 56 hours with a single IPU server that contained eight C2 cards. Moreover, according to the company’s claims, customers have reported, on average, an inferencing throughput that is 3 times higher along with latency improvements of 20 percent. As far as image recognition is concerned, the European search engine Qwant has claimed a 3.5 times improvement in its image search application since it began to use Graphcore chips.

The deal between Microsoft and Graphcore – a winning formula for Azure

In last December, Garphcore was valued at $1.5 billion through a $200 million funding round with contributions from the likes of Microsoft and BMW (ETR:BMW). For 2019, the company expects $50 million in revenue. The chipmaker employs over 200 engineers and is currently expanding its footprint in Norway, Taiwan, and the U.S.

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The corporate vice-president of Azure Compute at Microsoft, Girish Bablani, said: “The Graphcore offering extends Azure’s capabilities and our efforts here form part of our strategy to ensure that Azure remains the best cloud for AI.”

The AI chip market is currently quite crowded with the involvement of startups such as Cerebras along with tech giants such as Google, Apple (NASDAQ:AAPL 262.64 -0.69%), Amazon (NASDAQ:AMZN 1754.6 0.08%), and Facebook. Interestingly, the Californian start-up Cerebras – a Graphcore competitor – unveiled, in August, the world’s largest chip with a surface area that is slightly larger than a standard iPad in order to power AI applications.

Microsoft’s pre-millennium meteoric rise can be attributed to its partnership with Intel (NASDAQ:INTC 57.81 -0.14%) that resulted in Windows OS running on Intel’s processors. Now Microsoft hopes to dominate the AI age with this newfound partnership with Graphcore.

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Microsoft this morning announced a partnership with multiple companies for quantum computing capabilities running in its Azure cloud computing service during its “Ignite” developer conference, with one of the partners being industrial giant Honeywell. 

Honeywell’s head of its quantum effort, Tony Uttley, spoke with ZDNet about the announcement, explaining how quantum computing is on an evolutionary path from assisting conventional computing to someday usurping it. 

“For those who don’t even know Honeywell is in quantum, think of us now as a fully fledged participant and a real contender,” said Uttley. 

The Microsoft offering is dubbed “Azure Quantum.”

When asked if quantum computing requires quantum “supremacy” to be demonstrated, as Google did two weeks ago in Nature magazine, Uttley responded that it’s a matter of evolving the science through multiple periods of increasing sophistication.

“We think of three eras,” says Uttley. “First is the era in which quantum computers act as co-processors” for classical computers, to help accelerate some of the work that the classical computers do. Then comes the era of the “classically impractical,” in which some things that are feasible to do on the classical computer nevertheless might be worth doing in a quantum system nevertheless might be better done on a quantum machine for the dramatic speed-up purposes. 

And last comes the era of the “classically impossible,” perhaps things such as factoring large numbers into primes, are really be infeasible on a classical computer. 

“We are on the verge of classically impractical,” is how Uttley described the present situation.

More important at the moment than fancy algorithm demonstrations such as Google’s is the basic fidelity of any quantum device, perhaps. Although Microsoft has for a long time been developing its own quantum circuitry, the partnership with Honeywell offers the company access to Honeywell’s hardware made up of “trapped ions.” An ion, of course, is an atom that has a net positive or negative electrical charge. The trap in this case is a fabricated device, like a computer chip, that can be used to manipulate those ions, similar to moving electrons through the gates made up of silicon transistors.

Trapped ions is a scientific area of exploitation going back at least 24 years. Ions are perceived as having some desirable properties versus other quantum approaches, including the relative stability of the qubits created with them, thanks to relatively long “coherence times” of the qubits (the period of time during which the all-important quantum entanglement can be maintained.)

To Uttley, all this adds up to a more reliable quantum device than some other approaches, on average. 

“There’s a a lot of discussion of how many qubits do you have,” says Uttley. “But more rarely asked is, What can you do with those? Are they fully connectable?

“At least as important is the question of what is the fidelity” of the qubits, says Uttley, “how accurate are they.”

Although Honeywell has fabricated ion traps to build its computer, Uttley is short on details of the gate configurations and circuit configurations and technical details of the trap’s properties, while promising to reveal more information on the technical side at a future point. Some interesting technical material is provided in a just-published paper posted on the arXiv pre-print server, “Subspace benchmarking high-fidelity entangling operations with trapped ions.”

For the moment, says Uttley, the focus is going to be on working with customers in a beta release by the end of this year, with a more public unveiling in early 2020.

For Honeywell, as Uttley sees it, quantum is a natural extension of the company’s decades-long legacy in control systems. And quantum computing offers the potential to speed-up machine learning algorithms for various industrial applications that could be of special interest to Honeywell customers, such as  for optimization of petrochemical processes and for optimization for air traffic control, and any number of other things that can be defined as control problems first and foremost. 

(More information on Honeywell’s Quantum Solutions unit is available on the company’s Web site.)

Microsoft states in today’s announcement that “we’ve been working together with a global quantum community to innovate across every layer of the quantum stack — from applications and software down to control and devices.” Microsoft is also using offerings from startups 1Qbit, IonQ and QCI.

Microsoft announced users of the system, such as Case Western Reserve University.

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