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If there's one thing that practically everybody in the business world has on their resumes, it's Microsoft Office. Most of us have used Microsoft Office at some point, either in school or at a previous job. But do you really know Office well enough to count it as a skill that sets you apart from somebody else?
If you don't, you should. And this accredited Microsoft Office bundle can help you get there.
This three-part bundle is taught by International Open Academy, a leader in online professional development, personal growth, and goal fulfillment. This organization has been accredited by the International Council for Online Educational Standards (ICOES) and Training Qualifications UK (TQUK), demonstrating their excellence in online learning. As such, your training is accredited as well.
You probably know Microsoft Office a little bit, but this bundle will help you take your knowledge of Word, Excel, and PowerPoint to the next level. You'll learn how to properly format documents, embed objects from other MS Office programs, work with references, insert graphics and images into documents, track changes, and much more. The Excel training will help you navigate Excel and understand its core tools, formulas, and functions. You'll be able to sort, analyze, and interpret data, create visualizations, better organize your worksheets and data, and much more. Finally, the PowerPoint course will help you create eye-catching, engaging slideshows and presentations that clearly present information in a compelling manner. You'll learn how to create links, charts, and infographics to amp up your presentations and learn how to make presentations for a wide variety of projects.
If you don't know MS Office as well as you think you should, this MS Office training is for you. Get it on sale for just $29 now.
Prices subject to change.
Windows 365 is a new way to experience Windows 10 or Windows 11 as a cloud service, streaming the full Windows experience — including apps, data and settings — to any device from the Microsoft Cloud. Windows 365 also creates a new hybrid personal computing category called Cloud PC, which uses both the power of the cloud and the capabilities of the device to provide a full, personalized Windows experience.
Windows 365 is designed to supply organizations a new, flexible and secure way to empower distributed workforces, temporary and seasonal employees and workers with a need for specialized workloads.
With either Windows 10 now, or Windows 11 once it is generally available later this calendar year, individual users or information technology personnel can choose the size of the Cloud PC that best meets their needs with predictable per user per month pricing. With instant-on boot to their personal Cloud PC, users can stream their applications, tools, data and settings from the cloud across devices.
Enterprise IT can use Microsoft Endpoint Manager to procure, deploy and manage Cloud PCs for their organization. Small businesses can use a simple, self-service model to procure Cloud PCs for their organizations without a need for IT experience. For all businesses, Windows 365 simplifies Windows updates and leverages the power of the cloud to mitigate security risk.
From ISVs to systems integrators to managed services providers to OEMs, Windows 365 creates new opportunities for partners to extend new, full Windows experiences to the cloud.
Learn more about this update on Innovation Stories and the Microsoft 365 Blog, or download visual assets.
Microsoft has unveiled two new security service offerings that will help Boost the intelligence capabilities of an organization’s security operations: Defender Threat Intelligence and Defender External Attack Surface Management (EASM).
The new intel service is based on the merger of RiskIQ, Microsoft’s nation-state tracking team, Microsoft Threat Intelligence Center (MSTIC), and the Microsoft 365 Defender security research team.
Although it may look similar to other Microsoft services, the company said the new security service offerings are unique because they offer customers “direct access to real-time data” from Microsoft’s security signals.
Microsoft Defender External Attack Surface Management can detect unknown and unmanaged customer resources that are visible and accessible from the internet, giving defenders the same view an attacker has when selecting a target.
Ultimately, Defender EASM helps customers discover unmanaged resources that could serve as potential entry points for attackers.
“We’re providing intelligence across all of them and bringing that into your security team — not just to learn the latest news… but also to explore it, so if I see an indicator, I might explore where that might live on the network and connect that to what I’m seeing in my company. It’s like a workbench for analysts inside a company,” says Rob Lefferts, Corporate VP of Microsoft’s Modern Protection and SOC unit.
The sources for this piece include an article in ZDNet.
Last year I wrote about eight databases that support in-database machine learning. In-database machine learning is important because it brings the machine learning processing to the data, which is much more efficient for big data, rather than forcing data scientists to extract subsets of the data to where the machine learning training and inference run.
These databases each work in a different way:
Now there’s another database that can run machine learning internally: Snowflake.
Snowflake is a fully relational ANSI SQL enterprise data warehouse that was built from the ground up for the cloud. Its architecture separates compute from storage so that you can scale up and down on the fly, without delay or disruption, even while queries are running. You get the performance you need exactly when you need it, and you only pay for the compute you use.
Snowflake currently runs on Amazon Web Services, Microsoft Azure, and Google Cloud Platform. It has recently added External Tables On-Premises Storage, which lets Snowflake users access their data in on-premises storage systems from companies including Dell Technologies and Pure Storage, expanding Snowflake beyond its cloud-only roots.
Snowflake is a fully columnar database with vectorized execution, making it capable of addressing even the most demanding analytic workloads. Snowflake’s adaptive optimization ensures that queries automatically get the best performance possible, with no indexes, distribution keys, or tuning parameters to manage.
Snowflake can support unlimited concurrency with its unique multi-cluster, shared data architecture. This allows multiple compute clusters to operate simultaneously on the same data without degrading performance. Snowflake can even scale automatically to handle varying concurrency demands with its multi-cluster virtual warehouse feature, transparently adding compute resources during peak load periods and scaling down when loads subside.
When I reviewed Snowflake in 2019, if you wanted to program against its API you needed to run the program outside of Snowflake and connect through ODBC or JDBC drivers or through native connectors for programming languages. That changed with the introduction of Snowpark in 2021.
Snowpark brings to Snowflake deeply integrated, DataFrame-style programming in the languages developers like to use, starting with Scala, then extending to Java and now Python. Snowpark is designed to make building complex data pipelines a breeze and to allow developers to interact with Snowflake directly without moving data.
The Snowpark library provides an intuitive API for querying and processing data in a data pipeline. Using this library, you can build applications that process data in Snowflake without moving data to the system where your application code runs.
The Snowpark API provides programming language constructs for building SQL statements. For example, the API provides a
select method that you can use to specify the column names to return, rather than writing
'select column_name' as a string. Although you can still use a string to specify the SQL statement to execute, you benefit from features like intelligent code completion and type checking when you use the native language constructs provided by Snowpark.
Snowpark operations are executed lazily on the server, which reduces the amount of data transferred between your client and the Snowflake database. The core abstraction in Snowpark is the DataFrame, which represents a set of data and provides methods to operate on that data. In your client code, you construct a DataFrame object and set it up to retrieve the data that you want to use.
The data isn’t retrieved at the time when you construct the DataFrame object. Instead, when you are ready to retrieve the data, you can perform an action that evaluates the DataFrame objects and sends the corresponding SQL statements to the Snowflake database for execution.
Snowpark for Python is available in public preview to all Snowflake customers, as of June 14, 2022. In addition to the Snowpark Python API and Python Scalar User Defined Functions (UDFs), Snowpark for Python supports the Python UDF Batch API (Vectorized UDFs), Table Functions (UDTFs), and Stored Procedures.
These features combined with Anaconda integration provide the Python community of data scientists, data engineers, and developers with a variety of flexible programming contracts and access to open source Python packages to build data pipelines and machine learning workflows directly within Snowflake.
Snowpark for Python includes a local development experience you can install on your own machine, including a Snowflake channel on the Conda repository. You can use your preferred Python IDEs and dev tools and be able to upload your code to Snowflake knowing that it will be compatible.
By the way, Snowpark for Python is free open source. That’s a change from Snowflake's history of keeping its code proprietary.
The following sample Snowpark for Python code creates a DataFrame that aggregates book sales by year. Under the hood, DataFrame operations are transparently converted into SQL queries that get pushed down to the Snowflake SQL engine.
from snowflake.snowpark import Session
from snowflake.snowpark.functions import col
# fetch snowflake connection information
from config import connection_parameters
# build connection to Snowflake
session = Session.builder.configs(connection_parameters).create()
# use Snowpark API to aggregate book sales by year
booksales_df = session.table("sales")
booksales_by_year_df = booksales_df.groupBy(year("sold_time_stamp")).agg([(col("qty"),"count")]).sort("count", ascending=False)
Snowflake’s “getting started” tutorial demonstrates an end-to-end data science workflow using Snowpark for Python to load, clean, and prepare data and then deploy the trained model to Snowflake using a Python UDF for inference. In 45 minutes (nominally), it teaches:
The task is the classic customer churn prediction for an internet service provider, which is a straightforward binary classification problem. The tutorial starts with a local setup phase using Anaconda; I installed Miniconda for that. It took longer than I expected to get and install all the dependencies of the Snowpark API, but that worked fine, and I appreciate the way Conda environments avoid clashes among libraries and versions.
This quickstart begins with a single Parquet file of raw data and extracts, transforms, and loads the relevant information into multiple Snowflake tables.
Now that we’ve finished the ETL/data engineering phase, we can move on to the data analysis/data science phase.
Finally we create and deploy a user-defined function (UDF) for prediction, using more data and a better model.
You can go into more depth by running Machine Learning with Snowpark Python, a 300-level quickstart, which analyzes Citibike rental data and builds an orchestrated end-to-end machine learning pipeline to perform monthly forecasts using Snowflake, Snowpark Python, PyTorch, and Apache Airflow. It also displays results using Streamlit.
Overall, Snowpark for Python is very good. While I stumbled over a couple of things in the quickstart, they were resolved fairly quickly with help from Snowflake’s extensibility support.
I like the wide range of popular Python machine learning and deep learning libraries and frameworks included in the Snowpark for Python installation. I like the way Python code running on my local machine can control Snowflake warehouses dynamically, scaling them up and down at will to control costs and keep runtimes reasonably short. I like the efficiency of doing most of the heavy lifting inside the Snowflake warehouses using Snowpark. I like being able to deploy predictors as UDFs in Snowflake without incurring the costs of deploying prediction endpoints on major cloud services.
Essentially, Snowpark for Python gives data engineers and data scientists a nice way to do DataFrame-style programming against the Snowflake enterprise data warehouse, including the ability to set up full-blown machine learning pipelines to run on a recurrent schedule.
Cost: $2 per credit plus $23 per TB per month storage, standard plan, prepaid storage. 1 credit = 1 node*hour, billed by the second. Higher level plans and on-demand storage are more expensive. Data transfer charges are additional, and vary by cloud and region. When a virtual warehouse is not running (i.e., when it is set to sleep mode), it does not consume any Snowflake credits. Serverless features use Snowflake-managed compute resources and consume Snowflake credits when they are used.
Platform: Amazon Web Services, Microsoft Azure, Google Cloud Platform.
Copyright © 2022 IDG Communications, Inc.
Humans have a well-deserved reputation for being the weakest link in the cybersecurity of any size organization. Whether it's an IT specialist misconfiguring a firewall setting, a DevOps engineer failing to secure a cloud storage bucket, or a hapless business user falling for a phishing scam, the vast majority of cybersecurity breaches are primarily caused by human error creating exploitable vulnerabilities. The result is many avoidable weaknesses being pursued by criminal opportunists enabled by cheap, plentiful cybercrime tools of the trade.
Thankfully, the humans working in the security operations center (SOC), the Tier 1 and Tier 2 analysts on the front line of cyber defense, are the strongest link in cybersecurity operations. They must be kept in the loop, ideally performing higher-value tasks than keeping "eyes on the glass" to review security telemetry.
Identifying the meaningful alerts in high-volume event streams is the perfect job for correlation rules and unsupervised machine learning (ML) algorithms that combine human knowledge and threat intelligence with continuous learning and improvement. Machines can handle the speed and scale required for the initial screening of the high-volume stream of event logs and alerts. Also, algorithms don't get tired or have a lapse in attention, go on vacation, or call in sick.
Automating this facet of SOC operations allows these AI-based tools to do the tedious work of sifting out false positives and correlating and surfacing real alerts in real time. Automation can also go a step further, applying rules in playbooks to enrich alerts with context (which machine or user, what happened, when), contain suspicious activity in the network, and trigger an automatic response in well-defined use cases.
The result can be minimizing the volume of alerts by a factor of 10 or more, from 10,000 a day to 1,000 or less. This noise reduction saves up to 50% of expert SOC labor, dramatically increasing SOC efficiency and effectiveness.
For example, John in HR typically accesses two databases during regular business hours. An alert comes through that John has accessed a third database on a Saturday. Only a human can determine if this new behavior is anomalous but nonthreatening. After the SOC analyst notifies the IT department about the unexpected database activity, IT confirms that John has been granted temporary access to the additional data, which is HR-related.
After triage by junior SOC analysts, high-priority alerts are forwarded to SOC senior analysts. These skilled security certified are charged with investigating the alerts and identifying where an attack is coming from, the cybercrime groups behind the attack, methods they are using, lateral movement observed, and the dwell time of attackers. SOC experts also propose strategies for mitigation and eradication.
Humans are most essential when identifying attacks that cut across different systems, applications, and access methods. It was skilled humans who uncovered new activity on the part of Hafnium. The nation-state cybercriminals had been exploiting vulnerabilities in Microsoft Exchange servers to steal emails, compromise networks, and move laterally in affected organizations. These incursions took place for three months prior to discoveries credited by Microsoft to researchers at security firms Volexity and Dubex.
SOC analysts need not be concerned about job security in the face of ML and automation. Rather, they should welcome the improved productivity and freedom automation provides to use their intelligence and creativity for higher-value activities such as research, threat analysis, remediation, and threat hunting.
Big Tech earnings got off to a solid start last week when Microsoft (MSFT) and Google (GOOG) (GOOGL) reported stable revenue growth and margins that are unchanged from exact macro conditions. The strong margins were especially welcomed as many companies have been missing on operating margins and cash flow. Meanwhile, Microsoft delivered free cash flow of $17.8 billion and net profits of $16.7 billion along with upbeat guidance for the year. Similarly, Google reported strong free cash flow of $12.6 billion and net profits of $16 billion in the exact quarter.
The same was not true for Meta (META), which primarily stumbled on its Q3 guide. The company reported its first decline in revenue in company history and guidance for next quarter missed due to FX headwinds. Analyst expectations for Q3 were for $30.4 billion, or 5% growth. Instead, the company guided for $26 billion to $28.5 billion, or a YoY decline of 6% at the mid-point of the guidance with the current exchange rates creating a 6% headwind.
The company reported revenue of 13%, or 16% in constant currency, for a total of $69.7 billion. The operating margin was flat year-over-year, which is a win. Operating expenses grew 24% yet the operating margin was in line with previous quarters at 28% for $19.58 billion in operating income.
The net margin was a bit weaker than previous quarters in 2021 at $16 billion yet in line with last quarter. The company has free cash flow of $12.6 billion. The company has $125 billion in cash and marketable securities. The company reported EPS of $1.21 compared to $1.36 for the same period last year.
Search was stable given the current environment at 13.5% growth to $40 billion and this provided relief that not all ad spend has been paused. Search was strong last quarter at 24% growth to $40 billion, and was flat sequentially in terms of total dollar amount.
The effects of Google's large R&D department and advances in AI cannot be overstated when it comes to the resiliency of Search in the current environment. We are getting a very slight glimpse of what's to come for Google in terms of its advertising dominance.
The expectations were that YouTube would weigh on the report, yet YouTube provided a bit of growth at 5% year-over-year. The company was adamant that YouTube growth is low because of the tough comps. The tough comps were touched on many times, such as this: "the modest year-on-year growth rate primarily reflects lapping the uniquely strong performance in the second quarter of 2021."
Notably, Google Cloud slowed to 35.6% growth, down from 43.8% growth last quarter. This means Google Cloud is growing slower than Azure on a lower revenue base. This is something to monitor in the future.
Many tech companies are declining to supply guidance while Microsoft's management provided strong guidance in both Q1 FY2023 and for FY2023. For Q1 FY2023, management provided a 10% guide across product lines for next quarter (this includes FX headwinds) and also provided guidance for fiscal year 2023 ending in June: "We continue to expect double-digit revenue and operating income growth in both constant currency and U.S. dollars. Revenue growth will be driven by continued momentum in our commercial business and a focus on share gains across our portfolio."
Revenue grew by 12% YoY to $51.9 billion (missed Wall Street analysts' estimates by 0.94%) and EPS came in at $2.23 (missed estimates by 2.9%). The strong US dollar negatively impacted the revenue by $595 million and EPS by $0.04. Microsoft Cloud revenue grew by 28% YoY to $25 billion. The company's results are good considering the various macro uncertainties, China lockdowns, and the strong US dollar. FY2022 revenue grew by 18% YoY to $198.3 billion and net income increased by 19% YoY to $72.7 billion.
The company's gross profits increased 10% YoY to $35.4 billion. The gross margin was 68.3% when compared to 69.7% in the same period last year. Excluding the impact from the change in the accounting estimate, the gross margin was relatively unchanged.
The operating income increased by 8% YoY to $20.5 billion. The operating margin was 39.6% compared to 41.4% in the same period last year. Excluding the impact from the change in the accounting estimate and FX, the operating margin would be relatively unchanged.
The company's cash flows continued to be strong in the exact quarter. Cash from operations grew by 8% YoY to $24.6 billion (47% of revenue) and free cash flow increased by 9% YoY to $17.8 billion (34% of revenue). The company has cash and investments of $104.8 billion and debt of $49.8 billion.
Despite weakness in PCs, the company's other segments continue to grow. Intelligent Cloud grew 20% YoY to $20.9 billion and Productivity and Business Processes segment grew 13% YoY to $16.6 billion.
The company also made an accounting change in the useful life for server and network equipment assets from four to six years which will extend the depreciation expenses for the company.
Amy Hood said in the earnings call, "First, effective at the start of FY '23, we are extending the depreciable useful life for server and network equipment assets in our cloud infrastructure from 4 to 6 years, which will apply to the asset balances on our balance sheet as of June 30, 2022, as well as future asset purchases.
As a result, based on the outstanding balances as of June 30, we expect fiscal year '23 operating income to be favorably impacted by approximately $3.7 billion for the full fiscal year and approximately $1.1 billion in the first quarter."
The market does not need a perfect quarter for Q2 given the numerous headwinds facing tech companies. What the market does need is a sign that a company may have bottomed and is able to guide growth (even if minimal) from Q2 to Q3.
In Q2, Meta's revenue declined for the first time in history. This was expected. However, what was not expected was the lower guide for the next quarter. The company guided for $26 billion to $28.5 billion, or a YoY decline of 6% at the mid-point of the guidance. The guidance takes into consideration the weak advertising demand the company experienced in the exact quarter and also the foreign exchange headwinds of 6%. The investors were expecting a return of growth in the next quarter.
The company had a slight beat on DAUs at 1.97 billion versus 1.96 billion expected. Monthly users were 2.93 billion and slightly missed expectations of 2.94 billion.
Operating expenses rose 22% YoY to $20.4 billion. This led to the drop in the operating margin to 29% in the exact quarter compared to 43% in the same period last year. It also led to the 36% YoY drop in the net income to $6.69 billion. The EPS came at $2.46 compared to $3.61 in Q2 2021.
The company is looking to further reduce the operating expenses for the year to $85 billion to $88 billion from the last quarter guidance of $87 billion to $92 million and the prior estimate of $90 billion to $95 billion.
We discussed why Meta is likely to continue to face headwinds in an in-depth webinar here.
Apple (AAPL) released strong results despite the challenging macro environment, strong US dollar, and supply chain issues. Revenue grew by 1.9% YoY to $83 billion, which was in-line with the analysts' estimates. It reported EPS of $1.20, which beat estimates by $0.04 (4% beat).
The product segment revenue declined marginally by 0.9% YoY to $63.4 billion and the services segment revenue grew by 12% YoY to $19.6 billion. The company's installed base of active devices reached an all-time high. It had more than 860 million of paid subscriptions, up 160 million in the past year.
The company did not supply exact revenue guidance for the next quarter. Tim Cook, CEO of the company, said in the earnings call, "We're going to accelerate revenues in the September quarter as compared to the June quarter and will decelerate on the Services side."
The company's gross margin was 43.26%, compared to 43.75% in the previous quarter and 43.29% in the same period last year. It was above the management's guidance of 42% to 43%.
Net income was $19.4 billion or $1.20 per share compared to $21.7 billion or $1.30 per share in the same period last year. It beat the analysts' EPS estimates by $0.04.
The company had cash and marketable securities of $179 billion and debt of $120 billion. The company reported strong operating cash flows of $23 billion (28% of revenue). The company returned over $28 billion to the shareholders in the exact quarter in the form of dividends and share repurchases.
Royston Roche, Equity Analyst at the Tech Insider Network, contributed to this article.
Press release content from Globe Newswire. The AP news staff was not involved in its creation.
JERSEY CITY, N.J., Aug. 04, 2022 (GLOBE NEWSWIRE) -- AvePoint (NASDAQ: AVPT), the most advanced SaaS and data management platform provider, today announced that its SaaS modern learning product line AvePoint EduTech is now MaivenPoint. The new name is part of a comprehensive rebranding effort across all aspects of the brand identity to reflect the transformation of education to meet every learner where they are and provide a holistic, collaborative and inspired learning experience.
“It’s clear that the way people learn, whether in the classroom or in the office, has changed dramatically,” said Dr. Tianyi Jiang, Co-Founder and Chief Executive Officer, AvePoint. “The future of education is an experience where the learning should come to you in a way that is intuitive and inspiring. Our rebranding reflects this shift from technology-driven learning to user-driven learning. Our mission with MaivenPoint is to make your learning experience limitless and inspire everyone to achieve their aspirations.”
MaivenPoint™ enables educators, students and organizations to create engaging learning experiences to meet their unique and evolving needs, including four key product suites:
Continued Progress and Industry Recognition to Make Learning Limitless
Several strategic acquisitions have been made in the past year that build upon its heritage of transformative technology experiences for higher education to extend a seamless, collaborative learning experience that stretches beyond the classroom.
Combined Knowledge Ltd, one of the world’s premier Microsoft training providers, was acquired to help advance the company’s mission of providing quality digital training enablement for organizations worldwide. Additionally, i-Access, a leading provider of training management solutions, was acquired to further content offerings for corporate learning and development. The R&D and product teams from both companies are now fully integrated to build upon MaivenPoint’s already robust capabilities to address the learning needs across industries.
The evolution to create engaging learning experiences that meet the needs of today’s learner continue to garner significant industry recognition, including:
To learn more, please visit https://www.maivenpoint.com.
Collaborate with Confidence. AvePoint provides the most advanced platform for SaaS and data management to optimize SaaS operations and secure collaboration. More than 9 million cloud users rely on our solutions. Our SaaS solutions are also available to managed service providers via more than 100 cloud marketplaces, so they can better support and manage their small and mid-sized business customers. Founded in 2001, AvePoint is a five-time Global Microsoft Partner of the Year and headquartered in Jersey City, New Jersey. For more information, visit https://www.avepoint.com.
AvePoint uses the https://ir.avepoint.com/ website as a means of disclosing material non-public information and for complying with its disclosure obligations under Regulation FD.
Forward Looking Statements
This press release contains certain forward-looking statements within the meaning of the “safe harbor” provisions of the United States Private Securities Litigation Reform Act of 1995 and other federal securities laws including statements regarding the future performance of and market opportunities for AvePoint. These forward-looking statements generally are identified by the words “believe,” “project,” “expect,” “anticipate,” “estimate,” “intend,” “strategy,” “future,” “opportunity,” “plan,” “may,” “should,” “will,” “would,” “will be,” “will continue,” “will likely result,” and similar expressions. Forward-looking statements are predictions, projections and other statements about future events that are based on current expectations and assumptions and, as a result, are subject to risks and uncertainties. Many factors could cause actual future events to differ materially from the forward-looking statements in this press release, including but not limited to: changes in the competitive and regulated industries in which AvePoint operates, variations in operating performance across competitors, changes in laws and regulations affecting AvePoint’s business and changes in AvePoint’s ability to implement business plans, forecasts, and ability to identify and realize additional opportunities, and the risk of downturns in the market and the technology industry. You should carefully consider the foregoing factors and the other risks and uncertainties described in the “Risk Factors” section of AvePoint’s most exact Quarterly Report on Form 10-Q, its registration statement on Form S-1 and related prospectus and prospectus supplements, and in its subsequent filings made to the SEC. Copies of these and other documents filed by AvePoint from time to time are available on the SEC’s website, www.sec.gov. These filings identify and address other important risks and uncertainties that could cause actual events and results to differ materially from those contained in the forward-looking statements. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and AvePoint does not assume any obligation and does not intend to update or revise these forward-looking statements after the date of this release, whether as a result of new information, future events, or otherwise, except as required by law. AvePoint does not supply any assurance that it will achieve its expectations.
NEW YORK, NY – Aug. 3, 2022 – Deep Instinct, the first company to apply end-to-end deep learning to cybersecurity, today delivered Deep Instinct Prevention for Applications, an agentless, on-demand, antimalware solution for the enterprise that is device and operating system agnostic. This new offering revolutionizes threat protection beyond the endpoint with flexible, deploy anywhere, in-transit file scanning via API to quickly return a malicious versus benign verdict at enterprise speed. It protects any web application or cloud storage from malicious content while fully ensuring data privacy.
Until now, financial services and other industries with petabytes of data in motion each day have been at high risk from uploaded malicious content that can be detonated upon get from storage. These organizations have been relying on antiquated solutions that are slow, consume vast CPU and memory resources, and miss unknown malware, leaving this threat segment underserved.
In the wake of the pandemic, fintech transactions alone rose by 13% and their volume by 11%, indicating significant industry growth. With tens of millions of files in transit each day connected to high-value trading data, mortgage applications, insurance claims, and other sensitive information, financial institutions are at risk from unchecked malicious uploads or downloads and lack viable options to ensure infected content is not a threat to their operations or their customers. As threat actors seek alternative points of entry into enterprise environments, this risk factor will only increase. In fact, one study found that 35% of "never-before-seen" malware files were hidden in Microsoft Office and PDF files.
"As threat actors compromise points of entry beyond the endpoint, financial services institutions that exchange tens of millions of files each day are at increased risk. This was primarily created by the failure of established antivirus, network and other solutions to evolve. They are slow, can't scale or process high volumes of daily traffic or handle large file sizes, and often resort to sandboxing. As a result, they continue to miss unknown threats, and incur high infrastructure costs. "That's the worst of both worlds for an enterprise," said Guy Caspi, CEO and Co-Founder of Deep Instinct. "Deep Instinct is disrupting the cybersecurity status quo by setting a new standard for preventing malicious files, both known and unknown, before they reach storage."
Deep Instinct Prevention for Applications provides organizations an on-demand, high-speed scanning solution that prevents >99% unknown malware hidden in files and easily scales to scan tens of millions of files per day. With very low CPU requirements, a low false-positive rate of <0.1%, near zero latency, and low processing requirements, Deep Instinct provides the most innovative solution for this underserved threat gap. A typical traditional AV solution is ineffective at preventing unknown malware, will require sandbox and cloud intelligence checks, and takes an average of 90 seconds to 3 minutes to make decisions. Traditional AV/sandbox solutions are ineffective at solving this issue as they are easily evaded and slow to respond. This increases risk and negatively affects the user experience but impacts the business by slowing down critical processes.
"Deep Instinct is addressing a major pain point of today's enterprise — their exposure to an ever-expanding number of attack vectors," said David OLeary, Field CISO – Sr. Director, Global Cybersecurity, SHI. "We look forward to bringing this truly unique solution to our enterprise customer base and helping them better protect themselves and their customers from malicious content as they exchange important files every day."
Other benefits of Deep Instinct Prevention for Applications include the following:
For more product information and case studies, please visit: https://deepinstinct.com/prevention-for-applications.
Deep Instinct takes a prevention-first approach to stopping ransomware and other malware using the world's first and only purpose-built, deep-learning cybersecurity framework. We predict and prevent known, unknown, and zero-day threats in <20 milliseconds, 750X faster than the fastest ransomware can encrypt. Deep Instinct has >99% zero-day accuracy and promises a <0.1% false positive rate. The Deep Instinct Prevention Platform is an essential addition to every security stack — providing complete, multilayered protection against threats across hybrid environments. For more, visit www.deepinstinct.com.
The ergonomic, untethered, self-contained holographic mixed reality headset provides out of box value with enterprise-ready applications and is backed by the reliability, security, and scalability of Microsoft’s Cloud and AI Services.
20 July, 2022; Dubai, United Arab Emirates – Microsoft today announced the availability of its industry leading HoloLens 2 mixed reality headset in the UAE. HoloLens 2 provides the most comfortable, intuitive, and immersive mixed reality experience available, delivering enterprise value across key sectors with Dynamics 365 business applications and industry ISV solutions, backed by the reliability, security, and scalability of Microsoft Azure.
Microsoft’s comprehensive mixed reality platform blends the physical and digital worlds, across the spectrum from augmented reality to virtual reality, extending computing beyond two-dimensional screens to fundamentally transform productivity and optimize operations. Thousands of leading organizations across the globe in industries such as manufacturing, construction, healthcare, retail, and education are using HoloLens 2 and Azure mixed reality services to save significant costs, reduce energy consumption and operational emissions, Boost learning and retention, enhance the delivery of patient treatment, and boost employee and customer satisfaction.
“Once again, Microsoft demonstrates its commitment to the UAE as we continue to invest and bring innovative solutions to market,” said Ihsan Anabtawi, COO and CMO, Microsoft UAE. “HoloLens 2 is a cutting-edge device ahead of its time, with AI-powered holograms that respond to commands and interact with real-world surfaces in real time. We are confident that enterprises in the UAE and beyond will leverage it to accelerate their digital transformation journeys and contribute to sustainable economic growth by innovating with confidence and collaborating without boundaries.”
A Forrester Total Economic Impact Study commissioned by Microsoft showed that HoloLens 2 offers a 177 percent return on investment (ROI) over three years, as well as improvements to employee health and safety, business continuity, customer experience, and customer outcomes. “As we shift to the next computing paradigm, Microsoft is excited to upskill every business and every developer to build secure, collaborative metaverse experiences using best-in-class hardware, intelligent cloud services and cross-platform tools.” said Ksenia Ternavskykh, Mixed Reality Product Marketing Lead, Microsoft Middle East and Africa. “By starting with mixed reality, organizations can derive significant and quantifiable impact today and build necessary capabilities to be ready for the future.”
HoloLens 2 enables organizations to empower their workforce from day one with mixed reality apps from Microsoft Dynamics 365 and offers over 200 applications from its rich partner ecosystem, across Independent Software Vendors, System Integrators and Digital agencies, to address unique industry-specific use-cases. HoloLens 2 will act as a business catalyst and empower partners to create new value, increase revenue, and Boost customer relationships by offering mixed reality capabilities.
Redington has been appointed as the sole distributor for HoloLens 2 in the UAE and has been fully onboarded together with a number of partners.
“As a market leader in broadline and value-added distribution, boasting a wide portfolio of world-class brands, Redington Gulf is uniquely positioned to offer end-to-end solutions with Microsoft HoloLens 2. With the combined strengths of both our business divisions – Volume and Value, our extensive partner network can craft creative and customized solutions with Microsoft HoloLens 2 to add unparalleled value for their customers’ operations. We have seen a high demand for mixed reality across a variety of industries and together with our strong partner ecosystem, we are primed to help customers leverage mixed reality solutions and become more innovative than they ever thought possible,” said Viswanath Pallasena, CEO, Redington Gulf.