Black Berry introduces Cloud-Based Intelligent Security Solution

| August 8, 2019

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BlackBerry has unveiled BlackBerry Intelligent Security, a cloud-based solution that leverages adaptive security, continuous authentication and artificial intelligence (AI) to provide mobile endpoint protection in zero-trust environments.BlackBerry Intelligent Security is built on top of the BlackBerry Spark Enterprise of Things (EoT) platform. It works in conjunction with the CylancePERSONA endpoint behavioral analytics solution to deliver real-time adaptive security with AI for all endpoints.BlackBerry Intelligent Security uses contextual and behavioral factors to adapt security requirements and calculate a unique risk score for each interaction, BlackBerry indicated. It then uses the risk score to grant a mobile user access to specific device applications and services as defined by IT administrators.

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CloudBrix, LLC

CloudBrix is a U.S. based support company providing services 24/7 to organizations of all sizes. Our technologists provide expert design, engineering and support services to customers wishing to outsource their high level IT staffing completely, or where they simply wish to supplement their existing department with greater technical knowledge and resources.

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How to Master Multi-Cloud Data Complexities

Article | May 20, 2020

The current patterns of cloud migration include simple “lift and shift,” which moves data with as little work as possible, typically by refactoring or redoing the applications and data so they work more efficiently on a cloud-based platform. More and more migrations include multi-cloud, which contributes to the appearance of new data complexity issues. When leveraging multi-cloud architectures, it’s important for IT leaders and cloud professionals to rethink how to deal with data complexity. If businesses are tasked with this massive and growing data management problem, it seems to me they ought to get their IT house in order. That means across a vast heterogeneity of systems, deployments, and data types. That should happen in order to master the data equation for your lines of business applications and services. Table of Contents What is multi-cloud? Why use multiple clouds? How to manage multi-cloud data complexities? What is multi-cloud? Multi-cloud is the use of two or more cloud computing services, including any combination of public, private, and hybrid. The end result is the capacity to orchestrate resources across multiple private or public cloud platforms that contain multiple cloud vendors, accounts, availability zones, or regions or premises. Why use multiple clouds? The three most important benefits of utilizing multiple clouds are: High availability – The multi-cloud provides protection for an organization’s data storage against threats. If a cloud is unavailable, other clouds remain online to run applications. Flexibility – Multi-cloud gives businesses the option to select the “best” of each cloud to suit their particular needs based on economics, location, and timing. Avoid Vendor Lock-In – This allows the application, workload, and data to be run in any cloud based on business or technical requirements at any given time. Cost effectiveness – Multi-cloud enables businesses to control their costs by optimizing the public cloud and choosing infrastructure vendors based on price. Public cloud services deliver functionality without having to hire personnel. Multi-cloud allows you to choose the right platform for your application and customers while using the best features from each cloud service provider. This gives companies the flexibility they need to select the “best” of each cloud to suit their particular needs based on economics, location, and timing. Multi-cloud also provides protection against the failure of a single cloud platform. Large enterprises may also be able to maximize the benefits of different infrastructure vendors that are competing on price for their business (smaller companies won’t have this luxury). Cloud is very different from your internal IT stuff — the way you program it, the way you develop applications. It has a wonderful cost proposition, at least initially. But now, of course, these companies have to deal with all of this complexity. - Martin Hingley, President and Market Analyst, IT Candor Limited Read more: How To Derive Data Insights In Hybrid Cloud Model And Drive Innovation How to manage multi-cloud data complexities? The reasons for the rising data complexity issues are fairly well known and include the following: The rising use of unstructured data that doesn’t have native schemas. Schemas are typically defined at access. The rising use of streaming data that many businesses employ to gather information as it happens and then process it in flight. The rise of IoT devices that spin off massive amounts of data. The changing nature of transactional databases, moving to NoSQL and other non-relational models. The continued practice of binding single-purpose databases to applications. Finally, and most importantly, the rise of as-a-service cloud-based and cloud-only databases, such as those now offered by all major cloud providers that are emerging as the preferred databases for applications built both inside and outside of the public clouds. Moreover, the use of heterogeneous distributed databases within multi-cloud architectures are preferred. Challenge of multi-cloud For the most part, those who build today’s data systems just try to keep up rather than get ahead of data complexity issues. The migration of data to net-new systems in multi-clouds is more about tossing money and database technology at the problem than solving it. Missing is core thinking about how data complexity should be managed, along with data governance and data security. We’re clearly missing the use of new approaches and helpful enabling technology within multi-cloud deployments that will remove the core drawbacks of data complexity. The challenge is that you need a single version of the truth. Lots of IT organizations don’t have that. Data governance is hugely important; it’s not nice to have, it’s essential to have. - Martin Hingley, President and Market Analyst, IT Candor Limited The core issue is to move toward application architectures that decouple the database from the applications, or even move toward collections of services, so you can deal with the data at another layer of abstraction. The use of abstraction is not new, but we haven’t had the required capabilities until the last few years. These capabilities include master data management (MDM), data service enablement, and the ability to deal with the physical databases using a configuration mechanism that can place volatility and complexity into a single domain. Virtual databases are a feature of database middleware services that technology suppliers provide. They serve to drive a configurable structure and management layer over existing physical databases, if that layer is in the requirements. This means that you can alter the way the databases are accessed. You can create common access mechanisms that are changeable within the middleware and do not require risky and expensive changes to the underlying physical database. Moving up the stack, we have data orchestration and data management. These layers provide enterprise data management with the ability to provide services such as MDM, recovery, access management, performance management, etc., as core services that exist on top of the physical or virtual databases, in the cloud or local. Moving up to the next layer, we have the externalization and management of core data services or microservices. These are managed, governed, and secured under common governance and security layers that can track, provision, control, and provide access to any number of requesting applications or users. ACT NOW Most enterprises are ignoring the rapid increase of data, as well as that of data complexity. Many hope that something magical will happen that will solve the problem for them, such as standards. The rapid rise in the use of multi-cloud means that your data complexity issues will be multiplied by the number of public cloud providers that end up being part of your multi-cloud. So, we’ll see complexity evolve from a core concern into a major hindrance to making multi-cloud deployment work effectively for the business. What’s needed now is to understand that a problem exists, and then think through potential solutions and approaches. Once you do that, the technology to employ is rather easy to figure out. Don’t make the mistake of tossing tools at the problem. Tools alone won’t be able to deal with the core issues of complexity. Considering the discussion above, you can accomplish this in two steps. First, define a logical data access layer that can leverage any type of back-end database storage system. Second, define metadata management with the system use of both security and governance. The solution occurs at the conceptual level, not with the introduction of another complex array of technology on top of already complex arrays of technology. It’s time to realize that we’re already in a hole. Stop digging. Read more:Flexible building blocks for the new cloud and data-driven world

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In the Face of a Pandemic, Cyberattackers Seek to Take Advantage

Article | March 17, 2020

Cyberattackers live for moments of crisis and confusion. Government agencies and companies already stretched thin are at their most vulnerable, and cyberattackers are all too willing to apply overwhelming pressure to maliciously disrupt operations or gain some financial benefit. As the world struggles to address the mounting challenges of the pandemic, we have already seen early examples of this. For example, news broke this week that the Department of Health and Human Services (HHS) had been hacked over the weekend.

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Top 4 Advantages of Merging Cloud Computing in Artificial Intelligence

Article | September 8, 2021

We live in the modern era of Artificial Intelligence (AI), where machines replicate human behavior. As a result, it is capable of performing many functions that are attributed to humans. According to Statista, the global AI market would reach USD 89 billion annually by 2025. The credit of the overall growth goes to the integration of cloud computing in artificial intelligence. The customized merger of these two modern technology domains has enhanced the lives of millions. In fact, in our daily life, we often use artificial intelligence when we command cloud-based digital assistants such as Siri, or Alexa, ask for directions on our phones, or get real-time traffic alerts on our GPS. Cloud Computing and Artificial Intelligence- An Overview With such an impactful collaboration, businesses are set to achieve rapid digital transformation to live up to the competitive edge. But before we ponder upon the business benefits of cloud computing and artificial intelligence integration, let us have a generic synopsis of both technologies. Cloud computing provides computing services for system resources (servers), cloud storage, databases, networking, applications, analytics, and intelligence, as and when required by the user. It assists you to share the resources, accelerate innovation, manage your infrastructure efficiently, scale with your business demands and optimize your capital expense by paying only for cloud services that you use. Artificial Intelligence, widely known as AI, is the induced virtual intelligence in machines to make them capable with intellectual powers similar to humans. The AI technology enables machines to think logically, learn from past activities, ascertain the meaning, and simplify actions. Artificial Intelligence has several applications such as text analytics, expert systems, machine language translation, speech recognition, natural language processing, sentiment analysis, and many more. How Does the Collaboration of Cloud Computing and Artificial Intelligence Help a Business? According to a study by McKinsey, across 19 different business domains and above 400 use cases, AI could create USD 3.5 trillion and USD 5.8 trillion per year in value. In addition, AI teaming up with the cloud computing environment makes organizations efficient, tactical, and insight-driven. Following are the vital four advantages of cloud computing and AI collaboration. Performs Data Mining and Analytics Cloud stores disparate data, which needs sorting and analysis to derive their hidden narratives. Artificial intelligence runs analytics to process the data, identify the pattern and determine the predictive algorithm to extract useful information. This process is known as data mining. Further, AI helps you to manage core workflows by leveraging cloud efficiency to ease out transactional processes. The integration of cloud computing with artificial intelligence supports your business in data management, determine actionable insights, deliver customer services, and augment workflows. Moreover, it equips you with accurate insights as there is no scope for human-interceded errors. Cloud Security in Real-Time Cloud Security in itself is a vast topic that engages expert’s bandwidth. However, its link with the role of AI in cloud computing recognizes its capability to detect anomalies while processing the large amount of data stored in the cloud. It can instantaneously provide an alert in real-time. Additionally, AI can also identify unauthorized attempts to access any network and helps you to block them promptly. Allows Unhindered Data Access and Improves Decision-Making Capabilities The merger of AI in cloud computing can help you avert complications related to data inaccessibility and boost your overall performance. Further, the combination helps you to mitigate potential risks in your cloud environment well in advance. As a result, it enables you to make the right decision for your business. Further, AI can facilitate seamless data transfer between on-prem and cloud environments. Maintains Cost-effectiveness and Improves Productivity The combination of cloud computing and AI intends to reduce overall costs. The merger helps you control the cost of implementing Artificial Intelligence in your business ecosystem because you don’t have to set up a separate data center. As a result, AI applications can significantly save capital expense concerning expenditure on arrangements for infrastructure. Further, it reduces the cost incurred due to manual operations and saves time to enhance your business productivity. Final Thoughts According to a recent conversation with Media 7, James Lee, Managing Director and Head of Financial Services, Analytics and Cloud Transformation at PwC, said, “A secure cloud data platform is fundamental to every successful digital transformation.” In the present competitive era, businesses are constantly looking for innovative solutions to secure their data, ensure their overall development. Cloud computing and artificial intelligence are advanced combinations to capture market attention and make a global presence. It helps you automate your processes, secure your operational data and network, control cost, boost your productivity and get accurate insights for better decision-making. Frequently Asked Questions What is the Use of AI in Cloud Computing? Businesses use AI-driven cloud computing to perform efficiently, plan effectively to extract data-driven insights. AI helps to simplify complex and redundant operations to improve productivity, perform analytics without any manual effort. Which are the Best Cloud Platforms for AI? Some of the best cloud platforms for AI are as follows: Microsoft Azure Google Cloud Machine Learning Engine Amazon ML platform services INTEL Nervana Platform Salesforce Einstein suite { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is the Use of AI in Cloud Computing?", "acceptedAnswer": { "@type": "Answer", "text": "Businesses use AI-driven cloud computing to perform efficiently, plan effectively to extract data-driven insights. AI helps to simplify complex and redundant operations to improve productivity, perform analytics without any manual effort." } },{ "@type": "Question", "name": "Which are the Best Cloud Platforms for AI?", "acceptedAnswer": { "@type": "Answer", "text": "Some of the best cloud platforms for AI are as follows: Microsoft Azure Google Cloud Machine Learning Engine Amazon ML platform services INTEL Nervana Platform Salesforce Einstein suite" } }] }

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Cloud computing the ultimate virus protection

Article | April 13, 2020

Cloud communications are playing a vital part in making remote work actually work, according to Nicholas Kyriakides, expert in cloud-based communications and COO of netTALK Connect. Kyriakides outlines why this is so.According to Kyriakides, cloud communications are playing a vital part in making remote work actually work. Such solutions have allowed companies to quickly turn to remote work to keep their employees safe while keeping operations going without any major obstacles.Kyriakides explains further that the current situation has created a great testing ground for the capabilities of remote work and cloud-communication use at a truly massive scale that could bring long-lasting changes about how we approach remote work and communication in business.Digital Journal spoke with Nicholas Kyriakides (netTALK Connect) about who to best implement cloud-based communications.

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CloudBrix, LLC

CloudBrix is a U.S. based support company providing services 24/7 to organizations of all sizes. Our technologists provide expert design, engineering and support services to customers wishing to outsource their high level IT staffing completely, or where they simply wish to supplement their existing department with greater technical knowledge and resources.

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