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102

Leveraging AI/ML Frameworks for Advanced Linux Security Solutions

As a Linux security admin, you understand the critical role of robust, reliable, and secure systems in any computing environment. But as AI and Machine Learning expand their horizons into AI frameworks like TensorFlow and PyTorch, there's also added responsibility and opportunity. When integrated effectively, these frameworks can provide invaluable insights by processing vast amounts of data more quickly than traditional methods. . Frameworks like Scikit-Learn and Keras offer straightforward methods of implementing ML algorithms and creating neural networks, making your security measures smarter and more proactive. With tools such as OpenCV, you can dive into computer vision tasks to better recognize and mitigate visual threats to your system. While learning curve and hardware compatibility may present hurdles, their benefits far outweigh them. Adopting AI technologies today not only means staying current but is an investment against the emerging threats of tomorrow. I'll introduce my favorite AI and ML frameworks on Linux and their unique benefits. I'll also share practical tips for overcoming challenges associated with implementing these frameworks in your Linux environment. The Power of TensorFlow TensorFlow has quickly become a household name in AI for good reason. It is indispensable for complex data analysis and is primarily designed to assist in deep learning tasks and neural network training. TensorFlow offers security admins a means of building models to detect suspicious traffic patterns or behavior and predict and identify suspicious events more quickly than traditional approaches can do. Furthermore, TensorFlow boasts robustness features like GPU acceleration support and automatic differentiation needed for efficient neural network training - two factors which are paramount when working on Linux security management tasks such as network monitoring or cyber defense activities. TensorFlow can be dauntingly complex to master, yet once learned, it can significantly enhance theidentification of threats preemptively. Linux offers seamless integration for TensorFlow that ensures full use of its computing strength with NVIDIA's CUDA and TensorRT technologies, ensuring you take advantage of every available computing resource on your hardware. PyTorch: Flexibility and Efficiency PyTorch stands out from other frameworks with its dynamic computation graph, setting it apart from others that use static graphs. Its fluid nature makes it more intuitive and user-friendly, making it ideal for iterative or experimental work such as security-related AI tasks. Using PYTorch, you will experience quicker prototyping times and more adaptable Machine Learning models! PyTorch offers solid support for cloud-based platforms and edge computing applications in Linux environments—two increasingly relevant aspects of cybersecurity. Larger deployments may need additional optimizations if scaling operations are planned. Simplifying Machine Learning with Scikit-Learn Scikit-Learn can simplify traditional ML tasks with its simple yet efficient implementation of fundamental algorithms like regression, classification, and clustering. If your goal is to enhance security through predictive analytics or anomaly detection without incurring deep learning's complexities, Scikit-Learn provides an ideal starting point. Scikit-Learn works seamlessly with Python environments on Linux, causing no cross-compatibility issues or delays in use. As a straightforward tool that delivers impressive results, Scikit-Learn is an invaluable addition to a Linux administrator's toolbox. However, its main drawback is that it is unsuitable for deep learning tasks, and more specialized frameworks will likely be necessary. Keras: High-Level Neural Network API Keras provides a high-level interface for neural network development, sitting atop more complicated engines such as TensorFlow. Suppose the direct use of TensorFlow seems daunting. In that case, Keras may simplify the experience by providing user-friendlyAPIs explicitly designed to expedite prototyping and model development quickly and efficiently. Keras running on Linux offers simplicity in model creation and the power of TensorFlow as a backend. However, its backend engine limits it; therefore, any restrictions or requirements of TensorFlow still exist when used via Keras. Delving into Computer Vision with OpenCV OpenCV has emerged as an indispensable tool in cybersecurity's computer vision field. From image and video processing to more sophisticated functions like facial recognition and motion detection, OpenCV offers an impressive set of functions to boost security measures and strengthen protection. With OpenCV, Linux users can utilize GPU acceleration with CUDA to increase performance for intensive tasks. It suits scenarios requiring large volumes of visual data processing, such as surveillance or automated monitoring systems. Unfortunately, OpenCV's complexity requires considerable knowledge of computer vision algorithms for optimal use—potentially overwhelming beginners. Benefits of Using Linux for AI and Machine Learning Linux provides the ideal foundation for seamlessly integrating AI and Machine Learning frameworks. Its open-source nature, suited for innovation-minded communities like AI developers, enables unparalleled customization and optimization—two essential benefits when running resource-intensive workloads typical of AI applications. Linux provides stability and security —essential elements in fields where data integrity and system reliability are paramount. If you work as a security admin, Linux provides an impressive ecosystem of security tools with strong community support worldwide. Its compatibility with high-performance computing tools such as CUDA or cuDNN means your AI/ML models will perform to their fullest potential! Linux also allows for increased flexibility when customizing systems for AI workloads, providing better resource management - whether on a single workstation or multiple cloud instances.Plus, its wide selection of open-source AI tools helps ensure agility and adaptability even as technology changes around you. Overcoming Challenges Integrating AI and Machine Learning in Linux security practices offers many advantages; however, its incorporation can present difficulties as the learning curve can be steep. Many frameworks for AI/ML implementation require extensive understanding of Machine Learning principles and proficiency with Python or another programming language, such as Rust , to implement effectively. Hardware compatibility can also be an issue. Not all devices and drivers play well with Linux, leading to lengthy troubleshooting sessions. Ensuring that all your hardware meets these criteria and that appropriate drivers have been installed is crucial for smooth operations. Dependency management presents another significant obstacle. Ensuring all libraries and dependencies work well together can be complicated when dealing with various versions or potential conflicts. It requires an exacting and meticulous approach to setting up and maintaining your development environment. Our Final Thoughts on Embracing AI and Machine Learning as a Linux Admin At its heart, cybersecurity relies upon predicting, detecting, and countering threats before they become critical. AI/ML frameworks like TensorFlow, PyTorch, Scikit-Learn, Keras, and OpenCV each provide specific benefits that, regardless of any obstacles they present, can transform how we approach system security. An AI and ML environment within Linux is ideal for optimizing AI/ML models. Though the road may be long, reaching your destination - fortified security systems with increased insight - makes the effort worthwhile. Deploy these technologies now and arm yourself against tomorrow's threats with skills necessary for effective security management! Are you using these AI/ML frameworks in your Linux environment? We'd love to hear about your experience @lnxsec ! . Explore how AI and ML frameworks can enhance Linuxsecurity, overcoming challenges and improving threat detection.. linux, security, admin, understand, critical, robust, reliable, secure, systems. . Brittany Day

Calendar 2 Mar 05, 2025 User Avatar Brittany Day
102

Open-Source AI Frameworks for Linux Development and Innovation

It seems like artificial intelligence (AI) has made its way into nearly every facet of modern life. Programs like the Amazon Alexa, Apple’s Siri and Microsoft’s Cortana are used by millions of people around the world. By the year 2022, over 50 percent of the online searches performed will be done with AI and the power of the human voice. . One of the biggest problems that business owners and tech entrepreneurs face when trying to incorporate AI into their software and apps is the fact that this market is dominated by proprietary programs. Many of the major players in the world of voice-activated AI keep the technology and code that powers these innovative devices’ secrets. Over the past few years, a number of open-source AI projects have popped up on the Internet. Read below to find out more about these open-source projects and why using them is a good idea. Linux is Being Used For a Number of AI Open Source Projects For years, programmers, researchers and web hosting gurus have used Linux for building and hosting their creations. One of the biggest benefits of using Linux for these AI open-source projects is that it is a stable program made to be used in just about any IT architecture and infrastructure. Some developers have the misconception that Linux is full of unwanted surprises like OSX and Windows, but this is not true because these programs are open source. This means that anyone can tinker with the open-source code online, which is why Windows and OSX are not widely used for AI programs. Linux has both the versatility and security needed to run open-source AI projects. Powerful tech companies like Google even use a variant of the Linux distribution Ubuntu to power their machine learning programs. Using tools like Loggly allows you to find errors in your Linux-powered creations and fix them quickly. Utilizing the power of the Linux/AI technology on the market will require lots of time and research. The more you know about the tools available to you, the easier itwill be to choose the right ones. Microsoft Onboard is Designed to Empower Business Owners and Individuals While most developers and tech entrepreneurs are familiar with the Microsoft Cognitive Toolkit , they are not as familiar with the open-source AI project known as Microsoft Onboard. The main goal of this open-source program is to empower individuals and companies looking to use AI to further their interests. When used in conjunction with the toolkit, Microsoft Onboard allows for production-grade AI to be created and used. Not only can this AI help you evaluate and train neural networks, it also easily scales across a variety of GPUs. If you are looking to use AI to manage and utilize various data sets, Microsoft Onboard is a great open-source tool to use. Build and Share AI-Infused Apps with Acumos Are you looking for an open-source framework for building and deploying AI apps? If so, Acumos AI is a great option. The best part about this open-source program is the fact that it offers a standardized infrastructure stack and components needed to run a normal AI environment. With this standardized environment, data engineers are able to focus more on developing the core competencies of the app in question. Many people fail to realize that Acumos is part of an organization in the Linux Foundation that is designed to support machine learning and artificial intelligence innovation. The stated goal of this foundation is to provide important new technology to the developers and data scientists attempting to create AI-infused programs and apps. Caffe is Open Source Technology Based on Speed The team at Yahoo is also getting into the open-source AI game with the addition of the CaffeonSpark tool . This tool is designed to help developers with deep learning AI tasks. In essence, deep learning is a branch of AI that is used to help machines recognize the contents of video and the human voice. This same type of technology is used in the digital personal assistantsmentioned at the beginning of this article. IBM also has a deep learning program known as SystemAL. If you are attempting to develop a program that uses voice commands to operate it, using CaffeeonSpark is a great option. Embracing the AI Revolution If you are looking for ways to make your new app or software different and useful to consumers, then using AI to optimize it is important. In the past few years, more and more companies have started to embrace AI, which is why now is the time to get on board with this revolutionary technology. With the help of the open-source programs mentioned in this article, you can get the framework needed to build an AI-infused program. About the Author Ashley Lipman is an award-winning writer who discovered her passion for providing knowledge to readers worldwide on topics closest to her heart - all things digital. Since her first high school award in Creative Writing, she continues to deliver awesome content through various niches touching the digital sphere. . Investigate community-driven AI initiatives designed for the Linux platform, empowering programmers to build cutting-edge AI solutions seamlessly.. Open Source AI, AI Frameworks, Linux AI Development, Machine Learning Tools, AI Applications. . Brittany Day

Calendar 2 Feb 24, 2020 User Avatar Brittany Day
102

Explore Five Open-Source AI Tools Compatible With Linux Users

Linux is arguably software developers’ favorite OS. Over 14,000 contributors have invested countless hours in developing the Linux Kernel. With Linux becoming increasingly popular due to its security and flexibility, developers who are interested in artificial intelligence (AI) may want to explore the possibilities within the Linux environment. . AI can easily be tagged as the future of technology, even if we already see it at work today. Virtual assistants such as Siri for Apple, Cortana for Microsoft, and Alexa for Amazon, are just some of the real-world examples of AI at work. The healthcare industry also uses AI in health monitoring, prescription management, drug discovery, and clinical documentation. Marketing benefits from AI as well, particularly in discovering trends, boosting revenue, and demand forecasting. As AI becomes more and more ingrained in our daily lives through consumer products, we can’t help but be concerned that proprietary software will comprise the market. And we are not talking about a million-dollar market, but a bigger one that may reach US$118.6 billion by 2025 . Many industries and end-users would thus benefit from more open-source AI projects and tools for developers’ use. That would save tons of individuals and companies money to build their own AI-powered apps. In this post, we explore five open-source AI projects or tools that are compatible with Linux and delve into the pros and cons of open-source AI and AI in general. Five Open-Source AI Projects for Linux Users TensorFlow The Google Brain team created TensorFlow. Its underlying software powers some of the technologies that Google uses today. It translates languages, improves search engine results, recognizes pictures in Google Photos, and understands spoken words, making its machine learning (ML) capabilities genuinely awe-inspiring. To the surprise of the tech community, Google open-sourced TensorFlow , making it available to everyone. Developers can createML models, classes for these models, and write imperative forward passes with it, among others. TensorFlow uses Python, C++, and CUDA. Microsoft Cognitive Toolkit Researchers at Microsoft Artificial Intelligence and Research initially developed the Microsoft Cognitive Toolkit, formerly known as “CNTK,” as an internal tool to speed up their research. It later served as an exhaustive toolkit for deep learning. It was first used by Liebherr to develop smart refrigerators and other appliances and powered Microsoft’s flagship products. Since becoming open source in 2016, the toolkit has been used by different organizations to perform a wide range of deep learning and ML activities. Microsoft Cognitive Toolkit uses C++ and Python. What sets it apart is its scalability. It can train and examine deep learning algorithms in a central processing unit (CPU), graphics processing unit (GPU), and other environments. Acumos AI Acumos AI is a product of the collaboration between TechMahindra and AT&T. It is an open-source AI platform that allows developers to build and deploy AI-powered applications. The platform also enables them to share AI-powered apps, fostering a community that does not hoard knowledge. The most significant contribution of Acumos AI to the market is that it allows for easy framework integration. Integration does not need to be performed by advanced programmers since the AI platform makes it uncomplicated for anyone. It supports several software languages such as Python, Java, and R. Apache SystemML IBM Almaden Research Center developed SystemML in 2010 to simplify the process of scaling ML algorithms written for small to big data. Before its development, data scientists who wrote ML algorithms using R or Python would rely on system programmers to convert the algorithms for big data using a different language. SystemML automatically scales an ML algorithm using a Python- or R-like language, effectively getting rid of the multi-iterative process, which tookweeks to complete. It wasn’t until June 2015 though that IBM open-sourced SystemML, and in 2017, it became an Apache Top-Level Project. OpenNN OpenNN is a neural network library written in C++. Data mining algorithms are present within its library, which can be embedded in other software to enable developers to perform predictive analysis. It’s important to emphasize that OpenNN is inherently a software library, and so doesn’t have a user interface (UI). The library, however, powers some predictive analytics tools such as Neural Designer, which allows users to model data through neural networks without needing to code programs. OpenNN’s development started in 2003 and was initially funded by the European Union (EU) under the research project named “Risk Assessment and Management of FLOODS (RAMFLOOD).” Artelnics, a tech company based in Spain, is currently developing the project. Open-Source AI: The Pros and Cons One factor that drives applications’ creators to release their work for free is the desire for AI to progress at a faster pace. By making their apps open-source, they can pool the knowledge of millions of experts togethe r, and development becomes a lot faster as a result of this global collaboration. Between the five open-source AI applications and libraries detailed ab ove, developers can program AI-powered software that could potentially change the world one industry at a time. AI in Agriculture Food security is a global issue, and with the increasing population, new methods of food production are much needed, and AI technology has been very helpful in this regard. Several countries around the world are benefiting from smart farming technologies that aid in livestock and crop monitoring, irrigation, weather forecasting, and overall farm management. AI in Marketing Big Data is a huge part of AI, and one of the industries that needs massive amounts of information is the marketing industry. AI has helped marketing professionals anticipateconsumer demand, discover new trends, and personalize products and services. All these capabilities help companies improve their bottom lines. AI in Healthcare The field of medicine is also increasingly making use of AI technology. For instance, AI systems are used to monitor a patient''s intake of prescribed medication. AI-powered health monitoring apps are also helping patients and doctors keep track of their heart rate and other vital statistics. AI in Cybersecurity Machine learning (ML), which is a component of AI, is changing the world of cybersecurity in terms of threat investigation and incident response. AI-powered cybersecurity tools can detect indicators of compromise (IoCs) such as malicious emails, URLs, IP addresses, and even unnatural network traffic. Open-source AI is also being explored in developing hardware , specifically microprocessors that are more secure. While advances in software have become a trend, hardware is lagging behind somewhat, making it easier for cybercriminals to gain access to microprocessors. But, with the help of AI, better and more secure chips can be developed. AI as a Cybercrime Weapon Along with the positive undeniable contributions of AI comes the other side of the coin, though—a new generation of cyberthreats backed by AI and smart technology. Blackhat hackers are, for instance, developing malware that uses AI to circumvent antivirus and antimalware detection tools. Cybercriminals use AI to hide malicious code in benign applications by training the malware to wait until the preset triggering action is performed. IBM Research demonstrated how DeepLocker can be used in cyber attacks. In the demonstration, DeepLocker can be trained to: Create an email that bypasses security filters Create a target profile Mutate to bypass antivirus or antimalware programs Perform cyber attacks at machine-like speed In the future, we may even see AI-powered malware trained to recognize a target’s face or voice.As a result, current cybersecurity tools and infrastructure may become obsolete. Aside from the possibility of cybercriminals launching AI-powered attacks, the very core of an AI machine is actually vulnerable to attacks. Threat actors, for example, can cause the deep neural networks of a system to cause it to make mistakes with the addition of subtle inputs. This vulnerability led IBM to develop an AI security software called Adversarial Robustness Toolbox (ART), which it also released as open-source software. Final Thoughts AI is all the rage in different industries, and rightly so. AI-powered tools and systems have the potential to change processes for the better—healthcare becomes more factual than intuitive, increases in revenue can be seen more clearly in marketing efforts, and food security becomes a reality rather than a dream. However, we should not discount the fact that AI can also be weaponized, empowering the wrong people. Cybersecurity systems must also be upgraded to counter AI-powered cyberattacks. And when developing AI-powered machines, it is critical to ensure that they are not vulnerable to attacks . About the Author Alexandre Francois is a serial entrepreneur and tech enthusiast who believes that knowledge about innovations and emerging technologies should be easily understandable and available to everyone. He is also the publishing director of Techslang — a tech awareness resource where cybersecurity and IT are explained in plain English. . The emergence of open-source AI has transformed the tech landscape for Linux users. Here are five key projects showcasing AI's industrial impact.. Open-Source AI, Machine Learning Tools, Linux Development, AI Projects, Software Collaboration. . Brittany Day

Calendar 2 Feb 10, 2020 User Avatar Brittany Day
102

Understanding Privacy Risks in Emerging Technologies and AI Surveillance

As technology evolves and the use of Artificial Intelligence and Machine Learning becomes increasingly mainstream, consumers are more concerned than ever before about protecting their privacy. Awareness surrounding how activities are being tracked and how personal information is being accessed and used is growing. The world’s biggest companies are frequently being challenged on the ways that they collect and utilize people’s data. . The growing concern surrounding privacy and data security encompasses technologies both online and offline. While consumers are fine with some forms of tracking, the bigger concern is how it could be used for surveillance. Here are three controversial emerging technologies with privacy-threatening implications that you may not have considered. Location-tracking technologies on mobile phones New technologies are capable of tracking and recording your every movement, revealing detailed information about your lifestyle and personal choices that you make. For example, your mobile phone registers its location with cell towers every few minutes whenever it is turned on. Mobile carriers collect this data on their customers, and government officials can easily obtain detailed information about you by accessing your location. The federal government invokes powerful surveillance authorities to collect sensitive data including location, contact lists, call records and contents of text messages and calls. Facial recognition technologies Facial recognition technologies analyze images of human faces for the purpose of identifying them. These technologies are often used for general surveillance, and passively collect images without people’s knowledge or consent. State motor vehicle agencies possess high-quality photographs of most citizens, which can be used for facial recognition programs that can serve identification and tracking purposes. People are becoming aware of the privacy implications associated with facial recognition technology and they are takinga stand. In May, San Francisco outlawed the use of facial recognition technology by city agencies, and other cities are considering regulating facial recognition technology as it continues to become increasingly controversial. Just recently, the state of California banned the use of facial recognition in police body cams. This new legislation ensures that body cameras, which were promoted as a tool for officer accountability, cannot be twisted into surveillance systems used to target and oppress marginalized populations. California’s law is impressively more preemptive than reactive, as no law enforcement officers in California were using body cameras with facial recognition software prior to this new rule. Despite the growing awareness of the dangers of facial recognition, technologies and programs that utilize it are still prevalent. France is currently in the process of creating a nationwide program to create legal digital identities for its citizens using facial recognition. And France is not alone. According to a report released by the Carnegie Endowment for International Peace, at least 75 out of 176 countries worldwide are actively using AI technologies for surveillance purposes. Automatic license plate readers Automatic license plate readers (ALPRs) are an emerging surveillance technology designed to track the movements of every passing driver and record traffic accidents. These readers are often mounted on police cars or objects like road signs, bridges, or traffic lights. They use discrete high-speed cameras to indiscriminately photograph not only license plates, but pedestrians, bicyclists, workers, residents and animals. The video footage collected by these readers is often pooled into regional sharing systems and, as a result, enormous databases of innocent people’s personal information are expanding rapidly. This data can be kept indefinitely with little or no privacy restrictions, and can be sold to and used by anyone who is willing to pay for it.Because these surveillance cameras are small and usually well-hidden, they often go unnoticed, making them especially invasive and threatening. To make matters worse, this technology is often abused. Data that the Electronic Frontier Foundation obtained from the Oakland Police Department shows that police disproportionately deploy ALPR-mounted vehicles in low-income communities and communities of color. In addition to the deliberate abuse of this software, ALPRs are not fool-proof and sometimes misread plates, leading serious consequences. In 2009, San Francisco police pulled over Denise Green, an African-American city worker, handcuffed her at gunpoint and searched both her and her car - all because her vehicle was misidentified as stolen due to a license plate reader error. This horrific incident led to the U.S. Ninth Circuit Court of Appeals ruling that technology alone can’t be the basis of such a stop. However, this legislation unfortunately does not apply everywhere, leaving people vulnerable to tragic law enforcement errors. How can I protect my privacy? People should not be forced to choose between technology and privacy. While some aspects of privacy are unfortunately out of individuals’ control, there are various practices and behaviors that people should engage in which will help protect citizens’ privacy both online and offline. They include: Choose strong, unique passwords for each of your accounts NEVER share passwords Set automatic locks on all devices Avoid connecting to unsecured WiFi networks Download apps from trusted sources Limit personal information given to apps and websites Manage what is shared online Practice robust data security and minimal data collection Encourage education and awareness By taking measures to protect aspects of our privacy that are within our control and by challenging privacy-threatening programs and initiatives, we can collectively work toward creating a more secure future! Have a questionor a comment about privacy? Please share it with us. We are passionate about this topic and would love to discuss it with you. . Delve into the confluence of cutting-edge tech and confidentiality as we analyze the implications of artificial intelligence, monitoring, and observation.. Emerging Technology, Data Protection, Privacy Awareness, AI Surveillance. . Brittany Day

Calendar 2 Oct 31, 2019 User Avatar Brittany Day
102

Guardian Digital's EnGarde Email Security Gateway Redefines Resilience

Resiliency is an important factor to consider when evaluating an email security solution, yet this characteristic often goes overlooked.. When choosing an email security solution, people often consider the technology it utilizes and how this technology is implemented to provide protection against current email-related threats. These are critical aspects to take into account, given that organizations are dependent on email for communications and email is currently the most popular attack vector for cybercriminals. An attribute that often remains unconsidered is resiliency or, in other words, how reliable and secure a solution is over time. Resiliency is a quality that is critical in determining if a solution can provide effective, uninterrupted protection against the myriad of dangerous attacks that constitute today’s email threat landscape. A significant factor in determining the resiliency of a solution is the software it is comprised of and the operating system it runs on. For numerous reasons, Linux and open source software have a greater potential to achieve a very high level of security than alternative operating systems and proprietary software do. The open source development model offers inherent security advantages, making open source products and technologies secure by design. Because of the collaboration and transparency involved in the development of open source software, vulnerabilities and potential security issues are rapidly detected and fixed. As a result of this collaborative development and review process, Linux is inherently more secure than proprietary OS alternatives like MacOS or Microsoft Windows. The various security advantages associated with open source technology make email security solutions that run on Linux and utilize open source software both highly secure and highly resilient. Key Features of Guardian Digital's Open-Source EnGarde Email Security Gateway: 1. Highly effective protection against sophisticated new and existing email-related threats includingphishing, business email compromise, malware, email spoofing and spam email 2. Rapid and highly accurate identification and quarantine of malicious email 3. Advanced machine learning and heuristics technology, Big Data techniques and broad file type analysis 4. Customizable to fit businesses’ specific demands 5. Exceptional customer support 6. Comprised of entirely open-source software and operated on an inherently secure hardened version of Linux 7. Cloud-based and highly convenient “For over a decade we’ve counted on Guardian Digital’s solutions for a more secure and reliable mail environment. I’m grateful for their years of great service — the staff is professional and prompt and has always had a quick resolution to our mail inquiries or any other issues.” -Sheldon Huey, Best Western International, Senior Operations Manager, Enterprise Email Guardian Digital just recently decommissioned two mail systems that were running optimally for more than four years without being rebooted or adjusted beyond standard maintenance. These systems reliably routed hundreds of millions of emails over their lifetime. They offered a myriad of businesses highly effective protection against serious and evolving email-related threats including spear-phishing, business email compromise and ransomware. All of Guardian Digital’s mail systems run on a hardened version of Linux specifically developed to optimize the systems’ security and reliability over time. A combination of the inherent security of Linux, the expertise of Guardian Digital engineers, and an strong emphasis on quality and customer service results in systems that will reliably route mail virtually indefinitely. In the long run, the performance of these systems is truly unrivaled. . When choosing an email security solution, people often consider the technology it utilizes and how t. resiliency, important, factor, consider, evaluating, email, security, solution. . Brittany Day

Calendar 2 Dec 26, 2018 User Avatar Brittany Day
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