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Where AIOps Is Delivering Results Today

Where AIOps Is Delivering Results Today

Bottom Line: Capitalizing on AI and machine learning’s inherent strengths to create contextual intelligence in real-time, LogicMonitor’s early warning and failure prevention systems reflect where AIOps is delivering results today.

LogicMonitor’s track record of making solid contributions to their customers’ ability to bring greater accuracy, insight, and precision into monitoring all IT assets is emerging as a de facto industry standard. Recently I was speaking with a startup offering Hosted Managed Services of a variety of manufacturing applications, and the must-have in their services strategy is LogicMonitor LM Intelligence. LogicMonitor’s AIOps platform is powered by LM Intelligence, enabling customers’ businesses to gain early warning into potential trouble spots in IT operations stability and reliability. LogicMonitor does the hard work for you with automated alert thresholds, AI-powered early warning capabilities, customizable escalation chains, workflows, and more.

Engineers who are working at the Hosted Managed Services provider I recently spoke with say LM Intelligence is the best use case of AI and machine learning to provide real-time alerts, contextual insights, discover new patterns in data, and make automation achievable. The following is an example of the LM Intelligence dashboard:

Where AIOps Is Delivering Results Today

How LogicMonitor’s Architecture Supports AIOps

One of the core strengths LogicMonitor continues to build on is integration, which they see as essential to their ability to excel at providing AIOps support for their customers. Their architecture is shown below. By providing real-time integration to public cloud platforms, combined with control over the entire IT infrastructure structure along with over 2,000 integrations from network to cloud, LogicMonitor excels at unifying diverse IT environments into a single, cohesive AIOps-based intelligence system.  The LogicMonitor platform collects cloud data through our cloud collectors. These collectors retrieve metrics such as the cloud provider health and billing information by making API calls to the cloud services. The collector is a Windows Service or background process that is installed in a virtual machine. This collector then pulls metrics from the different devices using a variety of different methods, including SNMP, WMI, perf Mon JMX, APIs, and scripts.

Where AIOps Is Delivering Results Today

Using AIOps To Monitor, Analyze, Automate

LogicMonitor has created an architecture that’s well-suited to support the three dominant dimensions of AIOps, including Monitoring, Analytics (AIOps), and Automating. Their product and services strategies in the past have reflected a strong focus on Monitoring. The logic of prioritizing Monitoring as a product strategy area was to provide the AI and machine learning models with enough data to train on so they could identify anomalies in data patterns faster. Their 2018/2019 major releases in the Monitor area reflect how the unique strength they have of capturing and making use of any IT asset that can deliver a signal is paying off. Key Monitor developers recently include the following:

  • Kubernetes Monitoring
  • Service Insight
  • Topology
  • Remote Sessions
  • Netflow
  • Configuration Monitoring
  • Public Cloud Monitoring
  • Applications Monitoring

LogicMonitor’s core strengths in AIOps are in the Anomaly Detection and Early Warning System areas of their product strategy. Their rapid advances in the Early Warning System development show where AIOps is delivering solid results today. Supporting the Early Warning System, there are Dynamic Thresholds and Root Cause Analysis based on Dependencies as well.

The Automate area of their product strategy shows strong potential for future growth, with the ServiceNow integration having upside potential. Today Alert Chaining and Workflow support integrations to Ansible, Terraform, Slack, Microsoft, Teama, Putter, Terraform, OpsGenie, and others.

Conclusion

LogicMonitor’s platform handles 300B metrics on any given day and up to 10B a month, with over 28K collectors deployed integrated with approximately 1.4M devices being monitored. Putting AI and machine learning to work, interpreting the massive amount of data the platform captures every day to fine-tune their Early Warning and Failure Prevention Systems, is one of the most innovative approaches to AIOps today. Their AIOps Early Warning System is using machine learning Algorithms to fine-tune Root Cause Analysis and Dynamic Thresholds continually. AIOps Log Intelligence is also accessing the data to complete Automatic Log Anomaly Detection, Infrastructure change detection, and Log Volume Reduction to Signal analysis.

 

 

 

5 Ways Machine Learning Can Thwart Phishing Attacks

5 Ways Machine Learning Can Thwart Phishing Attacks

Mobile devices are popular with hackers because they’re designed for quick responses based on minimal contextual information. Verizon’s 2020 Data Breach Investigations Report (DBIR) found that hackers are succeeding with integrated email, SMS and link-based attacks across social media aimed at stealing passwords and privileged access credentials. And with a growing number of breaches originating on mobile devices according to Verizon’s Mobile Security Index 2020, combined with 83% of all social media visits in the United States are on mobile devices according to Merkle’s Digital Marketing Report Q4 2019, applying machine learning to harden mobile threat defense deserves to be on any CISOs’ priority list today.

How Machine Learning Is Helping To Thwart Phishing Attacks

Google’s use of machine learning to thwart the skyrocketing number of phishing attacks occurring during the Covid-19 pandemic provides insights into the scale of these threats. On a typical day, G-Mail blocks 100 million phishing emails. During a typical week in April of this year, Google’s G-Mail Security team saw 18M daily malware and phishing emails related to Covid-19. Google’s machine learning models are evolving to understand and filter phishing threats, successfully blocking more than 99.9% of spam, phishing and malware from reaching G-Mail users. Microsoft thwarts billions of phishing attempts a year on Office365 alone by relying on heuristics, detonation and machine learning strengthened by Microsoft Threat Protection Services.

42% of the U.S. labor force is now working from home, according to a recent study by the Stanford Institute for Economic Policy Research (SIEPR). The majority of those working from home are in professional, technical and managerial roles who rely on multiple mobile devices to get their work done. The proliferating number of threat surfaces all businesses have to contend with today is the perfect use case for thwarting phishing attempts at scale.

What’s needed is a machine learning engine capable of analyzing and interpreting system data in real-time to identify malicious behavior. Using supervised machine learning algorithms that factor in device detection, location, user behavior patterns and more to anticipate and thwart phishing attacks is what’s needed today. It’s a given that any machine learning engine and its supporting platform needs to be cloud-based, capable of scaling to analyze millions of data points. Building the cloud platform on high-performing computing clusters is a must-have, as is the ability to iterative machine learning models on the fly, in milliseconds, to keep learning new patterns of potential phishing breaches. The resulting architecture would be able to learn over time and reside on the device recursively. Protecting every endpoint if it’s connected to WiFi or a network or not is a key design goal that needs to be accomplished as well. MobileIron recently launched one of the most forward-thinking approaches to solving this challenge and its architecture is shown below:

5 Ways Machine Learning Can Thwart Phishing Attacks

Five Ways Machine Learning Can Thwart Phishing Attacks 

The one point of failure machine learning-based anti-phishing apps continue to have is lack of adoption. CIOs and CISOs I’ve spoken with know there is a gap between endpoints secured and the total endpoint population. No one knows for sure how big that gap is because new mobile endpoints get added daily. The best solution to closing the gap is by enabling on-device machine learning protection. The following are five ways machine learning can thwart phishing attacks using an on-device approach:

1.    Have machine learning algorithms resident on every mobile device to detect threats in real-time even when a device is offline.  Creating mobile apps that include supervised machine learning algorithms that can assess a potential phishing risk in less than a second is what’s needed. Angular, Python, Java, native JavaScript and C++ are efficient programming languages to provide detection and remediation, so ongoing visibility into any malicious threat across all Android and iOS mobile devices can be tracked, providing detailed analyses of phishing patterns. The following is an example of how this could be accomplished:

5 Ways Machine Learning Can Thwart Phishing Attacks

2.    Using machine learning to glean new insights out of the massive amount of data and organizations’ entire population of mobile devices creates a must-have.  There are machine learning-based systems capable of scanning across an enterprise of connected endpoints today. What’s needed is an enterprise-level approach to seeing all devices, even those disconnected from the network.

3.    Machine learning algorithms can help strengthen the security on every mobile device, making them suitable as employees’ IDs, alleviating the need for easily-hackable passwords. According to Verizon, stolen passwords cause 81% of data breaches and 86% of security leaders would do away with passwords, if they could, according to a recent IDG Research survey. Hardening endpoint security to the mobile device level needs to be part of any organizations’ Zero Trust Security initiative today. The good news is machine learning algorithms can thwart hacking attempts that get in the way making mobile devise employees’ IDs, streamlining system access to the resources they need to get work done while staying secure.

4.    Keeping enterprise-wide cybersecurity efforts focused takes more than after-the-fact analytics and metrics; what’s needed is look-ahead predictive modeling based machine learning data captured at the device endpoint.  The future of endpoint resiliency and cybersecurity needs to start at the device level. Capturing data at the device level in real-time and using it to train algorithms, combined with phishing URL lookup, and Zero Sign-On (ZSO) and a designed-in Zero Trust approach to security are essential for thwarting the increasingly sophisticated breach attempts happening today.

5.    Cybersecurity strategies and the CISOs leading them will increasingly be evaluated on how well they anticipate and excel at compliance and threat deterrence, making machine learning indispensable to accomplishing these tasks. CISOs and their teams say compliance is another area of unknowns they need greater predictive, quantified insights into. No one wants to do a compliance or security audit manually today as the lack of staff due to stay-at-home orders makes it nearly impossible and no one wants to jeopardize employee’s health to get it done.  CISOs and teams of security architects also need to put as many impediments in front of threat actors as possible to deter them, because the threat actor only has to be successful one time, while the CISO/security architect have to be correct 100% of the time. The answer is to combine real-time endpoint monitoring and machine learning to thwart threat actors while achieving greater compliance.

Conclusion

For machine learning to reach its full potential at blocking phishing attempts today and more advanced threats tomorrow, every device needs to have the ability to know if an email, text or SMS message, instant message, or social media post is a phishing attempt or not. Achieving this at the device level is possible today, as MobileIron’s recently announced cloud-based Mobile Threat Defense architecture illustrates. What’s needed is a further build-out of machine learning-based platforms that can adapt fast to new threats while protecting devices that are sporadically connected to a company’s network.

Machine learning has long been able to provide threat assessment scores as well. What’s needed today is greater insights into how risk scores relate to compliance. Also, there needs to be a greater focus on how machine learning, risk scores, IT infrastructure and the always-growing base of mobile devices can be audited. A key goal that needs to be achieved is having compliance actions and threat notifications performed on the device to shorten the “kill chain” and improve data loss prevention.

Answers To Today’s Toughest Endpoint Security Questions In The Enterprise

Answers To Today's Toughest Endpoint Security Questions In The Enterprise

  • Enterprises who are increasing the average number of endpoint security agents from 9.8 last year to 10.2 today aren’t achieving the endpoint resilience they need because more software agents create more conflicts, leaving each endpoint exposed to a potential breach.
  • 1 in 3 enterprise devices is being used with a non-compliant VPN, further increasing the risk of a breach.
  • 60% of breaches can be linked to a vulnerability where a patch was available, but not applied. Windows 10 devices in enterprises are, on average, 95 days behind on patches.

CIOs, CISOs and cybersecurity teams say autonomous endpoint security is the most challenging area they need to strengthen in their cybersecurity strategy today. Software agents degrade faster than expected and conflict with each other, leaving endpoints exposed. Absolute’s 2020 State of Endpoint Resilience Report quantifies the current state of autonomous endpoint security, the scope of challenges CISOs face today and how elusive endpoint resiliency is to achieve with software agents. It’s an insightful read if you’re interested in autonomous endpoint security.

Endpoint Security Leads CISOs’ Priorities In 2020

With their entire companies working remotely, CIOs and CISOs I’ve spoken with say autonomous endpoint security is now among their top three priorities today. Cutting through the endpoint software clutter and turning autonomous endpoint security into a strength is the goal. CISOs are getting frustrated with spending millions of dollars among themselves only to find out their endpoints are unprotected due to software conflicts and degradation.  Interested in learning more, I spoke with Steven Spadaccini, Vice President, Sales Engineering at Absolute Software and one of the most knowledgeable autonomous endpoint cybersecurity experts I’ve ever met. Our conversation delved into numerous cybersecurity challenges enterprise CIOs and CISOs are facing today. My interview with him is below:

The Seven Toughest Questions the C-Suite Is Asking About Endpoint Security

Louis: Thank you for your time today. I have seven questions from CIOs, CISOs and their teams regarding endpoint security. Let’s get started with their first one. What happens if an endpoint is compromised, how do you recover, encrypt, or delete its data?

Steven:  It’s a challenge using software agents, both security and/or management, to do this as each agents’ tools and features often conflict with each other, making a comprised endpoints’ condition worse while making it virtually impossible to recover, encrypt, delete and replace data. The most proven approach working for enterprises today is to pursue an endpoint resilience strategy. At the center of this strategy is creating a root of trust in the hardware and re-establishes communication and control of a device through an unbreakable digital tether. I’m defining Endpoint Resilience as an autonomous endpoint security strategy that ensures connectivity, visibility and control are achieved and maintained no matter what is happening at the OS or application level. Taking this approach empowers devices to recover automatically from any state to a secure operational state without user intervention. Trust is at the center of every endpoint discussion today as CIOs, CISOs and their teams want the assurance every endpoint will be able to heal itself and keep functioning

Louis: Do endpoint software security solutions fail when you lose access to the endpoint, or is the device still protected at the local level?

Steven: When they’re only protected by software agents, they fail all the time. What’s important for CISOs to think about today is how they can lead their organizations to excel at automated endpoint hygiene. It’s about achieving a stronger endpoint security posture in the face of growing threats. Losing access to an endpoint doesn’t have to end badly; you can still have options to protect every device. It’s time for enterprises to start taking a more resilient-driven mindset and strategy to protecting every endpoint – focus on eliminating dark endpoints. One of the most proven ways to do that is to have endpoint security embedded to the BIOS level every day. That way, each device is still protected to the local level. Using geolocation, it’s possible to “see” a device when it comes online and promptly brick it if it’s been lost or stolen.

Louis: How can our cybersecurity team ensure compliance that all cybersecurity software is active and running on all endpoints?

Steven: Compliance is an area where having an undeletable tether pays off in a big way. Knowing what’s going on from a software configuration and endpoint security agent standpoint – basically the entire software build of a given endpoint – is the most proven way I’ve seen CISOs keep their inventory of devices in compliance. What CISOs and their teams need is the ability to see endpoints in near real-time and predict which ones are most likely to fail at compliance. Using a cloud-based or SaaS console to track compliance down to the BIOS level removes all uncertainty of compliance. Enterprises doing this today stay in compliance with HIPAA, GDPR, PCI, SOX and other compliance requirements at scale. It’s important also to consider how security automation and orchestration kicks on to instantly resolve violations by revising security controls and configurations, restoring anti-malware, or even freezing the device or isolating it from data access. Persistent visibility and control give organizations what they need to be audit-ready at every moment.

Having that level of visibility makes it easy to brick a device. Cybersecurity teams using Absolute’s Persistence platform can lead to humorous results for IT teams, who call the bricking option a “fun button as they watch hackers continually try to reload new images and right after they’re done, re-brick the device again. One CIO told the story of how their laptops had been given to a service provider who was supposed to destroy them to stay in compliance with the Health Insurance Portability and Accountability Act (HIPAA) and one had been resold on the black market, ending up in a 3rd world nation. As the hacker attempted to rebuild the machine, the security team watched as each new image was loaded at which time they would promptly brick the machine. After 19 tries, the hacker gave up and called the image rebuild “brick me.”

Louis: With everyone working remote today, how can we know, with confidence where a given endpoint device is at a moments’ notice?

Steven: That’s another use case where having an undeletable tether pays off in two powerful ways: enabling autonomous endpoint security and real-time asset management. You can know with 100% confidence where a given endpoint device is in real-time so long as the device is connected to a permanent digital tether . Even if the device isn’t reachable by your own corporate network it’s possible to locate it using the technologies and techniques mentioned earlier. CIOs sleep better at night knowing every device is accounted for and if one gets lost or stolen, their teams can brick it in seconds.

Louis: How can our IT and cybersecurity teams know all cybersecurity applications are active and protecting the endpoint?

Steven: By taking a more aggressive approach to endpoint hygiene, it’s possible to know every application, system configuration and attributes of user data on the device. It’s important not to grow complacent and assume the gold image IT uses to configure every new or recycled laptop is accurate. One CIO was adamant they had nine software agents on every endpoint, but Absolute’s Resilience platform found 16, saving the enterprise from potential security gaps. The gold image is an enterprise IT team was using had inadvertently captured only a subset of the total number of software endpoints active on their networks. Absolute’s Resilience offering and Persistence technology enabled the CIO to discover gaps in endpoint security the team didn’t know existed before.

Louis: How can we restrict the geolocations of every endpoint?

Steven: This is an area that’s innovating quickly in response to the needs enterprises have to track and manage assets across countries and regions. IP tracking alone isn’t as effective as the newer techniques, including GPS tracking, Wi-Fi triangulation, with both integrated into the Google Maps API. Enterprises whose business relies on Personal Identifiable Information (PII) is especially interested in and adopting these technologies today. Apria Healthcare is currently using geofencing for endpoint security and asset management. They have laptops in use today across Indonesia, the Philippines and India. Given the confidential nature of the data on those devices and compliance with local government data protection laws, each laptop needs to stay in the country they’re assigned to. Geofencing gives Apria the power to freeze any device that gets outside of its region within seconds, averting costly fines and potential breaches.

Louis: How can our IT team immediately validate an endpoint for vulnerabilities in software and hardware?

Steven: The quickest way is to design in audit-ready compliance as a core part of any endpoint resilience initiative. Endpoint resilience to the BIOS level makes it possible to audit devices and find vulnerabilities in real-time, enabling self-healing of mission-critical security applications regardless of complexity. The goal of immediately validating endpoints for current security posture needs to be a core part of any automated endpoint hygiene strategy. It’s possible to do this across platforms while being OS-agnostic yet still accessible to over 500M endpoint devices, deployed across Microsoft Windows, macOS via a Mac Agent and Chrome platforms.

Conclusion

Knowing if their autonomous endpoint security and enterprise-wide cybersecurity strategies are working or not is what keeps CIOs up the most at night. One CISO confided to me that 70% of the attempted breaches to his organization are happening in areas he and his team already knew were vulnerable to attack. Bad actors are getting very good at finding the weakest links of an enterprises’ cyber defenses fast. They’re able to look at the configuration of endpoints, see which software agents are installed, research known conflicts and exploit them to gain access to corporate networks. All this is happening 24/7 to enterprises today. Needing greater resilient, persistent connections to every device, CISOs are looking at how they can achieve greater resilience on every endpoint. Capitalizing on an undeletable tether to track the location of the device, ensure the device and the apps on that device have self-healing capabilities and gain valuable asset management data  – these are a few of the many benefits they’re after.

10 Ways AI Is Accelerating DevOps

10 Ways AI is Accelerating DevOps

Looking to reduce the delays DevOps teams are challenged with, software development tool providers are accelerating the pace of integrating AI- and Machine Learning technologies into their apps and platforms. Accelerating every phase of the Software Development Lifecycle (SDLC) while increasing software quality is the goal. And the good news is use cases are showing those goals are being accomplished, taking DevOps to a new level of accuracy, quality, and reliability.

What’s particularly fascinating about the ten ways AI is accelerating DevOps is how effective it is proving to be in assisting developers with the difficult, time-consuming tasks that take away from coding. One of the most time-consuming tasks is managing the many iterations and versions of requirements documents. A leader in using AI to streamline every phase of the SDLC and assist with managing requirements is Jira Software from Atlassian, widely considered the industry standard in this area of DevOps.

The following are ten ways AI is accelerating DevOps today:

  1. Improving DevOps productivity by relying on AL and ML to autosuggest code segments or snippets in real-time to accelerate development. DevOps teams interviewed for this article from several leading enterprise software companies competing in CRM, Supply Chain Management, and social media markets say this use case of AI is the most productive and has generated the greatest gains in accuracy. Initial efforts at using AI to autocomplete code were hit or miss, according to a DevOps lead at a leading CRM provider. She credits DevOps’ development tools providers’ use of supervised machine learning algorithms with improving how quickly models learn and respond to code requests. Reflecting what the DevOps teams interviewed for this article prioritized as the most valuable AI development in DevOps, Microsoft’s Visual Studio Intellicode has over 6 million installs as of today.
  2. Streamlining Requirements Management using AI is proving effective at improving the accuracy and quality of requirements documents capturing what users need in the next generation of an app or platform.  AI is delivering solid results streamlining every phase of creating, editing, validating, testing, and managing requirements documents. DevOps team members are using AI- and ML-based requirements management platforms to save time so they can get back to coding and creating software products often on tight deadlines. Getting requirements right the first time helps keep an entire project on the critical path of its project plan. Seeing an opportunity to build a business case of keeping projects on schedule, AI-powered software development tools providers are quickly developing and launching new apps in their area. It’s fascinating to watch how quickly Natural Language Processing techniques are being adopted into this area of DevOps tools. Enterprises using AI-based tools have been able to reduce requirements review times by over 50%.
  3. AI is proving effective at bug detection and auto-suggestions for improving code. At Facebook, a bug detection tool predicts defects and suggests remedies that are proving correct 80% of the time with AI tools learning to fix bugs automatically. Semmle CodeQL is considered the leading AI-based DevOps tool in this area. DevOps teams using CodeQL can track down vulnerabilities in code and also find logical variants in their entire codebase. Microsoft uses Semmle for vulnerability hunting. Security researchers in Microsoft’s security response team use Semmle QL to find variants of critical problems, allowing them to identify and respond to serious code problems and prevent incidents.
  4. AI is assisting in prioritizing security testing results and triaging vulnerabilities.  Interested in learning more about how ML can find code vulnerability in real-time, I spoke with Maty Siman, CTO Checkmarx, says that “even organizations with the most mature SDLCs often run into issues with prioritizing and triaging vulnerabilities. ML algorithms that focus on developers’ or AppSec teams’ attention on true positives and vulnerable components that pose a threat are key to navigating this challenge.” Maty also says that ML algorithms can be taught to understand that one type of vulnerability vs. another has a higher percentage of being a true positive. With this automated “vetting” process in place, teams can optimize and accelerate their remediation efforts in a much more informed manner.
  5. Improving software quality assurance by auto-generating and auto-running test cases based on the unique attributes of a given code base is another area where AI is saving DevOps teams valuable time. This is invaluable for stress-testing new apps and platforms across a wide variety of use cases. Creating and revising test cases is a unique skill set on any DevOps team, with the developers with this skill often being overwhelmed with test updates. AI-based software development tools are eliminating test coverage overlaps, optimizing existing testing efforts with more predictable testing, and accelerating progress from defect detection to defect prevention. AI-based software development platforms can identify the dependencies across complex and interconnected product modules, improving overall product quality in the process. Improving software quality enhances customer experiences, as well.
  6. AI is proving adept at troubleshooting defects in complex software apps and platforms after they’ve been released and shipped to customers. Enterprise software companies go to great lengths in their software QA processes to eliminate bugs, logic errors, and unreliable segments of code. Retrofitting releases or, worst case, recalling them is costly and impacts customers’ productivity. AI-based QA tools are proving effective at predicting which areas of an enterprise application will fail before being delivered into complex customer environments. AI is proving effective at root cause analysis, and also has proved effective in accelerating a leading CRM providers’ application delivery and a 72% reduction in time-to-restore in customers’ enterprise environments. Another DevOps team says they are using AI to auto-configure their applications’ settings to optimize performance in customer deployments.
  7. ML-based code vulnerability detection can spot anomalies reliably and alert DevOps teams in real-time. Maty Siman, CTO Checkmarx told me that, “assuming that your developers are writing quality, secure code, machine learning can set a baseline of “normal activity” and identify and flag anomalies from that baseline.” He continued, saying that “ultimately, we live in an IT and security landscape that’s evolving every minute of every day, requiring systems and tools that learn and adapt at the same, if not a greater, speed. Organizations and developers can’t do it alone and require solutions that improve the accuracy of threat detection to help them prioritize what matters most.” Spotting anomalies quickly and taking action on them is integral to building a business case for AI software-based QA and DevOps tools.
  8. Advanced DevOps teams are using AI to analyze and find new insights across all development tools, Application Performance Monitoring (APM), Software QA, and release cycle systems. DevOps teams at a leading Supply Chain Management (SCM) enterprise software provider are using AI to analyze why certain projects go so well and deliver excellent code while others get caught in perpetual review and code rewrite cycles. Using supervised machine learning algorithms, they’re able to see patterns and gain insights into their data. Becoming data-driven is quickly becoming part of their DNA, a DevOps lead told me this week on a call.
  9. Improving traceability within each release cycle to find where gaps in DevOps collaboration and data integration workflows can be improved.  AI is enabling DevOps teams to stay more coordinated with each other, especially across remote geographic locations. AI-driven insights are helping to see how shared requirements and specifications can reflect localization, unique customer requirements, and specific performance benchmarks.
  10. Creating a more integrated DevOps strategy where AI can deliver the most value depends on frameworks that can keep DevOps customer-centric while improving agility and nurturing an analytics-driven DNA to gain insights into operations. DevOps leaders interviewed for this article say integrating security into development cycles reduces bottlenecks that get in the way of staying on schedule. Several went on to say that frameworks capable of integrating Quality Assurance into the DevOps workflows are key. AI’s use cases taken together reflect the potential to revolutionize DevOps. Executing on this promise, however, requires a framework that empowers enterprise DevOps teams to deliver a transcendent customer experience, automate customer transactions, and provide support for automation everywhere. One of the leaders in this area is BMC’s Autonomous Digital Enterprise framework, which helps businesses harness AI/ML capabilities to run and reinvent in a rapidly transforming world. It’s helping enterprises innovate faster than their competitors by enabling the agility, customer centricity, and actionable insights integral to driving data-driven business outcomes.

Conclusion

Accelerating development cycles while ensuring the highest quality code gets produced is a challenge all DevOps teams face. AI is helping to accelerate every phase of DevOps development cycles by anticipating what developers need before they ask for it. Auto suggesting code segments, improving software quality assurance techniques with automated testing, and streamlining requirements management are core areas where AI is delivering value to DevOps today.

Improving Online Learning Experiences One Secured Endpoint At A Time

Improving Online Learning Experiences One Secured Endpoint At A Time

Bottom Line: Defining the perfect mix of cloud apps, platforms and secured endpoints to create compelling online learning experiences customizable to students’ learning strengths is how schools are overcoming the challenge of virtual teaching.

There are over 56 million students in the U.S. alone who are relying on remote learning apps, platforms and autonomous endpoint security to protect them as they pursue their education. School districts, online educators and teachers quickly realized the move to 100% online classes could mean the end to outdated mechanized approaches to teaching. Eager to teach using technologies that tailor individual learning programs to every student’s unique learning strengths, schools are combining cloud, e-learning and endpoint security with strong results. Combining technologies gives every student regardless of their socioeconomic background a chance to excel. The goal is to provide unique personalized instruction at scale using a teaching technique called scaffolding. Scaffolding stresses creating an individual learning plan for each student complete with reinforcement for each lesson.

Why Cybersecurity Is The Cornerstone Of Online Learning 

Tailoring the latest technologies to the diverse needs of online learners is the easy part of creating an online learning program. Far more difficult is choosing the right endpoint security strategies to protect their identities, every one of their video conference sessions with peers and teachers and thwarting breach attempts. Parents, teachers, students and administrators all need to trust an e-learning platform to make it work. The bottom line is an e-learning platform needs to create and grow trust while being adaptive enough to meet students’ unique learning needs.

Interested in learning more about how leading online educators are bringing together the latest cloud and autonomous endpoint security technologies to help students learn online, I recently interviewed Eric Ramos Chief Technology Officer at Duarte Unified School District and Dean Phillips, Senior Technology Director, David Atkins, Director of Marketing and Communications and Jennifer Shoaf, Deputy Chief Academic Officer at PA Cyber.  Duarte Unified School District (USD) serves the educational needs of 3,400 scholars at the elementary, K-8 and high school levels. The Pennsylvania Cyber Charter School (PA Cyber) in Midland, PA, is one of the most experienced and successful online K-12 public schools in the nation serving over 12,000 students. Together the group of education professionals provided valuable insights into how educators can combine cloud, collaboration and cybersecurity applications to create more personalized, effective learning experiences for students. David Atkins of PA Cyber says that their approach to e-learning is succeeding because they take a fully holistic view of the student, their family and their situation. “Our collaboration with the student starts from the very moment that there’s interest in having some sort of cyber education. And we go from enrollments, all the way through any issues of that students could have, or the students family could have and take them all the way through graduation’ David said. “We take the time to listen and see the student as a complete person.”

The following are the key insights based on our conversations:

  • Choosing to make cybersecurity the highest priority treats students as customers, protecting their unique online learning experiences while providing excellent access across all socioeconomic levels. That’s when online learning experiences excel. What’s impressive how committed the team of educators I spoke with is about making technology work as a catalyst to help every student achieve their educational goals across all socioeconomic levels. They’re also the most advanced at tailoring complex technologies to deliver customized online learning experiences with PA Cyber serving 12,000 remote students at once. “Each of our students is different and they’re looking to accomplish different things and they learn in different ways. We have a different classroom options that they can choose from. And we have a lot of different scaffolding options in place when it comes to our instructional platform, “Jennifer Shoaf, Deputy Chief Academic Officer at PA Cyber said. Eric Ramos, CTO at USD says that he and his staff “reach out to teachers and staff members and provide them with the latest cybersecurity alerts and make sure they are aware of how their autonomous endpoint security platform is securing every laptop and making their job of staying in compliance to security protocols easy.” Eric continued saying that, “having an undeletable digital tether gives my staff, senior educators and me peace of mind, especially with summer here and the need to keep track of the Chromebooks out with students and families.”
  • The more resilient the autonomous endpoint security on the laptop, the easier it is to secure, upgrade and locate each of them if they’re lost or stolen. Duarte Unified School District provides Chromebooks to students for use all year long, often also providing an Internet HotSpot as many students’ families don’t have Internet access. PA Cyber provides students a Dell laptop and an entire technology kit that includes printers and peripherals as well. Having an undeletable digital tether to every laptop makes it possible to keep every system up to date on security and system patches. Dean Phillips, Senior Technology Director at PA Cyber, says that it’s been very helpful to know each laptop has active autonomous endpoint security running at all times. Dean says that endpoint management is a must-have for PA Cyber “We’re using Absolute’s Persistence to ensure an always-on, two-way connection with our IT management solution, Kaseya®, which we use to remotely push out security patches, new applications and scripts. That’s been great for students’ laptops as we can keep updates current and know where the system is. Without an endpoint management solution on student laptops, it is very difficult to manage endpoints without that agent. So Absolute absolutely helps us with that as well. That’s been a big plus.” Eric Ramos, CTO, says that Absolute has been great, especially when student calls in and says they can’t find their laptop. I don’t know where it is. It’s lost or maybe stolen. We’re able to pull that up, figure out the last time it got pinged and we can locate that usually. Nine times out of 10, the student finds it by next day by just having that information. So that’s been crucial. It’s always been something we love having.”
  • Standardize on a secure cloud platform that is flexible enough to support scaffolding or individualized learning yet hardened enough to protect every laptop connected to it via an undeletable digital tether. A major challenge both online schools face is keeping their cloud platforms adaptive enough to support students’ varying skills yet also secure enough to protect every student online.  Dean Phillips, Senior Technology Director at PA Cyber, says that it’s best to “keep technology as simple as possible for the students and families. Standardization is key, I think, with everything you do from a technology standpoint. Making sure that you build from the inside out from the core. Your applications and networks and making sure that that’s consistent all the way to the endpoint, I think that’s extremely important.” PA Cyber’s lessons learned creating a secure and adaptive e-learning platform makes the goal of providing personalized instruction for every student achievable at scale.  Jennifer Shoaf, Deputy Chief Academic Officer at PA Cyber, explains how the school personalizes online instruction for every student. “It all starts when the student first comes to PA Cyber and we try to get an understanding of where they are and where they should be and where they want to see themselves, whether it’s in a month or in a couple years, or when they graduate from our school. So one of the things that we pride ourselves on here at this school is allowing for multiple modes of instruction for our students,” Jennifer said.
  • Capitalizing on the excellent asset management reporting autonomous endpoint security solutions have, CTOs and senior IT directors are gaining new insights into how to improve learning effectiveness. Having resilient, persistent connections to every endpoint with an undeletable digital tether also provides invaluable asset management data. Eric Ramos of Duarte USD and Dean Phillips of PA Cyber are leaders in this area of e-learning today. Eric Ramos says that asset management and activity reports made possible by the autonomous endpoint platform he is using from Absolute makes getting prepared for senior management meetings easy. “During principal meetings, I’m able to pull up these reports and say, look, these were the goals at the beginning of the year to use these four products at this amount of time. And here’s where you’re at on a small window. Or you can look at it over time and saying, this has been an increase here, this is a decrease here, these sites are doing really well with it, these sites may be not. But let’s now talk about what’s working for you. What are your teachers liking about the particular program? Or, program aside, how are your results coming about?” Eric Ramos, CTO said.

Conclusion

Delivering an excellent online learning experience needs to start with a cybersecurity strategy that includes autonomous endpoint security. Duarte USD and PA Cyber are leaders in this field, being among the first to see how combining core technologies while having an undeletable digital tether to every laptop is a must-have. Earning and growing the trust of parents, students, teachers and school administrators start with an endpoint security strategy that can adapt and grow as an e-learning program does.

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