ExtremeAI Security. The software gathers data from a variety of sources to detect errant network traffic and report the anomaly to network operators. Extreme runs on its servers the security algorithms that analyze network, application and device data to identify malicious traffic. ExtremeAI Security gathers traffic flow data from NetFlow-enabled switches and routers. The software also draws IoT device data from Extreme's Defender for IoT and application data from Extreme Analytics. The fourth source of information is third-party threat feeds that provide continuous updates on blacklisted URLs and malicious IP addresses. Defender for IoT identifies IoT devices and assists network managers in setting security policies for groups of connected hardware, which could include medical devices, surveillance cameras or point-of-sale systems. Extreme Analytics draws application telemetry from a sample of network traffic flow to monitor application performance and notify managers when it falls below a set baseline. Extreme includes both in its list of Elements products.
Agile is a beneficial method for projects where the final goal is not set. As the project progresses, the development can adapt as per the requirements of the product owner. Kanban is about reducing waste and removing activities that never add value to the team. ... Kanban process is nothing but a Board, which is called "Kanban Board." Agile methodology is a practice which promotes continuous iteration of development and testing throughout SDLC life-cycle. Kanban process visualizes the workflow which is easy to learn and understand. The goal of the Agile method is to satisfy the customer by offering continuous delivery of software. In Kanban method, shorter cycle times can deliver features faster. In the agile method, breaking the entire project into smaller segments helps the scrum team to focus on high-quality development, testing, and collaboration. Kanban scrum needs very less organization set-up changes to get started. In Agile methodologies, Sprint planning can consume the team for an entire day.
Google faces strong competition as demand for team collaboration tools continues to soar; its rivals have already attracted significant numbers of users. Slack has 10 million daily active users, including 85,000 paid business customers, while Microsoft Teams, which like Hangouts Chat is available as part of a suite subscriptions, is used in 500,000 organizations. Facebook’s Workplace has more than 30,000 paid organizations and about 2 million users in total. It’s not clear how widely Hangouts Chat is actually used. The app is available as part of G Suite subscriptions, of which there are 5 million customers, but Google doesn’t break out stats for the messaging platform. Google’s offering appears to lag behind others in the market. “Based on our volume of conversations with clients, there isn’t much customer momentum with Hangouts Chat,” said Larry Cannell, a research director at Gartner. By integrating Hangouts Chat with Gmail, Google could spur greater adoption, said Angela Ashenden, a principal analyst at CCS Insight, providing an opening for adoption of the chat tool.
Intelligent automation presents a powerful new lever with which to digitally transform an enterprise and fundamentally change how work gets done. By combining a wide range of techniques to enable the digitization, processing and evaluation of information, companies can improve the performance of a function, the effectiveness of the employees involved and, ultimately, the experience of the customer. Unfortunately, many attempts to implement intelligent automation disappoint because companies try to automate their current environment, rather than optimizing that environment to best leverage new tools and truly enable their workforce. One flawed approach focuses on finding applications for specific tools, much like a hammer looking for a nail. Certain steps might be automated, but they are fragmented across the existing flow, yielding fragmented capacity that can’t easily be realized as a benefit. Another common pitfall occurs when a process includes tasks that the tool isn’t intended to address, yet the tool is applied anyway, overextending its capabilities and introducing the risk of instability.
Spreadsheets in particular make it immediately clear—simply by opening and glancing at a document—when you’ve been neglecting your good habits. My philodendron houseplant needs regular, consistent watering to thrive, and so does a goal-tracking spreadsheet, which otherwise appears riddled with holes made of missing data. The motivation to fill out the spreadsheet is baked into the form: All those sad, empty boxes need to be filled in, and only you can do it, by completing whichever task you’ve set out for yourself and then marking it as done in the correlated column. “Rather than fall into patterns of procrastination that just breed more stress and hopelessness, a brief and specific to-do list can help you stay on track,” says Hershenberg. “When you make any steps toward that item on your to-do list, you can and should celebrate that effort. Finding a sense of accomplishment from things that are hard to do is an important part of improving depressed mood and low motivation.”
At just about every customer site, we are asked to help train SOC managers to do a better job of communicating technical information to non-technical executives. This is hard enough to do when you have time to prepare what you want to say, so imagine how stressful it can be to explain the nuances of a ransomware situation to a CFO or CEO when a decision on whether or not to pay the ransom needs to be made in a matter of minutes. ... SOC teams must be able to collect and disseminate information and tasks across multiple teams. For example, when correlating information about a new attack, clues usually come from multiple sources: network and endpoint experts, malware analysts, operations teams, and additional team members. Incident responders must not only communicate effectively and succinctly, they must be able to delegate to and project manage multiple teams that may have limited understanding of cybersecurity, and under accelerated timelines where broken communication channels can have irreversible negative consequences.
There’s no denying that in the modern world, the explosion of knowledge (and the efficiency of capitalism) promotes specialization. If you break a tooth, after all, I would suggest you see my wife, the dentist, rather than me, the generalist. Unfortunately, increasing specialization can have the paradoxical effect of narrowing horizons and limiting innovation to incremental advances. The scientific grant funding system seems to reinforce this syndrome. In medicine, where the spread of specialization is most obvious, patients in the U.S. often get good results on complex procedures (at very high prices), while the health of the population at large suffers. Does that mean expertise has no value? Of course not. But someone needs to see the big picture. Citing economist Robin Hogarth, Epstein relates a useful distinction here between the different kinds of arenas people work in. Chess and golf are “kind” learning environments: “Patterns repeat over and over, and feedback is extremely accurate and usually very rapid.” These environments tend to have strict and unchanging rules, and they reward repetition. Practice may not make perfect, but it certainly makes better.
Organizations can best learn from companies – regardless of industry – that are exploring leveraging more than one DARQ capability to unlock value. This is where true disruption lies: those exploring how to integrate these seemingly standalone technologies together will be better positioned to reimagine their organizations and set new standards for differentiation within their industries. Volkswagen is an excellent example. The company is using quantum computing to test traffic flow optimization, as well as to simulate the chemical structure of batteries to accelerate development. To further bolster the results from quantum computing, the company is teaming up with Nvidia to add AI capabilities to future models. Volkswagen is also testing distributed ledgers to protect cars from hackers, facilitate automatic payments at gas stations, create tamper-proof odometers, and more. And the company is using augmented reality to provide step-by-step instructions to help its employes service cars.
Deep learning describes a particular type of architecture that both supervised and unsupervised machine learning systems sometimes use. Specifically, it is a layered architecture where one layer takes an input and generates an output. It then passes that output on to the next layer in the architecture, which uses it to create another output. That output can then become the input for the next layer in the system, and so on. The architecture is said to be "deep" because it has many layers. To create these layered systems, many researchers have designed computing systems modeled after the human brain. In broad terms, they call these deep learning systems artificial neural networks (ANNs). ANNs come in several different varieties, including deep neural networks, convolutional neural networks, recurrent neural networks and others. These neural networks use nodes that are similar to the neurons in a human brain. Neural networks and deep learning have become much more popular over the last decade in part because hardware advances, particularly improvements in graphics processing units (GPUs), have made them much more feasible.
I spend four chapters laying out the different government interventions that can improve cybersecurity in the face of some pretty severe market failures. They're complex, and involve laws, regulations, international agreements, and judicial action. The subsequent chapter is titled "Plan B," because I know that nothing in those four chapters will happen anytime soon. And I don't even think my Plan B ideas will come to pass. There are a lot of reasons for this, but I think the primary one is that technologists and policy makers don't understand each other. For the most part, they can't understand each other. They speak different languages. They make different assumptions. They approach problem solving differently. Give technologists a problem, and they'll try the best solution they can think of with the idea that if it doesn't work they'll try another -- failure is how you learn. Explain that to a policy maker, and they'll freak. Failure is how you never get to try again. Solving this requires a fundamental change in how we view tech policy. It requires public-interest technologists.
Quote for the day:
"Take time to deliberate; but when the time for action arrives, stop thinking and go in." -- Andrew Jackson