As with other ransomware gangs, such as Maze and Sodinokibi, the operators behind the Egregor ransomware are threatening to leak victims' data if the ransom demands are not met within three days, according to an Appgate alert. The cybercriminals linked to Egregor are also taking a page from the Maze playbook, creating a "news" site on the darknet that offers a list of victims that have been targeted and updates about when stolen and encrypted data will be released, according to the alert. "Egregors' ransom note also says that aside from decrypting all the files in the event the company pays the ransom, they will also provide recommendations for securing the company's network, 'helping' them to avoid being breached again, acting as some sort of "black hat pentest team," according to Appgate. It's not clear how much ransom the operators behind Egregor are demanding or if any data has been leaked, according to Appgate. A copy of one ransom note posted online notes the cybercriminals plan to release stolen data through what they call "mass media." While Appgate released an alert to customers on Friday, the Egregor ransomware variant was first spotted in mid-September by several independent security researchers, including Michael Gillespie, who posted samples of the ransom note on Twitter.
Do you measure the velocity of the team? Do you calculate how long a person was busy doing something? Do you measure estimated time for a task vs. actual time spent? Or measuring things like defects per story, defect removal efficiency and code coverage, etc. It is not that the above is harmful as long it is used for the right purposes like velocity for forecasting and code coverage for quality of code. But it makes more sense to measure time to market, customer satisfaction, NPS, usages index, response time, and innovation rate. If you were releasing once a year and now releasing every quarter, you have already improved by 400%, but would you like to stick here? Look at how much time your team takes from development to deployment in production? ... We wanted people to reach faster by driving faster. We taught them how to drive, manage traffic well, and put instructions everywhere, but people are still not going above 40 KM an hour. Although it has improved the overall time as there are fewer troubles while driving. When checked, people complained about 20 years old car that they have been driving. We have a similar story to our team.
Organisations often find it challenging to carry out business transformation projects successfully — and shaping the future of the workplace is no different. While there may be a willingness to change, there are many ways that change projects become stuck in the mire, their momentum stalled by hundreds of micro-actions taken (and not taken) throughout the organisation. The pandemic changed things. Businesses have learned that a major change project that would normally have taken six months to a year — such as enabling everyone to work remotely — can be done much faster. Necessity is indeed the mother of invention; innovation happens when people and organisations realise they have to act fast to stay competitive. ... As virtual working becomes less novel, more businesses will explore ways that they can support their employees and keep the team working efficiently. We’ll also start to see a re-evaluation of what working means. The days when it was defined by who sat at their desk the longest had already started to wane before the pandemic hit. Now, with the freedom to be creative that lockdown granted business leaders, companies are starting to look beyond hours worked and things produced and towards the quality of that work and the effect it has on the goals of the business.
Nowadays, the digital transformation is actually about applying a data-driven approach to every aspect of the business in an effort to create a competitive advantage. That's why more and more companies want to build their own data lake solutions. This trend is still continuing and those skills are still in need. The most popular tools here are still HDFS for the on-prem solution and cloud data storage solutions from AWS, GCP, and Azure. Aside from that, there are also some data platforms that are trying to fill several niches and create integrated solutions, for example, Cloudera, Apache Hudi, Delta Lake. ... There are Data Warehouses where the information is sorted, ordered, and presented in the form of final conclusions(the rest is discarded), and Data Lakes — "dump everything here, because you never know what will be useful". Data Hub is focused on those who do not belong to either the first or the second category. The Data Hub architecture allows you to leave your data where it is, providing centralization of the processing but not the storage. The data is searched and accessed right where it is located at the moment. But, because the Data Hub is planned and managed, organizations must invest significant time and energy determining what their data means, where it comes from and what transformations it must complete before it can be put into the Data Hub.
According to Ashenden, the need to support creativity and innovation is urgent for businesses in the current context. As a result, the tools that enable collaboration are getting a huge boost – and not a short-term one. "Those areas will become much more central going forward," she said. "A lot of work processes that once relied on face-to-face have gone digital now, and that won't go back. Even when people are back in the office – once these things live in a digital world, that's where they live." Connectivity, according to CCS Insights, will also change as a result of the switch to remote work. From next year, the firm expects network operators to offer dedicated "work from home" packages to businesses, differentiating between corporate and personal usage, so that employers can provide staff with appropriate services such as security, collaboration tools and IT support. Operators will also increase their focus on connectivity in suburban zones, rather than city centers, as the workforce becomes increasingly established outside of the office. And as connectivity becomes ever-more important, the research firm predicts that the next three years will be rocked by governments' actions to better protect their national telecom infrastructure.
It’s about discovering otherwise hidden and hard-to-find bugs. There’s a ton that we miss with basic unit testing, where we write out some fixed set of inputs and assert that our program produces the expected output. We overlook some code paths or we fail to exercise certain program states. The reliability of our software suffers. We are fallible, but at least we can recognize our limitations and compensate for them. Testing pseudo-random inputs helps us avoid our own biases by feeding our system “unexpected” inputs. It helps us find integer overflow bugs or pathological inputs that allow (untrusted and potentially hostile) users to trigger out-of-memory bugs or timeouts that could be leveraged as part of a denial of service attack. Some people are familiar with testing pseudo-random inputs via “property-based testing” where you assert that some property always holds and then the testing framework tries to find inputs where your invariant is violated. For example, if you are implementing the reverse method for an array, you might assert the property that reversing an array twice is identical to the original array.
Consumers have come to expect organizations to use their personal information to create custom solutions. Especially during the pandemic, consumers have become accustomed to the benefits of Netflix and Spotify using machine learning for entertainment recommendations, Zoom using just a couple clicks to create video engagement, and Google Home or Amazon Alexa using voice for everything from answering inquiries to simplifying shopping. These same consumers expect their bank or credit union to use their relationship date, behaviors and preferences the same way … or better. But, advanced analytics and AI should not be a goal in and of itself. These tools should be used to support broader strategies. According to Wharton, “Instead of exhaustively looking for all the areas AI could fit in, a better approach would be for companies to analyze existing goals and challenges with a close eye for the problems that AI is uniquely equipped to solve.” Some solutions include everything from fraud detection to facilitating predictive solution recommendations for customers. Now more than ever, AI needs to be used to deliver human-like intelligence across the entire organization.
The ongoing disruption of the pandemic has shown how important it can be for businesses to be built for change. Many executives are facing demand fluctuations, new challenges to support employees working remotely and requirements to cut costs. In addition, the study reveals that the majority of organizations are making permanent changes to their organizational strategy. For instance, 94% of executives surveyed plan to participate in platform-based business models by 2022, and many reported they will increase participation in ecosystems and partner networks. Executing these new strategies may require a more scalable and flexible IT infrastructure. Executives are already anticipating this: the survey showed respondents plan a 20 percentage point increase in prioritization of cloud technology in the next two years. What’s more, executives surveyed plan to move more of their business functions to the cloud over the next two years, with customer engagement and marketing being the top two cloudified functions. COVID-19 has disrupted critical workflows and processes at the heart of many organizations’ core operations. Technologies like AI, automation and cybersecurity that could help make workflows more intelligent, responsive and secure are increasing in priority across the board for responding global executives.
Energy efficiency with an enterprise may go hand in hand with other organizational traits, according to the report. Accenture’s research from 2013 to 2019 found that companies that consistently earned high marks on environmental, social, and governance performance also saw operating margins 4.7x higher than organizations with lower performance in those areas. There were also indications of higher annual returns to shareholders among those environmentally minded enterprises. In addition to the potential benefit cloud migration presents for the environment, Accenture’s report shows there can be total cost of ownership savings of up to 30-40% when organizations migrate to more cost-efficient public clouds. The report also shed light on how cloud migration affected Accenture’s expenses. The firm runs 95% of its applications in the cloud, the report says. After its third year of migration, Accenture saw $14.5 million in benefits, plus another $3 million in annualized costs saved by right sizing its service consumption. Moving to the cloud might not mean much in terms of cutting energy consumption if the service provider does not take steps to be more energy efficient.
Rather than separate out the memory and computing like most chips in use today, neuromorphic hardware keeps both together, with processors having their own local memory -- a more brain-like arrangement -- that saves energy and speeds up processing. Neuromorphic computing could also help spawn a new wave of artificial intelligence (AI) applications. Current AI is usually narrow and developed by learning from stored data, developing and refining algorithms until they reliably match a particular outcome. Using neuromorphic tech's brain-like strategies, however, could allow AI to take on new tasks. Because neuromorphic systems can work like the human brain -- able to cope with uncertainty, adapt, and use messy, confusing data from the real world -- it could lay the foundations for AIs to become more general. "The more brain-like workloads approximate computing, where there's more fuzzy associations that are in play -- this rapid adaptive behaviour of learning and self modifying the programme, so to speak. These are types of functions that conventional computing is not so efficient at and so we were looking for new architectures that can provide breakthroughs," says Mike Davies
Quote for the day:
"It's not the position that makes the leader. It's the leader that makes the position." -- Stanley Huffty