Ransomware attacks have become more powerful and lucrative than ever before – to such an extent that advanced cyber-criminal groups have switched to using it over their traditional forms of crime – and it's very likely that they're just going to become even more potent in 2021. For example, what if ransomware gangs could hit many different organisations at once in a coordinated attack? This would offer an opportunity to illicitly make a large amount of money in a very short amount of time – and one way malicious hackers could attempt to do this is by compromising cloud services with ransomware. "The next thing we're going to see is probably more of a focus on cloud. Because everyone is moving to cloud, COVID-19 has accelerated many organisations cloud deployments, so most organisations have data stored in the cloud," says Andrew Rose, resident CISO at Proofpoint. We saw a taster of the extent of the widespread disruption that can be caused when cyber criminals targeted smartwatch and wearable manufacturer Garmin with ransomware. The attack left users around the world without access to its services for days. If criminals could gain access to cloud services used by multiple organisations and encrypt those it would cause widespread disruption to many organisations at once.
Synthetic data is data that is artificially generated rather than collected by real-world events. It is data that serves the purpose of resembling a real dataset but is entirely fake in nature. Data has a distribution, a shape that defines the way it looks. Picture a dataset in a tabular format. We have all these different columns and there are hidden interactions between the columns, as well as inherent correlations and patterns. If we can build a model to understand the way the data looks, interacts, and behaves, then we can query it and generate millions of additional synthetic records that look, act, and feel like the real thing. Now, synthetic data isn’t a magical process. We can’t start with just a few poor-quality data points and expect to have a miraculous high-quality synthetic dataset from our model. Just like the old saying goes, "garbage in, garbage out," in order to create high-quality synthetic data, we need to start with a dataset that is both high-quality and plentiful in size. With this, it is possible to expand our current dataset with high-quality synthetic data points.
The popularity of DeFi—using crypto technology to recreate traditional financial instruments such as loans and insurance—has exploded over the last year or so, growing to a $16 billion global market. The price of ethereum, the world's second largest cryptocurrency by value, has soared this year as investors pour funds into DeFi projects that are built on top of it. "There's more and more DeFi innovators in London," said Stani Kulechov, the founder and chief executive of London-based technology company and DeFi protocol Aave, speaking over the phone. "Up until recently, fintechs and banks have been all about innovating on the front-end—the user experience. Now, DeFi is helping the back-end innovate." Aave, a money market for lending and borrowing assets, has become one of the top DeFi protocols since it was created in 2017 and was given an Electronic Money Institution license in July by the U.K.'s Financial Conduct Authority (FCA). "I think we'll see London emerge as a hub for DeFi," added Kulechov. The City of London, a financial powerhouse rivaled only by New York, is currently under threat as the U.K. prepares to end its transition out of the European Union at the end of this month.
With more and more startups relying on data driven business models and analytics for improving the service/product, and using data for their competitive advantage, data governance laws with steep compliances are a cause for worry. The regulations will have a direct effect on how the businesses deal with data available to them, and that is on the market. The regulatory uncertainty in matters pertaining to handling data and the drawing economic value from data, causes indirect impact on long term innovation and investments as well. Investors that are looking for facilitating growth in the domestic market are also deeply concerned about the current trend of steep compliance, excessive government access to data and regulatory uncertainty. In this context, the commonalities in the two frameworks are pertinent to note. Firstly, both the PDP and the NPD framework restrict cross border data flows, citing reasons pertaining to sensitivity of data that underlies it. While the concerns regarding harm are valid, the solution to address the concerns might be misplaced. The assumption is that security is better served if the data is stored within the territorial limits of the country and that rests on shaky grounds.
"Cybersecurity is very good at identifying activities that are black or white--either obviously bad and dangerous or clearly good and safe," writes Margaret Cunningham, PhD, psychologist and principal research scientist at Forcepoint's Innovation Lab, in her research paper Exploring the Gray Space of Cybersecurity with Insights from Cognitive Science. "But, traditional cybersecurity tools struggle with ambiguity--our algorithms are not always able to analyze all salient variables and make a confident decision whether to allow or block risky actions." For example, an employee accessing sensitive files after company business hours might not be a security issue--the person could be traveling and in a different time zone. "We don't want to stop the person from doing work because the access is flagged as an unapproved intrusion due to the time," says Cunningham. "Building the capability to reason across multiple factors, or multiple categories, will help prevent the kinds of concrete reasoning mistakes that result in false positives and false negatives in traditional cyber toolsets." The success of cybercriminals, admits Cunningham, is in large part due to their ability to quickly morph attack tools, and cybersecurity tech cannot keep pace.
If you are putting in place a data governance framework you can’t put controls and data quality reports on every single piece of data throughout your organisation. But if you have data lineage it will help you identify the areas where your data is most at risk of something going wrong, enabling you to put in place appropriate checks, controls and data quality reports. Having data lineage also allows you to speed up data discovery. So many organisations have vast quantities of data that would be valuable to them, if only they knew it existed. Finally, as I mentioned at the start of this article for many industries there is a regulatory requirement to have data lineage in place. It’s clear that having data lineage has lots of benefits, but on so many occasions data lineage is captured and documented manually. Whether you do data lineage automatically or manually you will achieve the benefits mentioned above, but taking a manual approach to data lineage requires considerable effort. When I first started capturing data lineage I tried starting at the beginning, where data first comes into the organisation and tried to follow it as it flowed. However, this approach fails because a lot of people who produce or capture data have absolutely no idea where it goes.
Unruh says part of his challenge when he joined Credit Karma about three years ago was to increase efficiency of releasing code across the company. The engineers there had been using an older Jenkins-style system, he says, which served as a generic job runner. Developing products on that system meant clearing a few hurdles along the way, Unruh says, including jumping through a remote desktop running on a Windows computer. On top of that, teams building new microservices were required to write custom deployment code to move production forward, he says. That would be the basis for the job for the system to execute the service, Unruh says. That meant everything was different because every team took their own approach, he says, which slowed them down. “It linearly required 15 steps just to deploy your service into production,” Unruh says. “It was really cumbersome and there was no way for us to standardize.” Looking for ways to improve efficiency, he wanted to eliminate the need to jump to another host just to access the system. Unruh says he also sought to end the need for custom code for deploying a service. “I just build a service and I can deploy it,” he says.
Retrospectives antipatterns are patterns I have seen recurring in many retrospectives, and the way I have described them in the book is in the context you would normally find them, the antipattern "solution" that is often used for various reasons, such as haste, ignorance, or fear, and the refactored solution to this antipattern. Some of the antipatterns have a refactored solution that will get you out of the pickle right away, but for some of the others it is more a warning of things to avoid, because if you find yourself in that antipattern there is nothing better to do than to consider other options for the next retrospective. ... The prime directive was written by Norm Kerth in his book "Project Retrospectives: A Handbook for Team Review" and it goes like this: "Regardless of what we discover, we understand and truly believe that everyone did the best job they could, given what they knew at the time, their skills and abilities, the resources available, and the situation at hand." It basically means that when we enter a retrospective we should strive to be in the mindset that allows us to think that everybody did the best they could at all times, given the circumstances.
Often, diversity and inclusion outcomes are directly linked to recruitment and outreach efforts. But while many people fret about flaws in the education system that seem to discourage young women from pursuing tech-related subjects, Barrett has found in her work with Girls Who Code that the problem lies elsewhere. It’s not a lack of interest amongst female students, she said. Instead, it’s the culture of the technology industry. Girls who complete the organization’s program go on to major in computer science at a rate of 15 times the national average. But, Barrett noted, “our girls still don’t feel welcome in tech.” According to a recent report by Girls Who Code and consulting firm Accenture, it’s possible to lower the attrition rate for female employees by 70% over the next decade. The study’s recommendations include establishing supportive parental leave policies, creating external goals and targets around diversity, providing workplace support for women and creating inclusive networking opportunities. Role models are also crucial. “We know very often that women report that it’s hard to be what they can’t see,” Barrett said. “It’s hard to feel connected to an organization when they don’t see women in tech thriving.”
Macquarie Government managing director Aidan Tudehope said he was disappointed by the decision to discontinue the CCSL certification regime. "This is about more than simply the physical geographic location where data is stored. Data sovereignty is about the legal authority that can be asserted over data because it resides in a particular jurisdiction, or is controlled by a cloud service provider over which another jurisdiction extends," he said. "Data hosted in globalised cloud environments may be subject to multiple overlapping or concurrent jurisdictions as the debate about the reach of the US CLOUD Act demonstrates. As the ACSC points out, globalised clouds are also maintained by personnel from outside Australia, adding another layer of risk." He believes the only way to guarantee Australian sovereignty is ensuring data is hosted in an Australian cloud, in an accredited Australian data centre, and is accessible only by Australian-based staff with appropriate government security clearances. "Taken alongside Minister Robert's planned sovereign data policy, this guide opens new opportunities for Australian cloud service providers," he said.
Quote for the day:
"The most important quality in a leader is that of being acknowledged as such." -- Andre Maurois