“Grady Booch, one of the fathers of modern computer science, said the whole history of computer scientists layering is adding new layers of abstraction. On top of existing technology, low-code is simply a layer of abstraction that makes the process of defining logic, far more accessible for the most people. “Even children are being taught the code programming through languages such as MIT‘s scratch, a visual programming language. Just like humans communicate through both words and pictures with a picture, being worth roughly 1000 words. So, developers can develop using both code, and low-code or visual programming languages. “Visual language is much more accessible for many people, as well, much safer. So many business users who are great subject matter experts can make small dips into defining logic or user interfaces, through low-code systems, without necessarily having to commit hours and days to developing a feature through more sophisticated methods.” ... Tools that use a visual node editor to create code paths are impressive but the code still exists as a base layer for advanced control. I once built a complete mobile video game using these visual editors. Once workflows get slightly more complex it’s helpful to be able to edit the code these tools generate.
In the surgical team of XXI century, every artifact shall have a designated owner. With ownership comes responsibility for quality of the artifact which is assessed by people who consume it (for example, consumers of designs are developers, and consumers of code are other developers who need to review it or interface with it). Common ownership as advocated by Extreme Programming can only emerge as the highest form of individual ownership in highly stable teams of competent people who additionally developed interpersonal relationships (a.k.a. friendship), and feel obligated to support one another. In other situations, collective ownership will end up with tragedy of commons caused by social loathing. Each team member will complete his assignments with least possible effort pushing consequences of low quality on others (quality of product artifacts becomes "the commons"). This is also the reason why software development outsourcing is not capable of producing quality solutions. The last pillar is respect. It is important for architect and administrator not to treat developers, testers and automation engineers as replaceable grunts (a.k.a. resources). An architect being the front-man of the team needs to be knowledgeable and experienced but it doesn’t mean that developers or testers aren’t.
There are already signs of emerging disparity. Weekday footfall in big urban centres, which plummeted during lockdown, has not bounced back – the latest figures suggest less than one-fifth of UK workers have returned to their physical workplaces – which has led to reductions in public transport. This disadvantages low-income workers and people of colour, and has led to job losses at global chains such as Pret a Manger and major coffee franchises. Meanwhile, house prices in the Hamptons have reached record highs as wealthy New Yorkers have opted to weather the pandemic at the beach. Companies have also started capitalising on reduced occupancy costs – potentially passing them on to workers. The US outdoors retailer REI plans to sell its brand-new Seattle campus, two years in the making, in favour of smaller satellite sites. In the UK, government contractor Capita is to close more than a third of its 250 offices after concluding its 45,000 staff work just as efficiently at home. Not every community will be able to take advantage of the remote working boom, agrees Serafinelli. Those best placed to do so already have – or are prepared to invest in – good-quality schools, healthcare and transport links.
As digitalization transforms industries and business models, organizations increasingly are adopting modern software engineering practices such as DevOps and agile to become competitive in the modern marketplace. DevOps enables organizations to release new products and features faster, but this pace and frequency of application releases can conflict with established practices of handling security and compliance. This leads to the enterprise paradox to go faster and innovate but stay secure by avoiding compromises on controls. However, integrating security into DevOps efforts (DevSecOps) across the whole product life cycle rather than being handled independently or left until the end of the development process after a product is released can help organizations significantly reduce their risk posture, making them more agile and their products more secure and reliable. When properly implemented, DevSecOps offers immense benefits such as easy remediation of vulnerabilities and a tool to mitigate against cost overruns due to delays. It also enables developers to tackle security issues more quickly and effectively.
According to the Information Commissioner’s Office (ICO), while the government has said that transfers of data from the UK to the European Economic Area (EEA) will not be restricted, from the end of the transition period, unless the EC makes an adequacy decision, GDPR transfer rules will apply to any data coming from the EEA into the UK. The ICO website recommended that businesses consider what GDPR safeguards they can put in place to ensure that data can continue to flow into the UK. Forrester also highlighted the lack of an adequacy decision, which it said would impact the supply chain of all businesses that rely on technology infrastructure in the UK when dealing with European citizens’ personal data. The analyst firm predicted that cloud providers will start to provide a way for their customers to make this transition. The authors of the report recommended that companies should focus on assessing compliance with UK data protection requirements, including the UK’s GDPR, and determine how lack of an adequacy decision will impact data transfers and work on a transition strategy. While the ICO is the UK’s supervisory authority (SA) for the GDPR, in July the European Data Protection Board (EDPB) stated that it will no longer qualify as a competent SA under the GDPR at the end of the transition period.
"You have a much bigger attack surface; not necessarily because you have more employees, but because they're all in different locations, operating from different networks, not working with the organisation's perimeter network on multiple types of devices. The complexity of the attack surface grows dramatically," says Shimon Oren, VP of research and deep learning at security company Deep Instinct. For many employees, the pandemic could have been the first time that they've ever worked remotely. And being isolated from the corporate environment – a place where they might see or hear warnings over cybersecurity and staying safe online on a daily basis, as well as being able to directly ask for advice in person, makes it harder to make good decisions about security. "That background noise of security is kind of gone and that makes it a lot harder and security teams have to do a lot more on messaging now. People working at home are more insular, they can't lean over and ask 'did you get a weird link?' – you don't have anyone do to that with, and you're making choices yourself," says Sherrod DeGrippo, senior director of threat research at Proofpoint. "And the threat actors know it and love it. We've created a better environment for them," she adds.
It’s difficult to say how rapidly enterprises are buying AI and ML systems, but analysts say adoption is in the early stages. One sticking point is confusion about what, exactly, AI and ML mean. Those imagining AI as being able to effortlessly identify attempted intruders, and to analyze and optimize traffic flows will be disappointed. The use of the term AI to describe what’s really happening with new network management tools is something of an overstatement, according to Mark Leary, research director at IDC. “Vendors, when they talk about their AI/ML capabilities, if you get an honest read from them, they’re talking about machine learning, not AI,” he said. There isn’t a hard-and-fast definitional split between the two terms. Broadly, they both describe the same concept—algorithms that can read data from multiple sources and adjust their outputs accordingly. AI is most accurately applied to more robust expressions of that idea than to a system that can identify the source of a specific problem in an enterprise computing network, according to experts. “We’re probably overusing the term AI, because some of these things, like predictive maintenance, have been in the field for a while now,” said Jagjeet Gill, a principal in Deloitte’s strategy practice.
“With the explosion in the adoption of IoT (which is soon to be catalyzed by 5G wireless networking), countless data sources in our daily life now generate continuous streams of data that need to be mined to save lives, improve efficiency, avoid problems and enhance experiences,” Bain says in an email to Datanami. “Now we can track vehicles in real-time to keep drivers safe, ensure the safe and rapid delivery of needed goods, and avoid unexpected mechanical failures. Health-tracking devices can generate telemetry that enables diagnostic algorithms to spot emerging issues, such as heart irregularities, before it becomes urgent. Web sites can track e-commerce shoppers to assist them in finding the best products that meet their needs.” IMDGs aren’t ideal for all streaming or IoT use cases. But when the use case is critical and time is of the essence, IMDGs will be have a role in orchestrating the data and providing fast response times. “The combination of memory-based storage, transparent scalability, high availability, and integrated computing offered by IMDGs ensures the most effective use of computing resources and leads to the fastest possible responses,” Bain writes. “Powerful but simple APIs enable application developers to maintain a simplified view of their data and quickly analyze it without bottlenecks. IMDGs offer the combination of power and ease of use that applications managing live data need more than ever before.”
A solution for this crucial predicament is a potential temporary regulatory grace period. Regulatory bodies or lawmakers could establish a window of opportunity for organizations to self-identify the type and duration of their non-compliance, what investigations were done to determine that no harm came to pass, and what steps were, or will be, taken to address the issue. Currently, the concept of a regulatory grace period is slowly gaining traction in Washington, but time is of the essence. Middle market companies are quickly approaching the time when they will have to determine just what to disclose during these upcoming attestation periods. Companies understand that mistakes were made, but those issues would not have arisen under normal circumstances. The COVID-19 pandemic is an unprecedented event that companies could have never planned for. Business operations and personal safety initially consumed management’s thought processes as companies scrambled to keep the lights on. Ultimately, many companies made the right decisions from a business perspective to keep people working and avoid suffering a data breach, even in a heightened environment of data security risks. Any grace period would not absolve the organization of responsibility for any regulatory exposures.
Quote for the day:
"Our expectation in ourselves must be higher than our expectation in others." -- Victor Manuel Rivera