The tech sector has long been governed by a certain subset of society and has lacked diversity. According to Diversity in Tech, 15% of the tech workforce are from BAME backgrounds and gender diversity is at 19%, compared to 49% for all other jobs within the UK. Considering the tech industry is growing almost three times as rapidly as the rest of the UK economy, tech and software development is a lucratively paid and in demand industry for those with the skills. However, there is no doubt it’s exclusionary. While this is a recognised issue many are keen to rectify, movement towards change is slow on the uptake. Socioeconomic dynamics mean privileged groups prevail. Change must happen at grassroots level. If children don’t have access to devices at home, attend schools with archaic software and hardware, or aren’t equipped with a support mechanism or role models, they will find themselves on the back foot for a career in tech. Roles such as software development take time to train and prepare for, meaning they can be hard to break into without background experience. Also, the lack of gender diverse and BAME role models within the tech industry perpetuates this imbalance.
Privacy concerns are not just related to the fact that stolen data could potentially harm patients and consumers, however. They are also tied to the simple reality that individuals feel as though they have no say in how their personal data is acquired, stored, and used by entities with which they have not meaningfully consented to share their information. According to the Pew Research Foundation, more than half of Americans have no clear understanding of how their data is used once it has been collected, and some 80% are concerned about how much of their data advertisers and other social media companies have collected. ... The legitimate concerns of consumers combined with a massive and growing amount of data theft make agreements like the one between Google and HCA unwise, despite potential benefits. While the data that Google will have access to will be anonymized and secured through Google’s Cloud infrastructure, it will be stored without the consent of patients, whose deeply personal information is in question. This is because privacy laws in the United States allow hospitals to share patient information with contractors and researchers even when patients have not consented.
Most of the classification tasks are based on images and videos. We have seen that to perform classification tasks on images and videos; the convolutional layer plays a key role. “In mathematics, convolution is a mathematical operation of two functions such that it produces a third function that expresses how another function modifies the shape of one function.” If you try to apply the above definition, the convolution in CNN denotes the operation performed on two images which can be represented as matrices are multiplied to give an output that is used to extract features from an image. Convolution is the simple application of a filter to an input image that results in activation, and repeated application of the same filter throughout the image results in a map of activation called feature map, indicating location and strength of detected feature in an input image. .... The CNN is a special type of neural network model designed to work on images data that can be one-dimensional, two-dimensional, and sometimes three-dimensional. Their application ranges from image and video recognition, image classification, medical image analysis, computer vision and natural language processing.
By enabling WebAssembly, Krustlet offers increased density, faster startup and shutdown times, smaller network and storage footprints, and all of these are features that not only support microservices but also operation on the edge and in IoT environments. In addition, WebAssembly also offers the ability to run on multiple architectures without being recompiled, has a security model that distrusts the guest by default, and can be executed by an interpreter and streamed in, meaning it can be run on the smallest of devices. “Krustlet, potentially combined with things like SUSE/Rancher’s k3s, can make inroads into IoT by providing a small-footprint extension to a Kubernetes cluster. This points to a sea change occurring in Kubernetes. When some folks at Google first wrote Kubernetes, they were thinking about clusters in the data center. But why think only in terms of the data center?” asks Butcher. “Imagine a world where the pod could be dynamically moved as close to the user as possible — down to a thermostat or a gaming console or a home router. And then, as the person left home, that app could ‘leave’ with them, hopping to the next closest place — yet still within the same cluster. Certainly, that’s tomorrow’s world, but Krustlet is a step toward realizing it.”
Unfortunately, many teams don’t think about security, and sometimes even overall governance, until it’s too late. Whether they don’t have the budget, think they don’t yet have the scale, or it’s just not top of mind, procrastinating on cloud security can expose an organization to breaches, non-compliance, and other high-risk issues. On the flip side, organizations might have initially taken too heavy-handed of an approach and implemented such strict controls that it prevents them from fully realizing the promise of cloud and DevOps in the future. Thinking about cloud security should happen early, which includes implementing not just the right tools, but also the right processes and people. And it’s never too early to start, because security needs to be woven into your process from the beginning. ... Organizations wanting to keep on top of their cloud security need to prioritize constant education and upskilling, not just around traditional security applied to the cloud but also around industry best practices and cloud fundamentals, too. Identify team members willing to go deeper and pair them with industry experts within the organization, or take advantage of free educational tools from the major cloud providers to keep your team’s knowledge base wide and ever-evolving.
In enterprise systems, automation refers to the ability to take a human operated task and reduce it to a data model, then create a script of code for repeatability. Compliance has typically been a labor-intensive practice. When considering the variety and amount of human labor required to meet compliance objectives, the concept of automation often cannot be broadly applied. Audit evidence collection, via an integration, lends itself well to an automated solution. This form of automation can also ensure the timeliness of evidence collection activity. However, this represents only a tiny percentage of the labor required to pass an audit. All organizations can realize benefits from automated compliance concepts by considering which tasks would traditionally require a consultant. Is that task repeatable across consultants? For example, performing an annual risk assessment. Another example is mapping exercises between an organization’s cybersecurity policies and controls against a common standard such as ISO 27001 or SOC 2. People are still required to ensure that the quality of these tasks are acceptable.
With more than a year of remote work for hundreds of thousands of people, many companies historically known for having on-premise based infrastructures are now shifting to multi-cloud strategies. Multi-cloud strategies are valuable because they provide the best possible cloud service for each workload. Today, our cyber security group is partnering with our digital transformation team to enable multi-cloud adoption in a way that advances and streamlines our specific business operations. Cyber leaders should develop risk controls upfront when ushering in multi-cloud strategies so that they don’t hinder the pace of adoption, while also protecting the company’s assets and data. ... Biometrics are a significant game-changer in cyber protection. It’s much harder for a threat actor to break into a system designed on behavioral attributes -- like how quickly people type, how they move their mouse, or what applications they have open -- than a system reliant on static passwords. In fact, we’re working with our data science team to pilot our own data models, leveraging new technologies available in the industry to replace passwords internally over time.
Over the past decade there has been an abundance of cases where well-known brands, which typically sit on a mammoth amount of historic data, have collapsed due to not handling it effectively. Companies including retailer Toys “R” Us, book chain Borders, and more recently, department store Debenhams, failed to optimise operations quickly enough to stay relevant in a highly competitive digital environment. Had they responded to what their data analysis was telling them, the outcome of these businesses could have been different. Adopting technology that can process and manage data as well as provide visualisations about what is happening within the organisation in real time can deliver greater insight into everything from product materials and production rates to customer shopping habits and market trends. By knowing what’s working and what’s not, businesses can make decisions based on the evidence the data shows, rather than relying on ‘gut instinct’. The pandemic is an excellent example of how the valuable data over big data can be used to drive decisions, as many businesses were forced to accelerate their digital strategies to remain viable. Management consultancy Mckinsey reports that the crisis brought about years of change to the way all companies and sectors do business
Quote for the day:
"If you don't understand people, you don't understand business." -- Simon Sinek