Confidential Computing offers a number of additional advantages that go beyond simple safeguarding. By ensuring that data is processed in a shielded environment it is possible to securely collaborate with partners without compromising IP or divulging proprietary information. ... Until now, many enterprises have held back from migrating some of their most sensitive applications to the cloud because of worries about data exposure. Confidential computing addresses this hurdle; not only is data protected during processing, companies can also securely and efficiently collaborate with partners in the cloud. For businesses migrating workloads into the cloud, a major concern is the ability to provide security for customers and continued compliance with EU data privacy regulations. This is especially the case where businesses are the stewards of sensitive data, such as healthcare information or bank account numbers. An important feature of Confidential Computing is its use of embedded encryption keys, which locks data in a secure enclave during processing. This keeps it concealed from the operating system as well as any privileged users i.e. administrators or site reliability engineers.
Technological expertise augmented by strong domain knowledge is important for an aspiring data scientist. One should have a clear understanding of the rules and practices of the industry before applying technological aspects to it. Be it automotive, BFSI, manufacturing or ecommerce, you can be a good data scientist in the field if you couple domain-specific knowledge with technical competence. Ideal candidates would have a degree or background knowledge of computer science or information technology. Data science is vast and may not suit everyone. Therefore, it is vital to have an aptitude to understand the data, see patterns, analyse from different perspectives and present findings to suit the end-user while also being open to understanding the domain. ... Industry partnerships are crucial to educational institutions. The two key components of a data science course are the fundamental conceptual foundation laid by highly qualified academicians and industry stalwarts with on-ground expertise and visibility. Both ensure that the key takeaways are beyond theoretical knowledge and include practical insights and understanding.
Digital twins could help guide decision-making as California completes its transition to 100% renewables, according to Parris, who points out that GE Digital is working with Southern California Edison, one of the state’s three largest investor-owned utility, to help model its operations. However, the mix of renewables in in the Golden State, not to mention Gov. Gavin Newsom’s ban on gasoline- and diesel-powered cars starting in 2035, will make it much harder to find a balance than in the Lone Star State. “It’s not just the heating [and cooling] of the buildings, but the cars,” Parris says. “It will be more distributed energy resources, like EVs [electric vehicles]. How do I bring them in? They add another complexity, because I don’t know when you’re going to charge your EV. I don’t know how much you’re going to use your car.” Backers of renewable energy are banking on large battery plants being able to handle short-term spikes in energy demand that have traditionally been handled by natural gas-powered “peaker” plants in California. But grid-scale battery technology is still unproven, and it also introduces more variables into the grid equation that will have to be accounted for. How long does that battery live [is] based on how often you charge and discharge it, so the life of the battery is a factor,” Parris says.
Computers have not become intelligent per se, but they have provided capabilities that augment human intelligence, he writes. Moreover, they have excelled at low-level pattern-recognition capabilities that could be performed in principle by humans but at great cost. Machine learning–based systems are able to detect fraud in financial transactions at massive scale, for example, thereby catalyzing electronic commerce. They are essential in the modeling and control of supply chains in manufacturing and health care. They also help insurance agents, doctors, educators, and filmmakers. Despite such developments being referred to as “AI technology,” he writes, the underlying systems do not involve high-level reasoning or thought. The systems do not form the kinds of semantic representations and inferences that humans are capable of. They do not formulate and pursue long-term goals. “For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations,” he writes. “We will need well-thought-out interactions of humans and computers to solve our most pressing problems. ...”
When dealing with HR AI tech, the limitations around diversity are the by-product of how solutions are designed. We are rapidly moving into space where solutions provide emotional recognition. AI analyzes facial expressions or body posture to determine decisions around recruitment. Current estimates expect emotion recognition is projected to be worth $25billion by 2023. Despite extraordinary growth in this area, there are challenges and significant kinks to be addressed, namely, ethical elements concerning the creation of the algorithms. Companies are grappling with HR AI and ethics. Recent examples demonstrate the enormity of the ramifications when things don't go according to plan. In other words, when things go wrong, they go badly wrong. Consider, for example, Uber, when fourteen couriers were fired due to a failure of recognition by facial identification software. In this case, the technology based on Microsoft's face-matching software has a track record of failing to identify darker-skinned faces, with 20.8 percent failure rate for darker-skinned female faces. The same technology has zero percent failure for white men.
Fortunately, using an old-school approach to AI and applying that to a different scope of the problem can save developers time in finding code by automating the process of precisely identifying the code that requires attention — regardless of how spread out it might be. Much like how contemporary AI tools cannot comprehend a book in a way a human does, human developers struggle to comprehend the intent of previous developers encoded in the software. By describing behaviors that need to change to AI tools, developers no longer have to labor searching through and understanding code to get to the specific lines implementing that behavior. Instead, developers can quickly and efficiently find potential bugs. Rather than dealing with a deluge of code and spending weeks searching for functionality, developers can collaborate with the AI tool to rapidly get to the code on which they need to work. This approach requires a different kind of AI: one that doesn’t focus on assisting the developer with syntax. Instead, AI that focuses on understanding the intent of the code is able to “reimagine” what computation represents into concepts, thereby doing what a developer does when they code — but at machine speed.
API security is at the forefront of cybersecurity. Emerging trends and technologies like cloud-native applications, serverless, microservices, single-page applications, and mobile and IoT devices have led to the proliferation of APIs. Application components are no longer internal objects communicating with each other on a single machine within a single process — they are APIs talking to each other over a network. This significantly increases the attack surface. Moreover, by discovering and attacking back-end APIs, attackers can often bypass the front-end controls and directly access sensitive data and critical internal components. This has led to the proliferation of API attacks. Every week, there are new API vulnerabilities reported in the news. OWASP now has a separate list of top 10 vulnerabilities specifically for APIs. And Gartner estimates that by 2022, APIs are going to become the number one attack vector. Traditional web application firewalls (WAF) with their manually configured deny and allow rules are not able to determine which API call is legitimate and which one is an attack. For them, all calls are just GETs and POSTs with some JSON being exchanged.
“The strategic concepts of Zero Trust have not changed since I created the original concept, through I have refined some of the terminologies,” he told Help Net Security. “I used to say that the first step in the five-step deployment model was to ‘Define Your Data.’ Now I say that the first step is to ‘Define Your Protect Surface.’ My idea of a protect surface centers on the understanding that the attack surface is massive and always growing and expanding, which makes dealing with it an unscalable problem. I have inverted the idea of an attack surface to create protect surfaces, which are orders of magnitude smaller and easily known.” Among the pitfalls that organizations that opt to implement a zero-trust model should try to avoid he singles out two: thinking that Zero Trust is binary (that either everything is Zero Trust or none of it is), and deploying products without a strategy. “Zero Trust is incremental. It is built out one protect surface at a time so that it is done in an iterative and non-disruptive manner,” he explained. He also advises starting with creating zero-trust networks for the least sensitive/critical protect surfaces first, and then slowly working one’s way towards implementing Zero Trust for the more and the most critical ones.
Innovation can come from the smallest and simplest of places. And the chances are, the cloud can take your business there, whether it’s to be more productive or agile, more sustainable, or secure. The important thing is for this vision to be clear, well communicated, and considered in all tech investments, hires and processes. For example, if a business wants to make better use of data across its operations, technologies such as IoT, AI and robotics will be critical to gathering, deciphering, and actioning that data across the cloud. Businesses will also be hiring and developing the talent to operate these tools. And we know this isn’t easy. UK businesses are hungry for cloud computing skills and the talent pool is not as big as they would like. They will also be thinking about the platforms available that enable the entire organisation — not just the tech team — to partake in this culture of data-driven operations. On the other hand, perhaps a business wants their cloud investment to bring them cost savings — a key driver for many migrations. To do successfully, CIOs will need to think strategically about how they are leveraging the cloud’s pay as you go ‘as a service’ model, whether they are using technologies, such as cloud virtualisation, to be more efficient or unlock revenue opportunities.
Like other blockchain-based platforms, NFT marketplaces are targeted by hackers. The centralized design of the marketplaces and the high value attached to NFTs make them prized targets. They can be subject to a range of attack vectors, including phishing, insider threats, supply chain attacks, brute-force attacks against account credentials, ransomware, and even distributed denial-of-service attacks. Blockchain design encompassing NFTs provides certain fundamental properties applicable to security, such as immutability and integrity checks. Immutability inherent in blockchain design is considered one of the core tenets of any transaction-security strategy. It's introduced to create a single source of truth and supports nonrepudiation, which is crucial for accountability of actions. But this still does not guard the platform against attacks leading to an account takeover (ATO), a major threat. There is a clear, exploitable scenario here as once an NFT has been transferred to someone else's wallet or sold, it may not be recovered by the sender or a third party. Enabling private keys to serve as gatekeepers is bound to create concentrated risk in one area, leading to a single-point-of-failure scenario.
Quote for the day:
"Most people live with pleasant illusions, but leaders must deal with hard realities." -- Orrin Woodward