IoT sensors are now available in a variety of sizes, as well, allowing for more portability and increased ease of use. This, in turn, has played a key role in establishing new use cases, which have entered various industries. “Sensors have developed from electro mechanical devices, to micro electro mechanical devices (MEMS), to nano electro mechanical devices (NEMS) and now Bio-MEMS, which can sense molecular level changes,” said Deepak Parameswaran, chief business officer for Industry NxT at Mindtree. ... Manufacturing is another industry with plenty of use cases for IoT sensor technology, as well as much potential going forward. With the Industrial Internet of Things (IIoT) aiding innovation in a sector that’s been otherwise slower to adopt digital processes, this trend is showing no signs of slowing down. Richard Simmons, group vice-president of technology – IoT at Logicalis Group, explained: “One of our customers had dedicated employees to go around with clipboards and climb up cranes and large, often dangerous, equipment just to write down how long it was used for. Then they go back to their office and record it.
Specifically, narrowband IoT (NB-IoT) is unleashing powerful machine and sensor connectivity, delivering specific data, low latency, and increased power efficiency. And, it’s likely to drive millions of different types of connections and use cases. Connecting billions of devices presents challenges due to several concerns -- security, standardisation, authentication, and ubiquitous connectivity, the number one roadblock when deploying IoT. Nowhere is this dynamic more apparent than in India, a country largely connected using inadequate terrestrial telecom networks and very limited coverage across India’s vast hinterlands. Today, connectivity remains intermittent at best, often failing totally, while many still experience non-existent coverage in remote areas, where remote farms operate, at the borders, at rural power line stations, at last-mile distribution centres, far out to sea, and many other industrial operations. Even as IoT deployments grow to connect billions of machines, the increased volume of devices will take the deployments into remote parts, where they will experience little or no connectivity - and what connectivity is available will not be affordable.
Speaking of silos, disparate security approaches also create silos that can affect visibility. This can hinder threat detection and complicate the organization’s ability to see the full scope of a security incident. When creating a cloud security strategy, DevOps teams should consider adopting and implementing consistent policies that work in, across and outside of cloud environments. Use tools that allow for security configurations that can be centrally applied, tested and updated, and that support creating a consolidated view of the threats you face. This kind of consolidated view will also help security teams focus more on response and less on collecting information. A security platform that includes WAAP functionality combined with a common management, analysis and orchestration interface can help. This platform approach should include API security controls that can be deployed for every exposed API, which could include APIs deployed in multicloud and hybrid environments. The solutions you implement should also have the ability to block API threats using a WAF or other API gateway.
Kamikaze satellites and shuttles adrift: Why cyberattacks are a major threat to humanity's ambitions in space
Although there are currently no known examples of cybercriminals hacking directly into satellites, vulnerabilities in the user and ground segments have been exploited in attempt to alter the flight path of satellites in orbit. “By design, every piece of infrastructure has entry points, each of which has the potential to create opportunities for attackers,” said Yamout. “On Earth, with all the advancements and new technologies, we have a relatively good level of security protection. But in space systems, the protections are much more basic.” “With evolving technology and science, it is likely we will visit space more than we used to. Cybersecurity has to be considered when designing space systems in all layers and must integrate in all segments and phases of the space domain evolution.” No matter how well space infrastructure is protected, however, criminals will find a way to launch attacks. The question then becomes: who and why? At the moment, the incentives for cyber actors to launch attacks against space infrastructure are relatively few.
The traditional way to detect malware is to search files for known signatures of malicious payloads. Malware detectors maintain a database of virus definitions which include opcode sequences or code snippets, and they search new files for the presence of these signatures. Unfortunately, malware developers can easily circumvent such detection methods using different techniques such as obfuscating their code or using polymorphism techniques to mutate their code at runtime. Dynamic analysis tools try to detect malicious behavior during runtime, but they are slow and require the setup of a sandbox environment to test suspicious programs. In recent years, researchers have also tried a range of machine learning techniques to detect malware. These ML models have managed to make progress on some of the challenges of malware detection, including code obfuscation. But they present new challenges, including the need to learn too many features and a virtual environment to analyze the target samples. Binary visualization can redefine malware detection by turning it into a computer vision problem.
The term “metaverse” was coined by science fiction writer Neal Stephenson in his book Snow Crash. He described a popular virtual world experienced in the first person by users equipped with augmented reality technology. This idea was taken a step further in Ready Player One by Ernest Cline. He defined it as a fully realized digital world that exists beyond the analog one in which we live. While this futuristic vision may seem far-fetched, the reality which is beginning to take shape is just as exciting. Hailed as the next iteration of the internet, the metaverse sits at the intersection of the web, augmented reality and the blockchain. Until recently, you could only experience the internet when you go to it through a browser or app. The metaverse, with its wide range of connectivity types, devices and technologies will allow us to experience the internet on a huge variety of devices — from commonplace screens and cell phones to VR (virtual reality) and AR (augmented reality). Ultimately, the metaverse will allow us to play games, shop, trade, chat, work or even attend concerts.
One of the crucial applications of machine learning in the financial industry is credit scoring. Many financial institutions, be it large banks or smaller fintech companies, are in the business of lending money. And to do so, they need to accurately assess the creditworthiness of an individual or another company. Traditionally, such decisions were made by analysts after conducting an interview with an individual and gathering the relevant data points. However, artificial intelligence allows for a faster and more accurate assessment of a potential borrower, using more complex methods in comparison to the scoring systems of the past. ... Given how inflation is affecting our savings and the fact that it is no longer profitable to keep the money in a savings account, more and more people are interested in passive investing. And this is exactly where robo-advisors come into play. They are wealth management services in which AI puts together portfolio recommendations based on the investors’ individual goals (both short- and long-term), risk preferences, and disposable income.
Adopting passwordless requires trust in authentication. The number one concern raised in conversations around passwordless is this: what happens when this new factor is compromised? The answer lies in the next set of security benefits from passwordless. Pair strong user authentication with device authentication. By configuring workflows with rules, correlation, and policies, at-risk authentications can be identified and blocked, such as people using suspicious or new devices. More mature approaches will include user behavior analytics. Consider a criminal who is cloning or spoofing a person’s biometrics. With device authentication, the adversary will also need to compromise the person’s phone and computer without being detected. With behavior analytics, the criminal will also need to open apps that the person normally uses during typical work hours — again, undetected. This increases the complexity required for an attack, increasing the organization’s likelihood of recognizing and responding before the attempt is successful. Increasing trust in authentication creates barriers for criminals. It reduces risk and enables us to investigate factors other than passwords.
Today AI is merely a tool, but in the near future, AI will become a new corporate competency that is crucial to the delivery of a consistent CX through every customer touchpoint. This core competency is the ability to get real-time data from the market and execute real-time decisions. Adopting and using Business AI throughout the enterprise to automate business decisions will help companies develop this corporate competency. This is critically important to delivering a consistent CX because customer expectation is so ephemeral. Every intent signal, transaction data, customer interaction insight, real-time materials cost, market volatility, inflationary pressure, and even competitive moves can potentially change a customer’s expectation. Without AI it’s virtually impossible to keep up with the dynamics of customer expectation. While this new AI competency will be important for every business, it’s often cost restrictive to develop in-house. The teams, systems, and infrastructure required to test, manage, secure and maintain proprietary AI systems can oftentimes turn the deployment of AI into a full-blown R&D operation.
Achieving greater efficiency and scale is the most significant benefit HR teams say AI provides today. AI also enables companies to reduce turnover because it allows them to build employee career paths and present growth opportunities. When internal mobility is high and turnover is low, HR teams can focus their time and resources on scaling the organization. ... AI can’t solve all the problems HR faces; however, it can provide contextual data and intelligence to help reframe a problem, so HR teams know what needs to be solved. Contextual intelligence is the goal, with AI supporting HR teams’ experience, insights, and intuition. ... Talent mobility, diversity, equity and inclusion, talent acquisition, talent management, and governance were the leading topics covered in the 33 sessions. Based on customer presentations, it’s clear Eightfold is concentrating on helping their customers accelerate and improve talent acquisition. Customers including Dexcom and Micron explained how they’re relying on Eightfold for each stage of talent acquisition, including sourcing, screening, interview scheduling, diversity hiring, candidate experience, candidate relationship management, and on-campus hiring.
Quote for the day:
"Confident and courageous leaders have no problems pointing out their own weaknesses and ignorance. " -- Thom S. Rainer