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Security & Privacy


Infineon's Good Alarm System Places Machine Studying to Bear on Breaking Glass and Different Triggers - Channel969

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Infineon Applied sciences has launched what it claims to be the "trade's first" artificially clever acoustic occasion and sensor fusion alarm system to be pushed wholly by battery energy: the Good Alarm System, or SAS. "We're excited to allow a novel and differentiated strategy to bringing AI/ML [Artificial Intelligence/Machine Learning] capabilities to cost-sensitive, battery-powered house safety sensor methods, with out sacrificing battery life," says Infineon's Laurent Remont of the launch. Infineon's new edge AI alarm design gives a claimed five-year battery life. "Present house safety options are unreliable for detecting occasions similar to glass break[ing]," Remont continues. "Our new resolution combines a lot of best-in-class applied sciences to create an alarm system that's sensible, dependable and energy environment friendly. We look ahead to bringing extra progressive options into the house safety market."


3 reasons to consider adopting AI cybersecurity tools

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Cybersecurity threats are increasing at a mind-numbing pace. However, hesitation about integrating artificial intelligence (AI) into people-based processes is hindering organizations and their cybersecurity -- especially as bad actors weaponize AI in cyberattacks. AI produces insights derived from vast amounts of data, allowing organizations to make real-time decisions that can't be achieved with human effort alone. As a result, AI improves organizations' ability to prevent and mitigate cyberattacks before people can detect them.


The following are three reasons why you should consider using AI-based cybersecurity products – Bestgamingpro

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Threats to cybersecurity are rising at an incredibly rapid rate. Nevertheless, companies' and their cybersecurity's hesitancy to integrate artificial intelligence (AI) into processes reliant on humans is inhibiting, particularly when bad actors weaponize AI in cyberattacks. Organizations may make real-time choices that would be impossible with human effort alone thanks to AI's ability to mine large volumes of data for useful insights. As a consequence, AI enhances the capacity of companies to identify and respond to cyberattacks before they may be detected by human beings. The use of artificial intelligence (AI) may help identify, prevent and react to cyberattacks when partnered with cybersecurity best practices--and it can help make security decisions quicker and more accurately.


Zscaler announces new AI and ML innovations for a good digital experience monitoring

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Zscaler, Inc. (NASDAQ: ZS), the leader in cloud security, today announced newly advanced AI/ML innovations powered by the largest security cloud in the world for unparalleled user protection and digital experience monitoring. The new capabilities further enhance Zscaler's Zero Trust Exchange security platform to enable organizations to implement a Security Service Edge (SSE) that protects against the most advanced cyberattacks, while delivering an exceptional digital experience to users, and simplifying the adoption of a zero-trust architecture. Organizations are facing a 314 percent increase in cyberattacks on encrypted internet traffic and an 80 percent increase in ransomware with nearly a 120 percent increase in double extortion attacks. Phishing is also on the rise with industries like financial services, government, and retail seeing annual increases in attacks of over 100 percent in 2021. To combat advancing threats, organizations need to adapt their defenses to real-time changes in risk.


Improving digital employee experiences must start with cybersecurity

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We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Trading off usability for more hardened cybersecurity is the price vendors have been paying for decades to reduce their customers' breach risks. Enterprises bought into the logic, assuming the more challenging a security app or platform was to use, the more secure it was and capable of reducing risk. Fast-forward to today and organizations now need to support work-from-home employees, a new hybrid workforce and road warriors that require secure, real-time connections from their own devices to the most valuable data a business has. The pandemic forever changed everyone's perspective of an excellent digital employee experience. Ivanti's State of the Digital Employee Experience (DEX) study published this week provides insights into how enterprises move beyond trading off usability for security and what's most important to new, more virtual workforces.


Vectra AI wins the "Excellence in Threat Solutions Award" at the SC Media Awards Europe 2022 - Actu IA

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The London Marriott Hotel Grosvenor Square was the venue for the SC Media Awards 2022, the cybersecurity industry's coveted and prestigious awards ceremony on June 21. Vectra, a leader in AI-based cyber threat detection and response for hybrid and multi-cloud enterprises, won the "Excellence in Threat Solutions Award" in the "Best Enterprise Behavioral Analysis and Threat Detection" category for its Vectra AI platform. Vectra didn't just win that title, however, as it was also ranked at the event as "Highly Commended" in the "Best Use of Machine Learning and Artificial Intelligence", "Best Customer Service" and "Best Security Company" categories. Founded in 2010 and based in San Jose, California, Vectra is a leader in threat detection and response for hybrid and multi-cloud enterprises. Its Vectra AI platform uses AI to quickly detect threats in the public cloud, identity, SaaS applications and data centers.


Australian firm halts facial recognition trial over privacy fears

Al Jazeera

Australia's second-biggest appliances chain says it is pausing a trial of facial recognition technology in stores after a consumer group referred it to the privacy regulator for possible enforcement action. In an email on Tuesday, a spokesperson for JB Hi-Fi Ltd said The Good Guys, which JB Hi-Fi owns, would stop trialling a security system with optional facial recognition in two Melbourne outlets. Use of the technology by The Good Guys, owned by JB Hi-Fi Ltd, was "unreasonably intrusive" and potentially in breach of privacy laws, the group, CHOICE, told the Office of the Australian Information Commissioner (OAIC). While the company took confidentiality of personal information seriously and is confident it complied with relevant laws, it decided "to pause the trial … pending any clarification from the OAIC regarding the use of this technology", JB Hi-Fi's spokesperson added. The Good Guys was named in a complaint alongside Bunnings, Australia's biggest home improvement chain, and big box retailer Kmart, both owned by Wesfarmers Ltd, with total annual sales of about 25 billion Australian dollars ($19.47m) across 800 stores.


AI

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This special issue highlights the applications, practices and theory of artificial intelligence in the domain of cyber security. In the past few decades there has been an exponential rise in the application of artificial intelligence technologies (such as deep learning, machine learning, block-chain, and virtualization etc.) for solving complex and intricate problems arising in the domain of cyber security. The versatility of these techniques have made them a favorite among scientists and researchers working in diverse areas. The primary objective of this topical collection is to bring forward thorough, in-depth, and well-focused developments of artificial intelligence technologies and their applications in cyber security domain, to propose new approaches, and to present applications of innovative approaches in real facilities. AI can be both a blessing and a curse for cybersecurity.


What is Data Anonymization?

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Data anonymization is the process of mitigating direct and indirect privacy risks within data, such that there is a measurable way to ensure records cannot be attributed to a specific individual or entity. With an estimated 2.5 quintillion bytes of data being generated every day and an increasing reliance on data to power new applications, machine learning models and AI technologies, the importance of implementing effective anonymization techniques and removing any bottlenecks is crucial to accelerating future developments and innovations. This post is a general introduction to anonymization, and the tools and techniques for providing sufficient privacy protections, so that personally identifiable information (PII) is safe from exposure and exploitation. Data anonymization should be considered a continuous process; one that can require rapid iteration of applying various privacy engineering techniques and then measuring those privacy outcomes until a desired end state is reached. In the following sections, we'll dive deeper into our core tenets of the data anonymization process, and then walkthrough how you might apply them to a notional dataset.


Three opportunities of Digital Transformation: AI, IoT and Blockchain

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Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).