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Think of all the data sources which include your personal information within the public administration services; be it bank account details, financial or medical records, tax information, etc. We often take it for granted that our data is safe and protected. However, what happens when this information is shared among different public administration entities? In reality, the General Data Protection Regulation (GDPR) laws safeguard the general public by limiting what data can be shared among entities, requiring that the data be anonymised before it is shared among different entities, including those within the public administration. The Multilingual Anonymisation for Public Administration (MAPA) Project is a European-funded project which is developing an open-source toolkit that enables effective and reliable text anonymisation, focusing on the medical and legal domains.
New technology always brings a host of worries about its negative potential, and 5G is no different. Although there are many legitimate 5G security concerns to address and discuss, it's worth first clarifying the false claim that 5G causes health problems. It doesn't -- 5G technology is not harmful to humans. With that out of the way, we'll discuss what 5G is, what security risks it faces, and how experts are working to ensure 5G security. What Is 5G? 5G stands for the fifth generation of cellular mobile network.
Artificial Intelligence (AI) is the faculty of a computer system to learn and reason, therefore, mimicking human intelligence. Over the course of the past several years, AI has become an indispensable part of cybersecurity measures. AI can predict cyberattacks with matchless precision, helps to create better security features that can bring down the number of cyberattacks and mitigate its impact on IT infrastructure. Artificial intelligence is a powerful cybersecurity tool for enterprises. It is rapidly turning into a sophisticated protective gear for enterprise cybersecurity, and many enterprises are adopting it at a rapid pace. Statista, in a recent post, noted that in 2019 approximately 83% of organizations based in the United States consider that without AI, their organization fails to deal with cyberattacks.
Yes, security is hard – no one is ever 100 percent safe from the threats lurking out there. But how is it that time and time again, companies – big companies – are continuing to fall for ransomware attacks? Let's explore the main reasons why, starting with some basics before getting more in-depth: Two-factor authentication (2FA) is probably the easiest security improvement an organization can implement, and it's one of the most advocated-for solutions by infosec professionals. Despite this, we continue to see breaches like Colonial Pipeline occur because organizations have either failed to implement 2FA or have failed to *fully* implement it. Anything that requires a username and password to access should have 2FA enabled.
The changing dynamics of the digital world have led to several privacy challenges for businesses, large and small. This is placing increasing pressure on them to evolve their processes and strategies. Much of the burden stems from the sheer volume of data present today, and in fact, the volume of data is predicted to balloon to 175 zettabytes (ZB) by 2025. Today, it is simply beyond human capability to effectively process and protect privacy without the assistance of privacy-enhancing technologies (PETs). This has led to an explosion of adaptive machine learning (ML) algorithms that can wade through the mountain of data while continuously and efficiently changing their behavior in real-time as new data streams are fed into them.
The European Union (EU) has launched the world's first comprehensive legislative package to regulate AI. The Artificial Intelligence Act (AIA), which is currently progressing through the EU legislative process, will establish a risk-based framework for regulating use of AI anywhere within the EU, including by companies based outside the EU. A limited number of unacceptable AI use cases, such as social profiling by governments, would be completely banned; high-risk use cases would be subjected to prior conformity assessment and wide-ranging new compliance obligations; medium risk functions are subject to enhanced transparency rules, and low-risk use cases can largely be pursued without any new obligations under the AIA. By legislating now, the EU hopes to establish a de facto global standard for AI. The EU is certainly well ahead of the US in this area, with debate in the US more focused on the extent to which the US may be falling behind China in military applications of AI, although some think tanks are looking at the ethics of AI and new state privacy laws have tasked regulators to develop standards for transparency and choice.
In today's modern era, AI and IoT are technologies poised to impact every part of the industry and society radically. Because most businesses devote their primary efforts to developing their brand, software applications, or network, new technologies are apt to transform how they operate. In addition, as companies attempt to draw better significance from the huge datasets gathered by linked devices, the potential of AI is accelerating the wider implementation of IoT. While businesses invest heavily in digitization, they are also incorporating AI throughout their IoT initiative, evaluating prospective future IoT ventures, and looking for ways to get greater value from current IoT deployments. Moreover, with the help of an AI development company, businesses can avoid unforeseen downtime, increase operational productivity, develop new services and products, and boost risk control.
Artificial intelligence is reorganizing the world, introducing innovations that will likely exceed those that came with the World Wide Web. And like the Web there were, and still are, security concerns. Today, trust in artificial intelligence is probably the single greatest risk to continuing AI innovation and adoption. A simple framework for the operational elements that need to be addressed for the responsible deployment of artificial intelligence must include'Fairness and Bias', 'Interpretability and Explainability', as well as the newest and equally important element of'Robustness and Security'. Note, that these are operational considerations, and privacy should be inherent in the design and implementation of responsible AI -- e.g., privacy must be foundational in every step of the process.
Cybercriminals are no longer a threat to be taken lightly in today's world. Each year, data theft affects more than a hundred thousand people. Unfortunately, this number is rising despite the availability of effective cybersecurity measures. In this particular context, how can AI play a role in improving cybersecurity? Many businesses and individuals are rushing to refresh their systems and protect their data. Due to the dramatic increase in threats, the need for security checks has increased.