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The Role of Artificial Intelligence in IoT and OT Security

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As the modern threat landscape continues to expand, adding artificial intelligence (AI) to a security strategy has become paramount to establishing and maintaining an effective security posture. Given the speed and complexity of modern cyberthreats and the current cybersecurity skills shortage, network security teams need the assistance of machine learning and other AI-based capabilities in order to detect, secure, and mitigate modern attacks. However, it should come as no surprise that while organizations are adopting AI to bolster their security efforts, cybercriminals are also adopting of things like agile software development, automation, and machine learning to potentially leverage AI themselves to better identify and more quickly exploit network vulnerabilities. Due to the growing number and variety of IoTand OT devices entering network infrastructures, cybercriminals already have the opportunity and capability to launch rapid, complex attacks that these inherently vulnerable devices as entryways into corporate networks. The potential attack capabilities posed by AI will only further compound the threats to today's digital transformation efforts.


30 companies merging AI and cybersecurity to keep us safe and sound

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By the year 2021, cybercrime losses will cost upwards of $6 trillion annually. It's no surprise, then, that the cybersecurity industry is exploding as it grows to protect the networks and systems on which companies and organizations operate and store data. Because effective information security requires smarter detection, many cybersecurity companies are upping their game by using artificial intelligence to achieve that goal. A new wave of AI-powered solutions and products keep bad actors on their toes while giving IT teams much needed relief. Here are 30 companies merging artificial intelligence and cybersecurity to make the virtual world safer.


How Automation and AI Improve Healthcare Cybersecurity - Security Boulevard

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When it comes to protecting patient information and proprietary medical research, the healthcare industry faces significant cybersecurity challenges every day. The adoption of new medical technology--including electronic health records (EHRs), online patient portals, connected devices and wearables--offers improved patient care and convenience. However, it also creates greater opportunity for attack. Of all the industries affected by advances in cybercrime techniques, healthcare providers continue to be at high risk. That's because providers not only store personal and financial data that's extremely valuable to criminals, but their network systems are also very sensitive to interruptions.


Artificial Intelligence for Smarter Cybersecurity

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Organizations continue to embrace the Internet of Things (IoT), the cloud, and mobile technology. This has influenced considerable changes in the threat landscape and created more vulnerability points. Cybercriminals are leveraging these new vulnerability points to develop and launch sophisticated, high-volume, multi-dimensional attacks. Such attacks mean that data is at risk, and organizations must analyze potentially malicious files. Using artificial intelligence software, organizations can process large volumes of threat data and adequately prevent and respond to breaches and hacks.


The 5-Step Guide to Making AI Work within your Cybersecurity Strategy

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In a rapidly transforming threat landscape, cyber defense solutions must be both innovative and flexible to harden organizational security against ever-evolving adversarial attacks. While current signature detection techniques effectively combat known attack structures, they are inherently reactive and require significant time to respond to sophisticated attacks. These challenges are compounded by the individualized characteristics of a given network, as each demands a system that understands its unique threats. Cybersecurity experts face the challenge of building flexible solutions that can learn the norms of a given network while rapidly adapting to defend against new attack structures. Generating timely identification of cyber threats hidden within the high volume of data generated by a network is an industry-wide problem that continues to challenge and stress organization's cybersecurity operations.