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Don't Fall for the Hype – Marketing Myths in Artificial Intelligence for Cybersecurity - Security Boulevard

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The cybersecurity provider landscape is cluttered with impossible claims, misrepresentations, and a confusing mix of inconsistent terminology. Worse, every minute you delay making a decision is another minute hackers have to gain access and knowledge about your network. With so much on the line, choosing what kind of platform and which company to trust with your company's data privacy can become a stressful decision. Leaning toward an AI-enabled platform is a step in the right direction, but which platforms actually do what they say they do? Luckily, you don't have to become an expert in AI cybersecurity to learn how to evaluate the efficacy of AI-enabled cybersecurity platforms.


Why Unsupervised Machine Learning is the Future of Cybersecurity - MixMode

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Not all Artificial Intelligence is created equal. As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Dr. Igor, Chief Scientist and CTO at MixMode explains: Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning).


The (Recent) History of Self-Supervised Learning - Security Boulevard

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Real unsupervised AI spots security issues sooner and predicts future behavior more accurately than older first- and second-wave solutions. Self-supervised AI technology draws on an understanding of the fundamental nature of the network where it lives, an understanding that isn’t possible with supervised-AI.


Why Unsupervised Machine Learning is the Future of Cybersecurity

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As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning). Supervised Learning relies on a process of labeling in order to "understand" information.


Is AI cybersecurity's salvation or its greatest threat?

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If you're uncertain whether AI is the best or worst thing to ever happen to cybersecurity, you're in the same boat as experts watching the dawn of this new era with a mix of excitement and terror. AI's potential to automate security on a broader scale offers a welcome advantage in the short term. Yet unleashing a technology designed to eventually take humans out of the equation as much as possible naturally gives the industry some pause. There is an undercurrent of fear about the consequences if things run amok or attackers learn to make better use of the technology. "Everything you invent to defend yourself can also eventually be used against you," said Geert van der Linden, an executive vice president of cybersecurity for Capgemini.