Claims raise troubling ethical and cybersecurity considerations

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Machine learning and artificial intelligence (AI) are becoming a core technology for some threat detection and response tools. The ability to learn on the fly and automatically adapt to changing cyberthreats give security teams an advantage.

However, some threat actors are also using machine learning and AI a to scale up their cyberattacks, evade security controls, and find new vulnerabilities all at an unprecedented pace and to devastating results. Here are the nine most common ways attackers leverage these technologies.

1. Spam, spam, spam, spam

Defenders have been using machine learning to detect spam for decades, says Fernando Montenegro, analyst at Omdia. “Spam prevention is the best initial use case for machine learning,” he says.

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Identity and access management (IAM) embraces a broad swath of IT practice.  This practice is subject to two forces pushing it towards greater prominence: increasing threat actor activity and increasing infrastructure complexity.  In response, we see increasing sophistication of the tools used to deal with both.

Web3 technology has unique characteristics that lend it to dealing with IAM.  To begin with, Web3 is built upon cryptography, with an unprecedented level of inherent privacy.  The validity of the blockchain is predicated on encryption; every piece of on-chain data is by its nature protected to a degree.

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One of UK’s largest ever drugs labs has been closed down

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Trellix finds bugs in OT used for physical access controls

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