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Black Hat 2019: 12 Cybersecurity Myths That Could Put You At Risk

CRN asks 12 executives, sales and technical leaders attending Black Hat 2019 what they see as the top oft-repeated beliefs about cybersecurity that are foolishly accepted as fact.

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Data Models Can Identify Security Threats

Artificial intelligence today is really about automating the threat hunting process and training machines to look at data sets and identify potential risks within an organization that need to be addressed, said Mike Adler, vice president of the RSA NetWitness Platform. But the data models aren't mature enough today to definitely identify a security threat as opposed to merely pointing out suspicious activity, Adler said.

Many organizations don't share data well with vendors or with one another, which Adler said exacerbates the challenge around creating well-tuned models. Most organizations want these types of analytical calculations to run on-premises, but Adler said that takes a lot of computational resources and data storage space.

Resource availability issues as well as a lack of experience in building more accurate data models across the board have hampered their construction, Adler said. But Adler expects the analytical capabilities associated with AI and machine learning to evolve significantly over the next half-decade.

 
 
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