The security industry frequently throws around the buzz words “Machine learning” and “artificial intelligence”, but how can you separate fact from fiction regarding claims about these technologies? And what role and value do these technologies actually have in solving today’s security problems?

Want to know a secret? What most vendors won’t tell you is that it’s not the machine learning algorithms that make the real difference: it’s the data used to train and optimize those algorithms. With a sufficiently large and relevant dataset, machine learning can be a powerful tool in a multi-layer security analysis chain, but it’s not a silver-bullet and it’s only as good as its input data and human designers.

The scale of mobile security threats today has reached “big data” proportions. Lookout, for example, tracks over 12 million unique piece of mobile malware and convicts on average 5,000 new threats every day. The sheer scale of mobile security issues like these has outstripped the capabilities of many traditional security technologies and created room for machine-learning driven approaches that can scale with this problem.

Watch this on-demand webinar to learn:

  • A 101 primer on machine learning driven security models
  • Tools to help you gauge the validity of machine learning claims
  • The advantages and limits of machine learning-driven security technologies
  • Mobile security threat insights from around the world
John Britton
Kevin Mahaffey
Co-founder and CTO, Lookout
Michael Murray
Michael Murray
VP of Research and Response, Lookout

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