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How Machine Learning, AI & Deep Learning Improve CybersecurityMachine intelligence is influencing all aspects of cybersecurity. Organizations are implementing AI-based security to analyze event data using ML models that identify attack patterns and increase automation. Before security teams can take advantage of AI and ML tools, they need to know what is possible. This report covers:
-How to assess the vendor's AI/ML claims
-Defining success criteria for AI/ML implementations
-Challenges when implementing AI
Incident Readiness and Building Response Playbook
In this special report, learn the Xs and Os of any good security incident readiness and response playbook.
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User Rank: Ninja
6/30/2017 | 12:51:40 PM
Stories about folks like Aaron Swartz (R.I.P.), Ed Snowden and Julian Assange also then became more about the "privacy" discussion than "security" when, in many cases, it really should have started with a discussion about security. I'm not taking a stance against privacy, or making a comment for or against these folks or organizations like Anonymous. Rather, I'm pointing to the evolution of how we as consumers of word meaning and media stories got here. I also see a lot of credit going to the tech legal eagles who have fought hard to blur lines to secure rights to "privacy" for the individual but also (not intentionally, I'm sure) threatening "security" in the process by 1) causing this confusion in meaning and 2) putting "privacy" as a proposed "right" before the rights of all consumers to have access to "security" in the products they use, the transactions they make, the information they obtain.
I think this is not just about defining each word clearly when defining your project or selling a solution, but it is also about making sure the frenzy behind "privacy" doesn't put your "security" project at risk, a situation I'm sure many an Enterprise Desktop, Mobile and Email security team has run into.