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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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1 Comments
User Rank: Ninja
4/12/2018 | 12:45:04 PM
Though the article warns: "...while segmentation enhances an organization's security posture, it also adds complexity [more properly: complication] and costs... "; I think that assumes an outside-in, rather than a truly systemic implementation of the fact-based business rules specific to that organization, which should be used to determine segmentation and sequestering. Micro-segmentation of the network directed from an informational requirements-based mapping, ought to result in a less complicated (so less costly in terms of added infrastructure), and more importantly dynamically responsive (to dynamic organizational requirements), solution. This is, after all, a software-defined approach. It only makes sense to incorporate the application-specific informational requirements system design which is (or ought to be), already serving that organization.