Security Analytics in Age of AI: In-depth Guide [2019 update]

Every day 2.5 quintillion bytes of data are produced. Some of it is important (and private), like the most essential of financial and medical records. Some of it, like the cleaning path taken by an IoT vacuum is largely useless (or is it?). And all of it can become vulnerable without the right security effort.

Subsequently, there is a growing need for us to find new and better ways to protect our most sensitive data from a host of digital threats rise. Cybersecurity analytics can ultimately help to lay the foundation for large scale data protection.  Some questions that this post sets out to answer include:

What is cybersecurity analytics?

What are the benefits of cybersecurity analytics?

What are some common cybersecurity use cases?

How do AI and cybersecurity analytics go together?

What are some best practices for cybersecurity analytics?

What are some pitfalls/challenges in cybersecurity analytics?

What are tools to aid in effective cybersecurity analytics?

How can I start to establish a cybersecurity analytics program in my organization?

What is Cybersecurity Analytics?

Every day millions of cyber attacks are successfully executed around the world. Though it may be impossible to 100% prevent them all, we can certainly learn from them to help develop better ways to stay protected. Cybersecurity analytics studies the digital trail left behind by cyber criminals to help better understand weaknesses and how to prevent similar losses in the future.

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