Posts under Security

Image - Continuous Security - More on Gartner’s CARTA Model

Continuous Security - More on Gartner’s CARTA Model

In recent blog posts, we’ve been highlighting some of the key takeaways from Gartner’s recent security conference. In the session on the top 10 principles of CARTA (Continuous Adaptive Risk and Trust Assessment), Neil MacDonald highlighted how organizations need to change their security practices to match today’s world. One of the more interesting observations Neil made was that organizations in general have over-invested in preventative measures and they’ve underinvested in the detection and response.

Image - Gartner on Continuous Security - the Model

Gartner on Continuous Security - the Model

As we continue to explore some of the major themes from Gartner’s recent security conference, the theme of Continuous Security came up throughout the week. Gartner analyst Neil MacDonald spent time defining both the principles of CARTA – Continuous Adaptive Risk and Trust Assessment – and highlighting the priority security projects that adhere to those principles. Most security infrastructure, Neil argues, was designed for a world in which we knew good vs.

Image - Gartner on the Need to ‘Shift Right’ in Security

Gartner on the Need to ‘Shift Right’ in Security

Over the next week or so, I’ll be sharing some insights and observations from last week’s Gartner security summit conference. We’ll explore key conference themes around how DevOps and Security can work better together, the role of ML and automation, and the major challenges still confronting security practitioners. The infinite loop pictured here was a theme throughout many presentations. All visual models like this quickly become a little too complicated, but this vision of continuous security and a constant feedback loop between the build/deploy phase and the runtime phase really hits a chord with us here at StackRox.

Image - Where machine learning meets security

Where machine learning meets security

The last few decades have seen tremendous progress in machine learning (ML) algorithms and techniques. This progress, combined with various open-source efforts to curate implementations of a large number of ML algorithms has lead to the true democratization of ML. It has become possible for practitioners with and without a background in statistical inference or optimization – the theoretical underpinnings of ML – to apply ML to problems in their domain.