Artificial Reality | The PatternEx Blog

Collections of the thoughts and the people behind the PatternEx Virtual Analyst Platform powered by AI2.

There Is No Auto-Pilot for Threat Hunting!

Vendors may claim complete automation for threat hunting, but they're promising the impossible. Threat hunters need a co-pilot—not a replacement. PatternEx can be a co-pilot and get rid of the mundane task of managing & sifting through data, and doing trial and error modeling around data analysis.

Detecting Lateral Movement with Data Science Sugar

Detecting Lateral Movement Webinar - a review of key use cases, data science sugar, and other interesting things we discussed in our latest webinar series.

AI SOC: All About the People

AI Enabled SOCs will change how people, process, and technology perform together more efficiently.

Data Exfil: AI Based Detection

Cloud based services like Gmail, Twitter and Facebook have emerged as another vector for data exfiltration and command and control (C2) attacks, and attacks through these channels are harder to detect and block. Here's how an AI solution can help.

AI Not Optional for the SOC

With the recent flood of cyber breaches, everyone is looking for answers to stop this seemingly unending cycle of attacks. The current model of multi-tier SOC analysts, SIEM, and basic ML alone is not our future. AI is no longer an option, but a requirement to keep up with threat volume and sophistication. Read more from our guest author, VP IT Security at a leading hospitality company.

Donald Trump, Theresa May, MIT & Apple all agree on one thing

Meta-trends around AI cybersecurity are driving industry and governments alike to drive change. Who is doing what and how can you get involved?

Can MDR's save the enterprise from security threats?

MDRs path to a smarter SOC to help enterprises solve security issues more cost effectively.

An Overview of AI for Security Pros: Lessons Learned from our Webinar

Get an overview of our recent webinar, "An Overview of AI for Security Pros," where we covered two of the critical elements of data science/AI applied to infosec -- labeling and data variety.

Success Criteria for PoCs

Try before you buy, or drive before you buy, or a proof of concept (PoC) which is the IT equivalent. However, to get the maximum value from a PoC, there are several important steps that need to be completed in advance. This is important for both the prospective customer as well as the vendor. Mistaken expectations can be costly for one or both of the parties. Do your homework! And, here is your homework assignment.

Rules vs. Correlations vs. Models

Information security practitioners need to broaden their vocabulary to understand machine learning terminology. For example, what are the differences between a "rule" versus a "correlation" versus a "model"? What is the difference between an algorithm versus a model?
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