Skip to the main content

Ram is a Data Cowboy in Azure Security at Microsoft, working in the intersection of Machine Learning and Security. At Microsoft, his primary focus is modeling massive amounts of security logs to surface malicious activity. For instance, how do you detect an attacker is moving through the system when you have to analyze billions of events per second?

Another area of focus, is the use of machine learning systems for offense - for instance, what does automatic attack planning and automatic attack execution look in the context of red teaming? His work has appeared in industry conferences like BlueHat, DerbyCon, MIRCon, Infiltrate, Strata+Hadoop World Practice of Machine Learning as well as academic conferences like NIPS, IEEE Usenix, ACM - CCS.

At Berkman, he is broadly investigating two questions: How do we assess the safety of ML systems? What are the policy and legal ramifications of AI, in the context of security? Ram graduated from Carnegie Mellon University with a Masters in Electrical and Computer Engineering and a separate Masters in Innovation Management focusing on Telecom Policy. If you are working in Machine Learning or Security, he wants to hear from you! Always reachable on twitter @ram_ssk


BKC Medium Collection

Legal Risks of Adversarial Machine Learning Research

Studying or testing the security of any operational system potentially runs afoul of the Computer Fraud and Abuse Act

Jul 15, 2020
Harvard Business Review

The Case for AI Insurance

BKC’s Ram Shankar Siva Kumar joins Frank Nagle to explore Adversarial Machine Learning's impact on businesses.  “Most major companies, including Google, Amazon,…

Apr 29, 2020

Politics of Adversarial Machine Learning

Adversarial machine-learning attacks and defenses have political dimensions

Apr 23, 2020

Microsoft Wants More Security Researchers to Hack Into Its Cloud

As Microsoft works on cloud security, it’s looking to attract `White Hat’ hackers with rewards and legal guarantees.

Jun 7, 2019

Artificial Intelligence vs. the Hackers

Machine-learning algorithms watch hackers’ behavior and adapt to their evolving tactics.

A profile of "Data Cowboy" Ram Shankar Siva Kumar, who trains security algorithms

Jan 3, 2019

Law and Adversarial Machine Learning

A survey of existing legal remedies for attacks that have been demonstrated on machine learning systems, and suggests some potential areas of exploration for machine learning…

Dec 20, 2018

Law and Adversarial Machine Learning

When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond?

Oct 26, 2018