Dr. Sarah Lewis Cortes, is the Sr Manager of M&A Security at Salesforce.com for Mulesoft. She earned her undergraduate degree at Harvard University, and holds an M.S. from Boston University in Computer Science, CyberSecurity. She earned her PhD in Computer Science, CyberSecurity at Northeastern University’s College of Computing and Information Science, and studied Forensic Science at Boston University Medical School. Her research focuses on threat intelligence and the darknet, privacy and privacy law, network security, international criminal legal treaties (MLATs), and digital forensics.
She is also a postDoctoral researcher with the Alameda County Sheriff’s Department-Digital and Multimedia Evidence Crime Lab. Sheperforms training and consultation with the FBI, Interpol, and other law enforcement agencies. Prior to undertaking her Ph.D, Sarah was SVP, Security, IT Audit and Disaster Recovery at Putnam Investments, an investment management firm with over $400 billion in assets under management. She oversaw Putnam’s recovery on 9/11 when then-parent company Marsh & McLennan’s World Trade Center 99th floor data center was destroyed. Before that, Sarah was SVP, Data Center and Security Operations with BNY Mellon Bank, a global investments company with $1.6 trillion in assets under management, previously a part of Shearson/Lehman/American Express.
Sarah has published and lectured extensively on the darknet, computer security and privacy, including articles featured in the 2017 IEEE International Symposium on Technologies for Homeland Security (HST17). Together with Department Chair, Boston University School of Medicine, Department of Biomedical Forensic Sciences and former Cellmark lab director Dr. Robin Cotton et al., Sarah implemented the DNA Mixtures online tool, with a grant from the US Department of Justice. DNA Mixtures was highlighted in the Executive Office of the President, President’s Council of Advisors on Science and Technology (PCAST), Report to the President: Forensic Science in Criminal Courts: Ensuring Scientific Validity of Feature-Comparison Methods in 2016.
