Preeti Ravindra
Preeti solves security problems with data driven methods and software as appropriate. Her research interest spans forward looking AI for security as well as security for AI.
She has experience developing and operationalizing machine learning differentiator solutions in security operations and attack surface management. Her value proposition is working cross-functionally and integrating security, data and services functions in adopting data driven practices to deliver business value. She gives back to the cybersecurity community by sharing her work at conferences and supporting women in cybersecurity through mentoring.
She is currently a Principal Research Engineer and holds a Masters Degree with a cybersecurity specialization from Carnegie Mellon University.
Session
Security co-pilots, chatbots and automation that leverage large language models are rampant in Security Operations with the intent of boosting analyst productivity and outcome quality. While there is a lot of focus on implementing GenAI use cases for the SOC, there is little focus on understanding the effects of introducing GenAI tooling before and after implementation in an analyst workflow leading to a counter-productive "AI in the human loop" scenario.
This session covers
1. Results from A/B testing different types of AI models with different levels of tooling and workflow integration and what it means for a security practitioner
2. Insights gained around friction points in integrating and obtaining alignment with GenAI in SecOps