Kumuda Sreenivasa
Sr Data Architect ,ATC Drivetrain Founder ,Unimonk & GoIcure
Dallas, Texas, United States
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I am a Senior Data Architect at ATC Drivetrain and the Founder of two ventures—Unimonk, an AI-powered education platform, and GoICure, a digital-first medical tourism solution. I’ve spoken at KCD Washington 2025 and will also be presenting at BazelCon, sharing my experiences in cloud-native engineering and secure data systems. At ATC Drivetrain, I design hybrid cloud architectures that integrate AWS, Azure, Kubernetes, and big data pipelines for mission-critical shopfloor operations, driving improvements in governance, scalability, and predictive maintenance. Through GoICure, I’m building intelligent, compliance-ready platforms for cross-border healthcare, while Unimonk is focused on transforming education and university admissions through automation and AI. With a Master’s in Data Science from the University of Texas at Dallas and a strong foundation in distributed systems, MLOps, and secure data engineering, I bring both enterprise experience and entrepreneurial innovation to solving real-world problems at scale.
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LLM-Assisted Binary Exploitation: Automating Vulnerability Discovery Beyond Human Speed
The offensive security world is being reshaped by Large Language Models (LLMs). While traditional exploit development demands years of reverse engineering expertise, we are now witnessing an acceleration where LLMs can reason about binaries, generate exploit scaffolding, and uncover patterns missed by humans. This talk explores the frontier of LLM-assisted binary exploitation, combining symbolic execution, automated fuzzing, and reinforcement learning to push vulnerability discovery beyond human speed.
We will demonstrate pipelines where LLMs act as intelligent assistants for reversing: reasoning about assembly flows, suggesting exploit primitives, and automating payload generation. Real-world case studies highlight how AI tools can identify exploitable memory corruption bugs faster than manual triage, while also reducing false positives common in conventional fuzzers.
The session also covers defensive implications: how attackers may scale zero-day discovery using LLMs, and how defenders can counter by integrating AI into detection and patch pipelines. This is not about “AI hype” but concrete methods, code snippets, and open-source tooling you can experiment with.
Hackers will leave with a realistic view of where LLM-assisted exploitation stands today, what’s possible tomorrow, and how to prepare for an era where machine speed challenges human ingenuity.
Immutable Infrastructure for Clinical AI: Bazel for Dependency Governance in Healthcare ML
Deploying AI in healthcare requires more than accurate models—it demands strict reproducibility, traceability, and compliance. In this talk, we explore how Bazel enabled a fully reproducible, audit-ready ML pipeline used in clinical radiology. We demonstrate how Bazel was used to manage Python, C++, and Docker-based components, enforce hermetic builds, lock dependencies, and generate Software Bills of Materials (SBOMs) for compliance. With Bazel, we achieved deterministic model training, sandboxed preprocessing, and secure inference packaging across multiple hospital environments. Attendees will gain insights into structuring Bazel for regulated machine learning workflows, managing multi-language codebases, and building trust in sensitive AI systems. This session offers practical strategies for engineering reproducible, scalable infrastructure in real-world clinical settings.
Kumuda Sreenivasa
Sr Data Architect ,ATC Drivetrain Founder ,Unimonk & GoIcure
Dallas, Texas, United States
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