Vasanth Mudavatu
Birla Institute of Technology and Science, Pilani, India
McKinney, Texas, United States
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Vasanth Mudavatu is a seasoned IT leader with a career spanning nearly two decades, specializing in generative AI, machine learning, application security, and enterprise-scale software engineering. With a blend of deep technical expertise and strategic business acumen, he has consistently led transformative digital initiatives for Fortune 500 companies and global enterprises.
Currently serving as a Senior Principal Software Engineer and Product Manager, Vasanth is at the forefront of building secure, intelligent AI systems tailored for complex enterprise environments. His work focuses on generative AI model adaptation, large language model fine-tuning, retrieval-augmented generation (RAG), and secure application development. He has led the implementation of multi-agent AI solutions that address real-world challenges in risk evaluation, data privacy, and regulatory compliance.
Vasanth's background in both product strategy and hands-on engineering allows him to navigate seamlessly between vision-setting and execution. He defines product roadmaps, manages go-to-market strategies, and leads cross-functional teams to deliver high-impact AI/ML solutions. He brings a strong focus on user experience, data analytics, and measurable business value, consistently aligning technology delivery with organizational goals.
He has demonstrated leadership in modern DevOps practices, CI/CD pipeline optimization, and cloud-native development. His initiatives have significantly improved development velocity, automation, and resource efficiency across engineering and product teams. He is well-versed in tools like GitLab, Kubernetes, Docker, and security platforms tailored for AI workflows.
Before his current role, Vasanth held senior leadership and project management positions at leading technology firms, where he oversaw the design, integration, and deployment of large-scale systems in cloud, DevOps, and infrastructure automation. He has worked extensively in diverse domains including healthcare, financial services, cloud networking, and enterprise IT service management.
Vasanth holds an MBA from Boston University and a Master’s in Software Systems from BITS Pilani, India, in addition to his Bachelor’s in Engineering. His cross-functional expertise bridges engineering, product leadership, and enterprise transformation, making him a sought-after voice in the evolving AI and enterprise IT landscape.
He is known for fostering innovation, mentoring high-performing teams, and advocating for responsible AI development that prioritizes security, scalability, and usability. Vasanth continues to explore the frontiers of intelligent systems while delivering real-world value through technology.
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Secure-by-Design for Enterprise AI: Building Scalable, Trustworthy, and Responsible Intelligence
As AI becomes deeply integrated into enterprise data ecosystems—fueling innovation in finance, healthcare, manufacturing, and national infrastructure—its security cannot be left to chance. The growing threat of adversarial attacks, model inversion, and data poisoning exposes vulnerabilities not only in code, but in the very datasets that power intelligent systems.
This session introduces the Secure-by-Design (SbD) approach to AI security—a proactive, lifecycle-driven framework that integrates security from data collection through to deployment and monitoring. Tailored for data scientists, AI engineers, security professionals, and enterprise architects, the session demonstrates how to operationalize SbD using secure data pipelines, threat modeling for ML systems, adversarial robustness testing, and API governance.
Drawing on real-world implementations and emerging global standards—including the NIST AI Risk Management Framework and ISO/IEC 42001—we'll explore sector-specific case studies and tools that have helped organizations reduce AI risk, accelerate remediation, and strengthen compliance across cloud-native environments.
Whether you’re deploying large-scale models, managing AI data infrastructure, or leading AI governance initiatives, this session equips you with a practical, scalable strategy for building intelligent systems that are not only innovative—but resilient, ethical, and secure by design.
Commit Secure AI: Embedding Security in Code, Pipelines, and Production
As AI features become a standard part of modern applications—whether it’s a machine learning model embedded in a backend service or an LLM-powered assistant integrated into user workflows—security must be treated as a first-class concern by developers, not just platform teams.
This talk introduces Secure-by-Design (SbD) for developers working on AI-powered systems. It’s a practical, code-to-deployment strategy that weaves security directly into your AI development workflow—from threat modeling during architecture reviews to CI/CD pipeline enforcement, runtime monitoring, and hardened APIs. Whether you're deploying models via containers, integrating AI APIs, or building real-time systems, you'll learn how to secure what you commit—line by line, function by function.
We’ll explore real-world incidents of AI vulnerabilities—like adversarial exploits, data poisoning, and insecure inference endpoints—and show how teams that adopt SbD practices early reduce CVEs, accelerate remediation, and ship confidently. This session highlights tools and techniques that developers can immediately apply: secure model wrappers, permissioned model APIs, input sanitization, adversarial testing, and observability integration into tools like GitHub Actions, Terraform, and Kubernetes.
If you write code that touches AI, this session will help you own your security posture. The era of AI-enhanced development demands AI-secure code—and that starts with the people committing it.
From Black Box to Blueprint: Operationalizing Algorithmic Transparency in AI-Driven Security
As artificial intelligence plays an increasingly critical role in modern security architectures, algorithmic transparency has emerged as a foundational requirement for organizations committed to building responsible, resilient, and trustworthy AI systems. This session introduces a structured framework centered around four pillars—explainability, accountability, bias mitigation, and auditability—and explores how these principles enable organizations to gain visibility into AI-driven decisions, improve threat detection accuracy, and meet evolving regulatory demands such as GDPR and the EU AI Act. Drawing on real-world case studies and enterprise benchmarks, the session outlines actionable strategies for embedding transparency into the AI lifecycle, including interpretable model design, stakeholder-specific explanation models, and auditable decision pathways. Attendees will gain practical insights into balancing model performance with traceability, ensuring regulatory alignment, and building governance-ready AI systems that deliver both technical excellence and ethical assurance.
Vasanth Mudavatu
Birla Institute of Technology and Science, Pilani, India
McKinney, Texas, United States
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