Title of the Talk
Governance & Security in Enterprise AI HPC: Enabling AI Commercialization
Abstract
Artificial Intelligence (AI) adoption in enterprise environments is accelerating rapidly, with organizations deploying High-Performance Computing (HPC) infrastructures to support large-scale model training, real-time inference, autonomous decision-making, and generative AI applications. As enterprise AI workloads continue to expand across industries such as healthcare, finance, manufacturing, and digital services, governance and security challenges are becoming critical barriers to sustainable commercialization.
This session explores how enterprises can build governance and security frameworks capable of managing the growing complexity of AI HPC ecosystems. The discussion highlights key risks unique to AI systems, including data poisoning, adversarial attacks, prompt injection vulnerabilities, model inversion threats, and unmanaged "Shadow AI" deployments that expose organizations to compliance, operational, and reputational risks. The presentation further examines how traditional cybersecurity controls are insufficient for AI-native environments where models, datasets, infrastructure, and decision pipelines are deeply interconnected.
Attendees will gain practical insights into implementing enterprise-scale AI governance through centralized AI model cataloging, lifecycle monitoring, risk classification frameworks, secure data pipelines, model validation controls, and cross-functional governance councils. The session also addresses emerging regulatory pressures requiring organizations to demonstrate transparency, accountability, fairness, and explainability in AI systems.
The presentation demonstrates how organizations can transform governance from a compliance obligation into a competitive advantage by enabling faster AI deployment, reducing operational risk exposure, strengthening customer trust, and improving commercialization readiness. Attendees will leave with actionable implementation strategies for securing AI HPC environments while supporting scalable and responsible AI innovation across the enterprise.
Brief Profile
Sreenivasulu Kamireddy is a seasoned technology professional with over 19 years of comprehensive experience in Big Data, Data Science, and Database technologies. Currently based in Fort Mill, SC, he brings nearly a decade of specialized expertise in Hadoop, AI, and Machine Learning implementations across enterprise environments.
As a Hadoop Architect at Bank of America since March 2025, Sreenivasulu leads the AIHPC (AI High Performance Cluster) project, designing and implementing cutting-edge AI infrastructure that supports both on-premise and cloud-based inference workloads. His current role involves managing 77 servers with 616 GPUs deployed across BofA's core data centers, enabling the commercialization and scaling of approved AI models while maintaining enterprise-grade security and governance standards.
Throughout his career, Sreenivasulu has demonstrated exceptional technical leadership in Hadoop administration and Big Data solutions. His expertise spans multiple Hadoop distributions, including CDP, CDH, and HDP, with deep knowledge in cluster setup, high availability configurations, security implementations using Kerberos and SSL, and performance optimization. He has successfully managed large-scale implementations at major financial institutions, including his previous tenure at Bank of America (2020-2025) working on the Technology Infrastructure Master Data Hub, Wells Fargo's Corporate Model Risk platform (2018-2020), and AT&T's Global Fault Platform (2016-2018).
His technical proficiency extends beyond traditional Big Data technologies to encompass modern AI and ML frameworks, including IBM Watson ML, Power AI, TensorFlow, and cloud platforms such as Azure, GCP, and AWS. Sreenivasulu is particularly skilled in integrating diverse data streaming technologies like Kafka, implementing ETL solutions using Informatica and DataStage, and working with both SQL and NoSQL databases, including Oracle, PostgreSQL, DB2, HBase, and MongoDB.
Before transitioning to Big Data, Sreenivasulu built a solid foundation in database administration and application development, spending several years as a DB2 Administrator and COBOL Developer at IBM Global Services and other organizations. This background provides him with a comprehensive understanding of enterprise data management lifecycles and system integration challenges.
Sreenivasulu holds an M.C.A. from the University of Madras, India, and a B.Sc. in Computer Science from S.V. University, India. He maintains multiple professional certifications, including Microsoft Azure Administrator, Scrum Fundamentals, and numerous DB2 certifications across different platforms. His commitment to continuous learning and rapid adoption of new technologies has made him a valuable asset in fast-paced, evolving IT environments where he consistently delivers innovative solutions that drive business value and operational efficiency.