Vaishnavi

As artificial intelligence integrates deeply into cloud computing infrastructures, the imperative for robust governance and ethical practices becomes paramount to mitigate risks and ensure equitable outcomes. This presentation examines key frameworks for AI governance within cloud environments, emphasizing principles of transparency, fairness, and accountability. Topics include regulatory compliance strategies, such as aligning with emerging standards like the EU AI Act and NIST guidelines, alongside technical approaches for bias detection, data privacy preservation through federated learning, and secure multi-tenant architectures. Drawing from case studies in enterprise deployments, the discussion will address challenges in ethical AI implementation, including auditability in distributed systems and the role of cloud providers in fostering responsible innovation. By exploring these elements, the talk aims to equip professionals with actionable insights for developing sustainable AI-cloud ecosystems that prioritize societal benefit while advancing technological frontiers.

Three learning objectives:

1. Identify core principles of AI governance and their application to cloud-based systems, including regulatory and ethical considerations for deployment.
2. Analyze techniques for mitigating ethical risks in AI models hosted on cloud platforms, such as bias auditing and privacy-enhancing technologies.
3. Develop strategies for integrating governance frameworks into MLOps pipelines, ensuring accountability across the AI lifecycle in scalable cloud environments.

Biography:

Vaishnavi Gudur is a Senior Software Engineer at Microsoft, where she leads initiatives at the intersection of full-stack engineering, AI automation, and cybersecurity. With a strong focus on building secure, compliant, and intelligent systems at scale, she has delivered enterprise-grade solutions that protect millions of users globally.

Her research spans generative AI, autonomous threat mitigation, and explainability in decision-making systems, with multiple publications in IEEE and Springer conferences. As a peer reviewer, conference committee member, and invited judge for global AI hackathons, Vaishnavi actively contributes to advancing the state of responsible AI and secure cloud infrastructure.

An engaging speaker and technical educator, she is passionate about demystifying complex AI systems and enabling developers to build ethically grounded, resilient technologies. Her talks seamlessly blend deep technical insights with forward-thinking perspectives on security, governance, and innovation in the AI era.

Sumeet

Abstract - As artificial intelligence rapidly reshapes the cloud computing landscape, its impact on cybersecurity remains concentrated within large enterprises—leaving small and mid-sized enterprises (SMEs) underprotected and underserved. This talk addresses how AI in cloud computing can be made more equitable by enabling SMEs to adopt scalable, cost-effective, and privacy-preserving AI-powered security solutions.

We explore practical approaches for embedding AI into SME cloud environments using open-source frameworks, cloud-native services, and federated learning. The session draws on real-world cloud security architectures and research prototypes that combine pre-trained AI models, anomaly detection pipelines, and lightweight automation to detect and respond to threats such as phishing, lateral movement, and credential abuse.

Attendees will gain actionable insights on how to implement intelligent, low-friction security mechanisms using tools available within common cloud platforms. The talk also highlights federated learning as a collaborative defense model that allows SMEs to benefit from shared intelligence without compromising data privacy.
Rooted in the core theme of AI in cloud computing, this session equips cloud practitioners, researchers, and security architects with the knowledge to extend AI’s cloud security capabilities beyond the enterprise—bridging the gap between innovation and inclusion.

Top 3 Audience Takeaways -
1. Practical ways to integrate AI into cloud security workflows
Learn how to apply pre-trained models, anomaly detection tools, and open-source AI frameworks to enhance security operations—without needing advanced AI expertise or high compute resources.
2. The role of federated learning in secure, collaborative AI
Understand how federated learning enables organizations to build smarter AI models while keeping sensitive security data decentralized and private—ideal for regulated or multi-tenant environments.
3. How cloud-native platforms make AI adoption more accessible
Explore how built-in AI capabilities from major cloud providers (like GCP and AWS) can be used to automate threat detection and incident triage with minimal overhead and configuration.

Biography:

Sumeet J. is a cloud security strategist at Google with over a decade of experience designing secure cloud architectures for enterprise environments. He has led major cybersecurity initiatives at Google, Akamai, and Capgemini, advising Fortune 500 clients on threat detection, cloud-native application security, and intelligent automation. Sumeet is also the Cybersecurity Tech Lead at the AI Collective, where he helps shape global initiatives at the intersection of AI and security. His current research focuses on privacy-preserving AI and federated learning for secure cloud deployments. He holds a Master’s in Telecommunications Engineering from the University of Maryland and also contributes to open source projects like the OWASP Gen AI Security Project.

Saichand

Abstract:
As enterprises increasingly adopt hybrid and multi-cloud environments, the challenge shifts from simply
deploying AI solutions to orchestrating scalable, secure, and cost-efficient AI automation. This session
presents a practical framework for integrating AI-powered automation across hybrid cloud
infrastructures-bridging on-premises systems and public cloud platforms.
Drawing on real-world industry use cases, we'll explore how intelligent pipelines can optimize DevOps,
infrastructure provisioning, and business operations through AI. Key considerations include using MLOps
best practices, deploying low-code AI solutions, and ensuring governance through ethical AI practices. The
talk will also highlight tools like AWS Step Functions, Azure ML Pipelines, and open-source orchestration
engines to enable robust AI lifecycle management at scale.
This session is designed for technologists, architects, and decision-makers looking to transform their digital
operations with practical, production-ready AI strategies.
Learning Objectives:
1. Understand the architectural patterns for deploying AI automation in hybrid and multi-cloud environments.
2. Learn how MLOps and low-code/no-code tools can accelerate enterprise AI adoption.
3. Explore strategies to ensure ethical, secure, and cost-effective AI governance in cloud environments.

Biography:

Saichand Raghupatrini is a seasoned AI Engineering Manager and Full Stack Architect with 12+ years of
experience leading enterprise cloud transformation and AI initiatives. He has built production-scale AI
pipelines using AWS, Kafka, and Spring Boot, and specializes in cloud-native architecture, MLOps, and
real-time systems. Saichand is an active contributor to the AI/ML research community, publishing papers on
secure AI systems and participating in peer-review panels. He is also working toward an EB-1A profile for
extraordinary ability in technology and frequently mentors professionals on scaling AI in the enterprise.
Saichand holds certifications in cloud architecture and AI governance and has spoken at IEEE-affiliated and
industry tech events.

Ramprasad

The modern HR landscape is undergoing a revolutionary transformation as
organizations pivot from traditional manual processes to intelligent, AI-powered
shared services centers. I will speak about the strategic roadmap for
implementing cutting-edge automation solutions that integrate enterprise
resource planning (ERP) systems with AI to create world-class HR service
delivery.
Building on my previous implementations and industry best practices, I will
explain how leading organizations achieve 30-50% cost savings while
significantly enhancing employee experience through automation. This includes
the entire technology architecture, from foundational ERP integration strategies
to advanced AI agents and predictive analytics using machine learning that
manage up to 95% of routine employee inquiries.
Participants will learn proven methodologies for overcoming common
implementation challenges, including system integration complexities, data
security concerns, and resistance to change management. The presentation
features my recent implementation that demonstrates a practical framework for
designing tiered service delivery models that scale with organizational growth.
Learning Objectives
1. Design and implement a comprehensive AI-powered HR shared services
architecture that integrates ERP systems, intelligent ticketing platforms,
and automated workflows to achieve measurable efficiency gains and cost
reductions.
2. Develop strategic roadmaps for overcoming common implementation
challenges while maximizing ROI through proven methodologies for
system integration, change management, and phased deployment
approaches.
3. Evaluate and select appropriate AI technologies and automation tools for
specific HR functions, enabling data-driven decision-making that
transforms HR operations into strategic business enablers instead of
managing repetitive tasks.

Biography:

As a SIEEE member, Ramprasad have over 20 years of work experience in the technology space, working with top companies such as NSF International, Abu Dhabi Investment Authority (ADIA), Oracle, and HCL Technologies. He is currently working as an Associate Director, HR
Information Systems. Throughout my career, he have been instrumental in driving digital transformation, AI and ML integration, and enterprise technology architecture. His expertise spans HR technology modernization, cloud ERP implementations, and AI-driven automation across various industries. These experiences have equipped him with a deep understanding of innovative solutions that enhance operational efficiency and business outcomes.