SAMEENA BEGAM SAVUKATH ALI is a Principal Software Engineer with 15+ years of experience delivering enterprise solutions across Aerospace, Banking, and Healthcare industries. She specializes in Java, .NET technologies, ReactJS, Node.js, Microservices, GraphQL, Cloud Platforms (Azure, AWS, GCP), and DevOps practices. Currently working at Liberty Mutual, she leads cloud-native architecture, secure enterprise application development, and DevSecOps initiatives. Previously, she contributed to large-scale modernization and cloud migration programs at Humana, Aspire Systems, and HCL Technologies. She has published research papers on enterprise modernization and legacy system integration and actively serves as a reviewer for IEEE conferences and journals. Sameena has also contributed to the developer community as a hackathon judge and mentor. Recognized with multiple awards for technical leadership, innovation, and delivery excellence, she is passionate about engineering best practices, mentoring teams, and building scalable, high-impact solutions. She is currently pursuing a Master’s degree in Information Technology at Southern New Hampshire University, USA.

AI governance and frameworks Artificial Intelligence is rapidly transforming enterprise systems, enabling organizations to automate decisions, enhance operational efficiency, and build intelligent digital experiences. However, the rapid adoption of Generative AI, AI agents, and autonomous systems has also introduced significant challenges related to security, compliance, transparency, bias, accountability, and operational risk. As AI systems become increasingly capable of making decisions and executing actions independently, the need for strong AI governance has become a critical enterprise priority. This technical talk explores the importance of AI governance in building responsible, secure, and trustworthy AI systems. The session discusses the key pillars of AI governance, including transparency, security and privacy, fairness, accountability, reliability, and continuous monitoring. It also examines the risks associated with ungoverned AI systems, such as hallucinations, data leakage, regulatory violations, and uncontrolled autonomous behavior.