Strategies for Responsible Data Use and Model Integrity in the Age of Generative AI

About the Session

As AI systems become increasingly embedded in critical infrastructure, financial services, and national security, the demand for responsible governance of data and algorithms has never been more urgent. This session explores the evolving landscape of AI governance, focusing on practical frameworks for ensuring transparency, fairness, and robustness across large-scale AI deployments. Drawing on real-world experiences developing AI-powered cybersecurity applications and financial intelligence systems, this talk will address: • The challenges of data provenance and lineage in AI pipelines • Frameworks for model auditability and explainability in high-risk domains • Mitigating risks related to model drift, bias, and adversarial exploitation • Emerging standards and regulatory pressures (e.g., NIST AI RMF, EU AI Act) • How organizations can align technical implementation with ethical oversight With perspectives from engineering, research, and policy, this session will benefit both technical professionals and organizational leaders who are building or scaling AI solutions. Whether you’re working on autonomous systems, LLMs, or enterprise ML models, you’ll leave with actionable strategies for embedding trust and accountability into your AI lifecycle.

Speaker

Gbenga Akingbulere

Palo Alto Networks, Staff Software Engineer