D&A leaders must develop DataOps as an essential practice to redefine their data management operations. This involves establishing business value before pursuing significant data engineering initiatives, and preventing duplicated efforts undertaken by different teams in managing the common metadata, security and observability of information assets within the data platforms.

The data landscape is rapidly evolving, with the rise of data marketplaces, AI, and self-service data access creating new data security and governance challenges. Now more than ever, data teams must quickly and constantly adapt their strategies to ensure data remains secure, compliant, and trustworthy while fostering innovation and data-driven decision making.

Data is the fastest growing asset for every enterprise, and traditional rule-based systems are not delivering intelligent, context-aware security that adapts at speed to evolving threats while reducing operational overhead. Join Ash Hunt to explore how enterprises are revolutionizing data security through the transformative power of artificial intelligence. Drawing from extensive experience protecting Fortune 500 companies, Ash will share how AI-native solutions are powering real-time threat detection and automated risk remediation across complex cloud environments.

As data becomes the lifeblood of modern enterprises, protecting it must be seamless, scalable and strategic. Discover how leading organisations use automation, tokenisation and AI to embed privacy from ingestion to sharing. Learn how built-in protection boosts breach resilience, automation cuts compliance costs, and controlled sharing enables monetisation. Real-world use cases show how AI and policy-based controls make privacy a driver—not a blocker—of faster, smarter decisions.

Embark on a transformative AI journey with our session focused on deploying AI agents that deliver immediate ROI while ensuring robust data security. We’ll delve into advanced AI orchestration techniques that not only enhance system efficiency but also improve employee productivity. By incorporating TRiSM principles, you’ll learn how to develop AI applications that are both trustworthy and risk-managed. Whether you are just beginning your AI journey or seeking to expand your existing framework, this session offers practical insights to transform AI potential into meaningful business outcomes.

As organizations face growing regulatory demands and data complexity, a mature data governance strategy is key—not just for compliance but for efficiency, risk reduction, and business value. At Centrica, data discovery with BigID enhances governance with visibility and control across the data lifecycle. From DSARs and data lifecycle automation to security and compliance, BigID helps Centrica reduce risk, accelerate response times, improve efficiency, and drive smarter decisions. Join BigID and Centrica to explore key insights, challenges, and best practices.

AI's potential depends on quality data. Many struggle with AI due to data governance or slow processes, especially with unstructured data. Join peers in discussing strategies for improving and governance to maximise AI potential, managing structured and unstructured data, connecting LLMs with enterprise data and data security best practices.

Discover why one-size-fits-all cloud approaches to AI often lead to cost inefficiencies, performance issues, and security risks. This session reveals how Private AI-building AI applications with enterprise data on infrastructure you control-delivers powerful insights without compliance concerns. Learn how Amdocs' true hybrid architectures optimize costs by deploying AI workloads where they make most sense financially and operationally in the monetization domain. Hear about Amdocs’ real-world implementation story and its vision for Private AI.

Companies must modernize to stay competitive, but siloed, unreliable data makes digital transformation nearly impossible. Traditional solutions fail to address data fragmentation, lacking the ability to power business applications in real-time or deliver reliable data when and where it’s needed.

Join this roundtable to explore how to implement a comprehensive data unification framework to improve trust, reusability, and governance, ensure continuous data reliability for AI systems, and mitigate security risks in AI-driven processing and decision-making.