Beat the rush and join us early for complimentary barista-made coffee and breakfast.

Explore the evolving challenges and opportunities that artificial intelligence introduces to data protection, emphasising risk management and ethical AI deployment.

‍

• Navigating new data vulnerabilities and AI-specific cyber threats such as deepfake fraud and automated phishing.
• Balancing AI automation with human oversight to maintain security and ethical standards.
• Leveraging AI technologies to enhance data protection capabilities without compromising privacy compliance.

As organisations accelerate their adoption of generative AI, protecting sensitive information has become a critical business priority. Understanding how AI systems access, process, and interact with enterprise data is essential to balancing innovation with governance, security, and trust.

Key takeaways:

• Understand where AI creates data exposure risks by examining how generative AI tools ingest, process, and interact with enterprise information.
• Establish effective guardrails and governance frameworks to maintain control over sensitive data while enabling responsible AI adoption.
• Unlock AI-driven value with confidence through practical strategies that reduce risk, strengthen compliance, and support secure innovation at scale.

‍

As privacy laws evolve and AI adoption accelerates, organisations must prepare for new governance expectations while maintaining trust, innovation, and operational resilience.

• Prepare for emerging PDPA requirements, AI governance expectations, and evolving enforcement priorities.
• Balance responsible data sharing, cross-border data flows, and business innovation without increasing compliance risk.
• Turn regulatory change into a competitive advantage through stronger governance, accountability, and trust.

Governance, Risk, and Compliance functions face growing pressure to align with various standards and regulations. However, the diverse evidence expectations and review cycles can lead to fragmentation and heavy workloads. AI-driven approaches can now perform evidence analysis, extract relevant insights, validate control alignment, and detect gaps early, resulting in a more reliable, scalable, and high-performing GRC function that supports faster decision-making and stronger organisational resilience.

In this innovative session, attendees will be faced with a series of scenarios that they may face in their roles. Attendees will discuss the possible courses of action with their peers to consider the ramifications of each option before logging their own course of action.

‍

Results will be tallied and analysed by our session facilitator and results will impact the way the group moves through the activity.

‍

Will we collectively choose the right course of action?

‍

A case study on implementing robust encryption methods to protect sensitive data both at rest and during transmission.

Learn how organisations across sectors can integrate generative AI technologies while maintaining strong data protection and managing associated risks.

• Establishing governance frameworks and designated AI oversight roles to ensure responsible AI use.
• Implementing data classification and access controls to protect sensitive information during AI deployments.
• Collaborating with trusted AI vendors and conducting ongoing risk assessments to safeguard organisational data assets.

Select a topic of discussion and engage in an interactive roundtable discussion with a group of your like-minded peers.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights - and a travel voucher - as the top scorer takes the crown.

‍

‍

This session highlights how embedding privacy and security into organisational culture can drive customer trust and long-term business success beyond compliance.

‍

• Strategies for fostering a privacy-first mindset across all levels of an organisation.
• Integrating privacy by design in products and services to achieve regulatory and market differentiation.
• Empowering cross-functional teams to proactively identify and mitigate data protection risks.

As AI adoption accelerates, workforce, customer and operational data are increasingly repurposed for analytics and training models, blurring the line between what’s technically possible and what’s ethically acceptable. 

With growing scrutiny and regulations, leaders must balance innovation, compliance, and trust, and decide; Just because we can use the data, should we?

This interactive session puts these questions directly to the audience. Participants vote live on a series of real-world scenarios, explore the results together, and vote again as perspectives shift through the discussion.

• Can internal data be used to train AI models without explicit consent?
• What level of de-identification is “safe enough” for AI analytics?
• How transparent should organisations be about AI data use?
• Who should own decisions on AI data ethics and privacy?
• When innovation conflicts with privacy risk, what should win?

‍

Unwind with your peers for a couple of drinks on us!