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.

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• 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.

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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.

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

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Will we collectively choose the right course of action?

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Data quality is often treated as a governance or compliance requirement, but its impact extends far beyond regulatory obligations. This session will provide a practical and accessible introduction to data quality, exploring how regulatory expectations have evolved and why getting data right is increasingly critical to business resilience, decision-making and security.

Using real-world examples, the session will examine what happens when poor-quality data becomes a business risk, from inaccurate reporting and ineffective controls through to operational and customer impacts. It will also explore how the rapid adoption of AI is raising the stakes for data quality, while creating new opportunities to use AI and machine learning to identify, manage and improve data quality at scale.

• How data quality has evolved from a regulatory obligation into a critical organisational capability, and what “good” data quality looks like today.
• How poor-quality data creates risks across operations, customers, compliance, and decision-making, extending far beyond the data and technology teams.
• How AI is raising the stakes for data quality while also creating new opportunities to identify, monitor, and improve data quality at scale.  

As employees turn to GenAI tools to work faster, sensitive information can easily end up in the wrong place. This practical session explores how Forcepoint gained visibility over GenAI usage, identified data leakage risks and introduced safeguards without putting the brakes on innovation.

We'll cover:

• How to identify what employees are sharing with public GenAI tools, copilots and AI-powered applications, and where sensitive data is most at risk.
• Practical approaches to data classification, access controls, DLP and AI governance that reduce leakage without creating unnecessary friction for users.
• How to give employees the confidence and flexibility to use GenAI safely while maintaining control over sensitive and regulated information.

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.

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Security operations are under growing pressure to detect and respond to threats faster than ever. This keynote explores how AI-powered SOCs and automated incident response are transforming security from a reactive function into a proactive, intelligent and resilient capability.

We'll explore:

• How AI can analyse vast volumes of security data, identify meaningful threats and reduce alert fatigue for security teams.
• Using AI and automation to accelerate investigation, prioritisation and incident response—reducing the time between identifying a threat and containing it.
• What it takes to combine AI, automation and human expertise to create security operations that continuously learn, adapt and stay ahead of emerging threats.

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?

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