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

The hardest data security problem rightnow is not protecting the databases you know about. It is finding every placeAI has quietly copied what was inside them.

RAG pipelines, copilots, vector stores,evaluation sets, and AI-powered SaaS all duplicate, cache, and log sensitivedata faster than most teams can map it. Much of that data lands in systemstraditional security programs were never built to inspect.

This keynote is a practical look at how teams regain visibility across an AI-enabled environment. Where sensitive data turns up, which copies are hardest to pin down, and how to reduce the blindspots before they become part of the next breach.

We’ll cover

• Where sensitive data now hides, from vector stores and prompt logs to evaluation sets and SaaS AI features
• How to discover and classify AI-created copies without slowing adoption
• Practical ways to remove, restrict, or protect exposed data before it becomes an incident

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.

Finding sensitive data is no longer enough. The harder question is whether you can prove it is governed.

Boards, auditors, and regulators want evidence. Who owns the data, who can reach it, how long it is kept, whether access is reviewed, and whether deletion really happened. In New York, this ismoving from an operational expectation to signed accountability. Since November1, 2025, DFS-covered entities have needed written policies for maintaining an inventory of their information systems, while annual Part 500 submissions aresigned by the highest-ranking executive and the CISO.

This panel gets into what defensible data governance actually looks like, and why programs that stop at discovery often fall apart when someone asks for proof.

We’ll discuss:

• Moving from periodic discovery to continuous, evidence-based governance
• Proving retention, access reviews, and deletion are happening across cloud, legacy, and AI environments
• What senior leaders need to know before they put their names behind the organization’s controls

Once AI can retrieve data, call tools, and take actions, access control stops being only about people.

Agents, copilots, and internal AI platforms often inherit broad permissions from the systems around them. That creates a new problem for security teams: controlling not only who can access sensitive data, but what an AI system can see, combine, and act on once it has that access.

This session looks at the guardrails that hold up when AI moves into daily use. How teams control agent identities, limit permissions, and keep humans involved when an AI system is allowed to do more than generate an answer.

We’ll cover:

• The gateways, runtime controls, and approval points teams are putting around enterprise AI
• Controlling the data, tools, and actions available to agents, copilots, and internal platforms
• Keeping agent identities, permissions, and human sign-off under control as AI becomes more autonomous

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?

Discover how one organization strengthened its data protection posture by implementing encryption across critical systems without compromising performance, usability, or business operations.

• Embed encryption into applications, cloud environments, and data storage to protect sensitive information.
• Balance strong security controls with operational efficiency and user experience.
• Overcome implementation challenges while meeting compliance and regulatory requirements.

Remote work has transformed how organizations share and access information, but it's also created new opportunities for data loss. Learn how one organisation strengthened its DLP strategy to protect sensitive data without slowing down its workforce.

• Protect sensitive data across endpoints, cloud applications, and collaboration platforms.
• Reduce insider risk with intelligent monitoring, automation, and user education.
• Balance strong security controls with the flexibility employees expect.

Privacy and security protect the same data, but they do not start from the same question.

Privacy asks whether data should be collected, retained, or reused at all. Security asks who can reach it and how it should be protected. Once AI starts combining and repurposing enterprise data in ways nobody imagined when it was collected, those decisions can no longer be made in separate rooms.

This panel brings privacy and security leaders together to compare how they divide the work, where responsibilities overlap, and what happens when the two functions reach different conclusions.

We’ll discuss:

• How privacy and security divide ownership of minimization, retention, and deletion
• Preparing for new state privacy and AI rules without rebuilding controls market by market
• Telling one defensible story about your data practices to regulators, boards, and customers

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.

Better discovery creates a new problem:too many findings.

Large organizations can surface millionsof sensitive records, thousands of exposed data stores, and years of redundantcopies. The real work is deciding which findings create meaningful businessrisk, which can be accepted, and which need an immediate control around them.

This keynote gets into how teams movefrom classifying everything to prioritizing what deserves action, then matchingthe response to the risk. Fix it, restrict it, encrypt it, tokenize it, ordelete it.

We’ll cover:

• Deciding what to fix, restrict,encrypt, or delete first across cloud, AI, and legacy systems
• Separating exposures that createreal business risk from lower-priority findings
• Where encryption, tokenization,and masking earn their place, and where they are the wrong tool

Sending data to a vendor does not send the accountability with it.

Cloud platforms, AI providers, and specialist SaaS tools now process some of the organization’s most sensitive information. The challenge is proving where that data goes, who can access it, whether it is being used to train models, and whether retention and deletion commitments are happening outside the contract.

This keynote gets into what actually holds vendors accountable, from diligence and contractual controls to technical verification and ongoing monitoring. It also looks at how teams keep pace with NYDFS, SEC, state privacy, and global requirements without turning every new vendor into a six-month approval process.

We’ll cover:

• Verifying where vendors keep your data, who can access it, and whether it can be used to train models
• Staying accountable after the data leaves the building through diligence, contracts, and continuous monitoring
• Meeting US and global requirements as they change without stalling the business

Most incident response plans assume you can trace what data was involved. AI breaks that assumption.

Sensitive information may now sit in prompt histories, vector stores, evaluation sets, temporary caches, and third-party AI services that were never included in the original data map. When an incident happens, the first and hardest question becomes what was actually exposed.

This think tank brings security, privacy, legal, and data protection engineering leaders together to work through how breach response changes when AI is involved. In New York, that can mean reconciling the SHIELD Act with NYDFS and SEC requirements, while determining whether encryption held and whether the keys were also compromised.

We’ll discuss:

• Scoping what was exposed when data has been copied far beyond production systems
• Making disclosure calls across NYDFS’s 72-hour requirement, the SEC’s four-business-day deadline once a public company determines an incident is material, the SHIELD Act, and other state regimes
• How encryption, tokenization, and masking change the real exposure, and how to prove those controls held after an incident

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

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