Privacy is playing a bigger role in deciding how organisations deploy AI. Data use, consent and personal information concerns are now delaying, redesigning and shelving AI projects. This session explores how privacy teams assess AI solutions, who else needs to be part of that thinking beyond privacy and security, and how these functions can work together earlier.

Key takeaways:

• Privacy is shaping AI deployment, not just reviewing it after the fact.
• Rethinking ownership, security and privacy teams approach AI risk differently, and other stakeholders are often missing from the conversation entirely.
• Involving all relevant teams from the start prevents the delays, redesigns, or incidents that surface later.

TBC

When an AI security tool gives the wrong answer, we often blame the model or the prompt. But frequently, the real problem is the data. Security questions can require information from multiple systems that use different names, identifiers and formats, connections that document retrieval cannot reliably make.

This session explores how to build a practical security knowledge graph, including the data sources, entity matching, early schema mistakes and time involved. It then compares the graph with document retrieval using the same questions, model and prompts showing where the graph improved results, where it made no difference and where it performed worse.

The goal is not a complex enterprise ontology, but the smallest useful graph that can answer real security questions and clear evidence of whether it was worth building.

Key takeaways:

• Why document retrieval struggles with questions that require connecting multiple systems.
• Why matching the same assets across inconsistent data sources is the hardest part.
• How to compare retrieval and graph-based approaches fairly, including where the graph falls short.

AI security issues are much harder to fix once an application is built. This session explores how to threat model AI applications early, looking beyond the model to data flows, integrations, orchestration and access to find risks before they become embedded.

Key takeaways:

• Threat model early, identify risks before they become expensive to fix.
• Look beyond the model, assess data, integrations, orchestration and access.
• Focus on real risk, prioritise the areas most likely to create meaningful exposure.

As Generative AI, copilots, and AI agents move from experimentation into everyday business operations, the challenge is no longer whether to adopt AI, but how to scale it responsibly and with confidence.

This keynote explores how effective AI governance can enable innovation while managing risks across privacy, data, intellectual property, cyber security, bias, compliance, and responsible use. It also examines the emerging governance challenges of AI agents and autonomous systems, where AI increasingly acts and makes decisions on behalf of organisations.

What we’ll cover:

• Why AI governance is now a board-level priority
• The key building blocks of effective AI governance
• How to scale AI safely without slowing innovation
• How to govern AI agents and autonomous systems
• How to establish clear accountability for AI decisions and outcomes
• The roles of cyber security, risk, legal, HR, technology, and business leaders

The organisations that gain the most from AI will be those that can scale it with confidence through effective governance, clear accountability, and trust.

AI security programs have moved past the whiteboard. Most large organisations now have something running. Some of it is working, some of it isn't, and most leaders are quietly rebuilding parts of what they started with. This panel brings together senior leaders to compare notes on what they've actually deployed, what they've abandoned, and what they wish they'd done sooner.

We'll cover:

• The controls we kept: What's earned its place in the program after 12 to 18 months in the wild
• The controls we dropped: The ideas that looked good on paper and didn't survive contact with the business
• What changes next: Where teams are tightening ownership, testing and accountability in 2026 and 2027

As organisations rush to deploy generative AI features, copilots and agents, traditional application security testing is failing to catch many of the risks they introduce.

In this session, Srinivas Karthik Putlur from the Commonwealth Bank shares lessons from penetration testing, code reviews and architecture assessments of real AI systems. He'll explore the vulnerabilities that appear most often from prompt injection and excessive agency to insecure retrieval pipelines and MCP-related risks, and the practical controls that help address them.

Key takeaways:

• Why traditional AppSec testing misses AI-specific risks
• The most common vulnerabilities found in real AI applications
• How to secure AI systems without slowing development

Choose one roundtable topic to join on the day. Each table is hosted by a peer practitioner and runs as a structured discussion. No slides. No vendor pitches. Just senior leaders comparing notes on what they're doing about the problems on the agenda.

We'll cover:

1. Securing AI agents and copilots in production
2. Guardrails and testing: what is actually holding up in production
3. Shadow AI: where policy is breaking in the real business
4. Securing AI-generated code at developer speed
5. Third-party AI risk: vendor features on by default
6. Identity and access for AI agents
7. Building an AI security program when AI is already in use
8. AI incident response: when the system doesn't behave the same way twice

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