Agents are now infrastructure, not a pilot. The security industry's instinct is to put a chokepoint on the network. That instinct is thirty years old and it no longer reaches the place where agents actually act: the harness. Turn on the lights today as the first step towards a safe AI Agent Lifecycle.

As financial institutions race to move AI-powered applications from concept to production, the biggest challenge is no longer building the technology, it is deploying it securely, responsibly, and at enterprise scale. Success requires far more than development and testing. It demands a governance strategy that is embedded throughout the entire application lifecycle.

In this session, Venkata Cheedella will provide a 360-degree view of what it takes to take modern applications, including AI-enabled systems, safely into production. From architecture and secure development to model governance, compliance, third-party risk, operational resilience, and continuous monitoring, attendees will explore where security, risk, and governance intersect with every stage of delivery. The discussion will also examine how cross-functional collaboration between engineering, security, risk, legal, and business leaders enables organizations to accelerate innovation without sacrificing trust or regulatory expectations.

Designed for cybersecurity executives and practitioners in financial services, this session will provide practical insights into building governance into the development pipeline, reducing production risk, and creating an application delivery process that is both agile and resilient in an AI-driven world.

Every cybercrime investigation generates valuable human insight, but too often that knowledge remains trapped in case notes, individual experience, or disconnected workflows. The next evolution of AI is not simply automating investigations; it is capturing human expertise, transforming it into structured data, and creating continuous feedback loops that make every investigation smarter than the last.

In this session, Abhishek Mittal of AML RightSource will explore how financial institutions can convert investigator knowledge into actionable data streams that continuously improve detection, decision-making, and operational effectiveness. Attendees will learn how meaningful metrics, investigator feedback, and AI can work together to identify emerging threats, refine investigative models, and accelerate the fight against financial crime. The discussion will examine practical approaches to building intelligence-driven feedback loops that enable organizations to learn from every case, adapt more quickly to evolving criminal tactics, and strengthen both operational resilience and fraud prevention. Participants will leave with a framework for transforming human expertise into a scalable, data-driven advantage in the ongoing battle against cyber-enabled financial crime.

Agentic AI is moving security operations beyond AI-assisted analysis toward systems capable of investigating threats, invoking tools, and taking action. For CISOs, this creates a fundamental governance challenge: how can organizations trust autonomous systems that may access sensitive data, operate with delegated authority, and act at machine speed?

In this session, Fortra Chief Product Officer Aaron Cockerill will examine how security leaders can build confidence in autonomous security operations through continuous adversarial testing. Drawing on Fortra’s decades of threat research, offensive security expertise, and experience protecting sensitive data across complex digital environments, he will outline how organizations can evaluate AI permissions, data-access pathways, tool use, decision logic, escalation controls, and human oversight—both before deployment and as these systems evolve.

Aaron will address both sides of the challenge: using AI to strengthen cyber defense while applying offensive techniques to identify weaknesses in AI-enabled systems before threat actors do. He’ll also discuss how the security analyst’s role is changing as AI assumes more investigative and response responsibilities.

Ultimately, trusted autonomy depends on more than model accuracy. It requires clear boundaries around what AI systems can access and do, continuous validation against adversarial behavior, and accountability that remains visible when decisions are made at machine speed.

For financial services leaders, the conversation has shifted from whether to adopt AI to how to scale it securely, govern it effectively, and prepare for what's next. As AI becomes deeply embedded across security operations, fraud detection, customer experiences, software development, and enterprise decision-making, the next wave of innovation is already taking shape.

This executive panel brings together experienced cybersecurity and technology leaders to examine the future of AI beyond today's deployments. The discussion will explore the rise of agentic AI, autonomous security operations, machine identities, AI-to-AI interactions, evolving regulatory expectations, and the new trust models required as intelligent systems gain greater autonomy. Panelists will also discuss how attackers are leveraging AI, where defensive capabilities are headed, and what leadership teams should be doing now to prepare for the next generation of AI-enabled financial services.

Attendees will gain practical insights into the strategic decisions, governance models, and security investments that will position their organizations to confidently navigate the next chapter of AI innovation

As AI becomes embedded across financial services, governance can no longer exist only as a framework on paper. Financial institutions are being challenged to operationalize AI governance across business units, technology teams, security, risk, compliance, and executive leadership, while keeping pace with rapidly evolving regulations and innovation.

In this panel, senior leaders will share how their organizations are turning governance strategies into measurable, enterprise-wide practices. From defining ownership and accountability to strengthening model oversight, managing third-party AI risk, and delivering meaningful board reporting, panelists will discuss what it takes to build governance programs that enable innovation while maintaining trust, resilience, and regulatory confidence. Attendees will leave with practical insights into creating governance that scales with AI adoption, not behind it.

Every financial institution is facing the same question: Should we build our own AI capabilities, buy commercial solutions, or pursue a hybrid approach? The answer has significant implications for security, governance, cost, innovation, and long-term competitive advantage.

In this panel, financial services executives will examine the strategic tradeoffs behind today's AI investment decisions. Panelists will discuss how they evaluate vendor risk, balance the use of proprietary and open-source models, measure return on investment, and address the governance and security challenges that come with each approach. Attendees will gain practical insights into the decision-making frameworks leading organizations are using to adopt AI responsibly while aligning technology investments with business objectives.

As financial institutions accelerate the adoption of AI, the opportunity to transform fraud detection, financial crime compliance, risk management, and customer experiences is significant. But so are the governance and regulatory challenges. Financial services organizations must determine how to innovate with AI while maintaining the transparency, control, and accountability expected in one of the world’s most highly regulated industries.

In this session, Manish Andhy, Financial Services AI Executive at Teradata, will explore the evolving risk and compliance landscape surrounding AI in financial services. He will examine how institutions can address issues such as model risk, data governance, explainability, regulatory oversight, and the emerging complexities of generative and agentic AI. Attendees will gain practical perspectives on building governance and compliance into AI initiatives from the outset, enabling organizations to move AI into production responsibly while maintaining the trust of regulators, customers, and the business.

Threat-led penetration testing has become one of the most powerful tools for assessing the real-world resilience of financial institutions, but the stakes are uniquely high in live FS environments. Testing must be realistic enough to expose genuine weaknesses, yet carefully controlled to avoid customer impact, market disruption, data loss, or operational instability.

This discussion explores how financial institutions can work with their expert cyber service providers to safely conduct high-risk, threat-led testing against critical systems, including payment platforms, trading infrastructure, cloud environments, identity systems, and third-party dependencies. Speakers will discuss how to balance realism with control, how to define acceptable risk, and how to ensure lessons from testing translate into lasting resilience improvements.

Key Discussion Questions
•    How can financial institutions work with partners to design threat-led penetration tests that are realistic enough to be valuable but controlled enough to avoid operational disruption?
•    What governance, rules of engagement, and escalation mechanisms are essential when testing critical or systemically important financial services infrastructure?
•    How should firms, regulators, and testing providers determine whether the greater risk lies in testing too aggressively or not testing realistically enough?

Your business continuity plan was built for yesterday's threats. Learn how AI-driven resilience helps CISOs respond faster, decide smarter and stay in control when a cyber crisis hits.

Attackers have already adopted AI. They scan, phish and move laterally at machine speed, while many organizations still run their crisis response from a binder, a spreadsheet and a group chat. When an incident escalates into a business crisis, the gaps show fast: unclear roles, delayed coordination between teams, scattered decisions and leadership without a real-time picture of what is happening.

This session explores what changes when AI fights AI, and how security leaders can move from static continuity documents to a living resilience capability that works before, during and after a crisis.

The number of AI pundits and experts seems to keep expanding.  How do you know what is happening in AI with any certainty?  Come to this session to hear a practitioner talk about where we are with current technology and where we are going.  Given his nearly 4 decades of experience in building AI environments, applications, and platforms, John will talk about what works and what doesn’t.  With clear guidance, you can place your bets and make commitments that you know will pay off.  Bring your questions and be ready to discuss your challenges and concerns about AI.

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As artificial intelligence becomes embedded across financial services, organizations must establish clear governance structures to ensure AI is deployed responsibly, securely, and in compliance with evolving regulations. This summit track will explore how cybersecurity, risk, compliance, and business leaders can build effective frameworks that balance innovation with accountability, transparency, and operational resilience.

Attendees will learn how leading financial institutions are creating AI governance programs that include policies, oversight committees, model inventories, risk assessments, and clear lines of accountability. Experts will also examine the growing regulatory landscape—from SEC and OCC guidance to the EU AI Act and NIST AI Risk Management Framework—and provide practical guidance for managing third-party AI risk across cloud, fintech, and generative AI providers.

Key Takeaways:

• Best practices for building enterprise AI governance frameworks and operating models.
• Strategies for aligning AI initiatives with regulatory requirements and risk management objectives.
• Approaches for managing AI model inventories, lifecycle governance, and accountability structures.
• Guidance for evaluating and monitoring third-party AI vendors and technology partners.
• Real-world examples of balancing innovation, compliance, and operational resilience.
• Actionable insights for developing responsible and trustworthy AI programs.

As AI capabilities become more powerful and accessible, attackers are increasingly targeting the models, data, and infrastructure that power them. This summit track will focus on the emerging threats facing AI systems and the security controls financial institutions need to deploy AI safely and confidently.

From adversarial attacks and model manipulation to prompt injection and AI-powered fraud, experts will share practical strategies for protecting AI applications throughout the development and deployment lifecycle. Attendees will explore secure architectures for generative AI, learn how to implement effective guardrails and access controls, and discover how AI red teaming can uncover vulnerabilities before they become business risks.

Key Takeaways:

• Understanding emerging threats targeting AI models and AI-enabled applications.
• Strategies for mitigating prompt injection, model poisoning, data poisoning, and evasion attacks.
• Best practices for securing generative AI deployments and large language model integrations.
• Guidance for implementing secure RAG architectures, monitoring, and access controls.
• Practical approaches to AI red teaming, validation, and security testing.
• Insights into defending against AI-driven fraud, abuse, and manipulation in financial environments.

Data is the foundation of every successful AI initiative, but it also represents one of the greatest sources of risk. This summit track will explore how financial institutions can protect sensitive information while enabling AI-driven innovation, analytics, and automation.

Industry leaders will discuss strategies for safeguarding customer information, transaction data, and proprietary business assets across AI workflows. Sessions will cover privacy-preserving technologies, secure data-sharing models, and governance practices that help organizations maintain compliance, transparency, and trust. Attendees will also learn how to address growing challenges related to data classification, retention, lineage, and oversight in increasingly complex AI ecosystems.

Key Takeaways:

• Best practices for protecting sensitive financial data used by AI systems.
• Strategies for managing PII, customer records, transaction data, and confidential business information.
• Insights into privacy-preserving AI techniques, including federated learning and differential privacy.
• Approaches for enabling secure data sharing while maintaining regulatory compliance.
• Guidance for strengthening data governance through classification, retention, lineage, and monitoring.
• Practical recommendations for reducing data security and privacy risks across AI-powered operations.