Modern threat actors and criminals have embraced the power of technology have incorporated the use of advance computing into their operational tradecraft.  The tactics used to engage in criminal activity such as illegal drug and human trafficking, intelligence collection and operations, as well as terrorist attacks have evolved as threat actors increasingly use AI obtain operational intelligence on law enforcement and security operations and to plan and conduct attacks or other illicit activities. These same threat actors operate using converged and cohesive integration of physical activities, cyber security exploits, and algorithmically enabled malign information operations.  Operational activity to detect, evaluate and mitigate emerging cyber and physical threats are enhanced when cyber and physical efforts are integrated and the tradecraft used by federal, state and local authorities to counter-act emerging threats and crime are evolving. 

This session will discuss this issue from the perspective of an individual who has and continues to work with law enforcement, intelligence, and cyber security professionals to protect our communities and Nation from foreign and domestic threats.  

Key Takeaways

1. The Evolution of AI-Enabled Crime: Understand how criminals, terrorist organizations, and other threat actors are leveraging artificial intelligence and advanced technologies to plan, coordinate, and execute increasingly sophisticated operations.

2. The Convergence of Cyber and Physical Threats: Explore how adversaries combine cyberattacks, physical operations, and AI-driven disinformation to exploit vulnerabilities and challenge traditional security and law enforcement approaches.

3. Rethinking Law Enforcement and Threat Intelligence: Discover why traditional investigative and security methods must evolve to address AI-enabled threats, and how intelligence sharing and integrated operations can improve threat detection and response.

4. Building a Unified Defense Against Emerging Threats: Learn how collaboration among cybersecurity professionals, law enforcement, intelligence agencies, and private-sector organizations can strengthen national security, protect communities, and stay ahead of evolving criminal tactics.

As financial institutions accelerate the adoption of agentic AI, autonomous agents are moving beyond experimentation and becoming part of critical enterprise infrastructure. These agents access sensitive data, interact with systems, and execute actions independently, introducing new security challenges that traditional network-level controls alone cannot address.

Securing agentic AI requires organizations to look beyond conventional security checkpoints and focus on where agents actually operate: the agent harness, which connects AI models to tools, data, permissions, and workflows. In this session, Justin Dolly, Chief Customer and Security Officer at Ory, explores why defense-in-depth strategies must extend into the harness to provide greater visibility, control, and accountability. Attendees will learn practical approaches to managing agent identities, monitoring autonomous actions, and building a secure AI agent lifecycle that enables financial institutions to innovate while reducing risk.

Key Takeaways

1. AI Agents as Critical Infrastructure: Understand the security implications of moving autonomous agents into production environments.

2. Beyond Traditional Security Controls: Discover why network-level security alone is insufficient and why the agent harness is a critical control point.

3. Identity, Access, and Accountability: Explore how to manage agent permissions, monitor activities, and enforce security policies.

4. Securing the AI Agent Lifecycle: Learn practical steps to improve visibility, strengthen governance, and safely scale agentic AI in financial services.

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.

Artificial intelligence is rapidly transforming financial services, from fraud detection and payment processing to customer experiences and enterprise operations. But as financial institutions move beyond AI experimentation and toward enterprise-wide adoption, the challenge is no longer simply what AI can do. It's how to deploy it securely, responsibly, and at scale.

In this panel discussion, Ari Kaufmann and fellow leaders from Mastercard will explore what it takes to turn AI ambition into operational reality. From building secure architectures and protecting sensitive financial data to managing AI-driven threats, governing autonomous systems, and maintaining regulatory compliance, the conversation will examine the real-world challenges of securing AI in one of the world's most highly regulated industries.

Panelists will share practical perspectives on balancing innovation with security, integrating AI into existing infrastructure, and building the resilience and trust necessary to support the next generation of financial services.

Key Takeaways

• Moving from AI Pilots to Production: Understanding the security challenges financial institutions face when scaling AI across critical business operations.

• Building Security Into AI Architecture: Establishing the infrastructure, identity controls, data protections, and security practices necessary for enterprise AI deployment.

• Managing Emerging AI Risks: Addressing new attack surfaces, autonomous AI agents, third-party dependencies, and evolving cyber threats.

• Balancing Innovation, Governance, and Compliance: Enabling AI adoption while maintaining regulatory readiness, accountability, and effective risk oversight.

• Turning AI Strategy Into Action: Identifying practical steps security and technology leaders can take today to build secure, scalable, and resilient AI capabilities.

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.

Key Takeaways

1. The Next Wave of AI Innovation: Explore how agentic AI, autonomous security operations, and AI-to-AI interactions will reshape financial services and create new opportunities and risks.

2. Securing an Increasingly Autonomous Enterprise: Understand the challenges of managing machine identities, delegated authority, and evolving trust models as AI systems gain greater independence.

3. Staying Ahead of AI-Driven Threats and Regulations: Examine how cybercriminals are leveraging AI, how defensive capabilities are evolving, and what changing regulatory expectations mean for financial institutions.

4. Preparing Today for Tomorrow's AI Landscape: Discover the strategic investments, governance frameworks, and leadership decisions needed to securely scale AI and build long-term resilience.

Microsoft Copilot and AI depend on grounding data across M365, Azure, and SaaS. How that data is accessed, governed, and secured is becoming more critical. Cyberattacks, compromised identities, or overprivileged agents threaten that data and business continuity. Explore why data resilience must evolve for this new threat landscape and what organizations can do to better protect the data that powers AI.

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?

AI is only as powerful as the data behind it. As financial institutions accelerate their adoption of artificial intelligence, the ability to deliver trusted, accessible, secure, and well-governed data has become a critical competitive advantage. Yet many organizations continue to struggle with fragmented data environments, legacy infrastructure, inconsistent data quality, and governance challenges that limit their ability to move AI initiatives from experimentation into production.

Building an AI-ready enterprise requires more than modernizing technology. It demands a strategic approach to how data is collected, managed, connected, protected, and made available to AI-powered applications and autonomous agents. Financial institutions must balance the need for greater data accessibility with strict requirements around security, privacy, regulatory compliance, and responsible AI use.

In this session, Deepak Gaikwad, SVP, Head of Data & AI Products at PNC, explores the essential building blocks of an AI-ready data strategy. From breaking down data silos and strengthening data governance to enabling real-time intelligence and supporting agentic AI workflows, attendees will gain practical insights into creating a scalable data foundation that drives innovation, improves decision-making, and delivers measurable business value.

Key Takeaways

1. Building the Foundation for AI Success: Understand why trusted, high-quality, accessible data is essential to scaling AI initiatives across financial services.

2. Breaking Down Data Silos and Legacy Barriers: Explore strategies for modernizing data architectures, improving interoperability, and making enterprise data more accessible to AI-powered applications.

3. Balancing Data Governance, Security, and Innovation: Learn how financial institutions can strengthen data quality, privacy, and regulatory compliance while enabling responsible AI adoption.

4. Preparing Data for the Agentic AI Era: Discover how an AI-ready data strategy can support autonomous workflows, improve enterprise decision-making, and deliver measurable business outcomes.

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.

Key Takeaways

1. Build, Buy, or Go Hybrid: Understand the strategic tradeoffs of developing AI internally, purchasing commercial solutions, or combining both approaches to meet business and security objectives.

2. Managing AI Vendor and Third-Party Risk: Explore how financial institutions evaluate AI providers, assess security vulnerabilities, and address governance, compliance, and data protection concerns.

3. Balancing Innovation, Cost, and ROI: Learn how organizations measure the value of AI investments while weighing proprietary versus open-source models, scalability, and long-term costs.

4. Building a Sustainable AI Strategy: Discover the governance frameworks, security controls, and decision-making approaches needed to align AI investments with business priorities and long-term competitive advantage.

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.

Key Takeaways

1. Turning Human Expertise into Actionable Intelligence: Learn how to capture investigator knowledge and transform case insights into structured data that strengthens financial crime detection and prevention.

2. Building Continuous AI Feedback Loops: Explore how investigator feedback, AI, and data-driven insights can continuously improve investigative models, accuracy, and decision-making.

3. Measuring What Matters in Financial Crime Investigations: Understand how meaningful metrics and performance indicators can help organizations identify emerging threats, improve efficiency, and measure investigative effectiveness.

4. Scaling Intelligence to Stay Ahead of Financial Crime: Discover how financial institutions can leverage human expertise and AI to adapt to evolving criminal tactics, accelerate investigations, and strengthen operational resilience.

As artificial intelligence reshapes financial services, leaders face a growing challenge: how to accelerate innovation while protecting trust, managing risk, and maintaining accountability. Success requires more than the right technology. It demands clear leadership, informed decision-making, and collaboration across cybersecurity, technology, risk, and business teams.

In this fireside chat, Joyce Brocaglia, Founder and CEO of the Executive Women’s Forum (EWF), and Suzanne Hall, EWF CISO in Residence and seasoned technology and cybersecurity executive, will explore the leadership perspectives shaping responsible AI adoption. Drawing on their experience across cybersecurity, enterprise leadership, and the EWF community, they will discuss how organizations can balance AI-driven opportunity with security and governance priorities, prepare leaders for an evolving risk landscape, and build the cross-functional trust needed to move from AI experimentation to meaningful business impact.

Attendees will gain practical perspectives on the leadership questions organizations should be asking now to advance AI with confidence, strengthen resilience, and ensure innovation delivers lasting value.

AI is changing cybersecurity faster than organizations can prepare their people to use it. But access to powerful AI tools doesn't guarantee better security outcomes. Drawing on Hack The Box's NeuroGrid research, this session explores the emerging divide between teams that simply deploy AI and those that know how to direct it. From automation bias and skill atrophy to the growing challenge of trusting autonomous agents, Tom Fernandez examines why human judgment is becoming a critical measure of cyber readiness. For financial institutions, the next competitive advantage won't come from deploying more AI. It will come from proving that both humans and agents are competent, accountable, and ready to perform together.

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.

Key Takeaways

1. Navigating the Evolving AI Regulatory Landscape: Understand how emerging regulations and compliance expectations are shaping AI adoption across financial services.

2. Managing AI Risk Across the Enterprise: Explore strategies for addressing model risk, data governance, explainability, and the unique challenges introduced by generative and agentic AI.

3. Building Governance into AI Innovation: Learn how financial institutions can embed security, compliance, transparency, and accountability throughout the AI lifecycle, from development to deployment.

4. Scaling AI Responsibly While Maintaining Trust: Discover how organizations can balance innovation with regulatory requirements, strengthen oversight, and confidently move AI initiatives into production while delivering measurable business value.

AI didn't enter your institution through architecture review. It arrived through business units, vendor product updates, and browser tabs and most firms still can't name the AI systems they're running. Meanwhile the EU AI Act, ISO/IEC 42001, NIST's AI RMF, and existing model risk expectations are converging on the same reporting cycle.

This session cuts through the framework noise and lays out a governance program you could stand up this quarter. We'll cover what's genuinely new about AI risk versus what your existing third-party, data, and change controls already handle; the controls that hold across the AI lifecycle from intake to operation; who needs decision rights and who accepts residual risk; and a sequenced first 90 days that produces board-ready evidence at each stage.

Key Takeaways

1. Gaining Visibility into Enterprise AI: Learn how to identify and inventory AI systems across business units, third-party vendors, and everyday workflows to uncover hidden risks.

2. Cutting Through AI Governance Complexity: Understand how to align emerging regulations and frameworks with existing security, risk, and compliance controls without creating unnecessary complexity.

3. Establishing Accountability Across the AI Lifecycle: Explore how to define decision-making authority, assign risk ownership, and implement practical controls from AI adoption through ongoing operations.

4. Building a 90-Day AI Governance Action Plan: Discover how to launch a measurable governance program that delivers quick wins, strengthens regulatory readiness, and produces board-ready evidence of progress.

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.

Key Takeaways

1. Turning AI Governance into Action: Learn how financial institutions are moving beyond policies and frameworks to implement practical, measurable governance across the enterprise.

2. Establishing Ownership and Accountability: Explore how organizations define roles, responsibilities, and oversight across security, risk, compliance, technology, and business leadership.

3. Managing AI Risk at Scale: Understand how to strengthen model oversight, assess third-party AI risks, and maintain compliance as AI adoption accelerates.

4. Building Board Confidence and Regulatory Readiness: Discover how to measure governance effectiveness, communicate AI risks to executive leadership, and build programs that balance innovation, trust, and resilience.

Agentic AI is fundamentally changing how financial institutions operate, introducing a new generation of autonomous systems capable of making decisions, accessing sensitive data, and executing complex tasks with limited human intervention. While these capabilities offer tremendous opportunities to improve efficiency, strengthen security operations, and accelerate innovation, they also introduce risks that traditional cybersecurity programs were never designed to address.

As AI agents become increasingly embedded across the enterprise, security leaders must rethink how they approach identity and access management, governance, threat detection, accountability, and operational resilience. The challenge is no longer simply securing the technology, but understanding how to maintain visibility, control, and trust when intelligent systems can act independently and at machine speed.

In this session, Susan Koski, Former CISO of PNC, explores what it takes to reinvent the modern security program for the age of agentic AI. Drawing on her experience in financial services cybersecurity leadership, she will examine how CISOs can adapt existing security frameworks, establish effective guardrails for autonomous systems, and balance innovation with risk management. Attendees will gain practical insights into building a more resilient, adaptable, and AI-ready security program that can keep pace with rapidly evolving technology and business demands.

Key Takeaways

1. Rethinking Cybersecurity for the Agentic AI Era: Understand why traditional security programs must evolve as autonomous AI systems take on greater responsibilities and introduce new enterprise risks.

2. Securing AI Identities, Access, and Autonomy: Explore how financial institutions can manage AI agent permissions, enforce appropriate controls, and maintain visibility into autonomous actions.

3. Building Governance and Accountability into AI Operations: Learn how CISOs can establish effective oversight, define risk ownership, and implement guardrails that balance AI innovation with security and compliance.

4. Creating a Resilient, AI-Ready Security Program: Discover practical strategies for modernizing security operations, adapting existing frameworks, and preparing cybersecurity teams for a future where humans and AI agents work side by side.

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.

Key Takeaways

1. Rethinking Business Continuity in the Age of AI: Understand why traditional crisis response plans are no longer sufficient against AI-powered cyber threats that move at machine speed.

2. Fighting AI with AI: Explore how AI-driven capabilities can accelerate threat detection, improve incident response, and help security teams stay ahead of increasingly sophisticated attackers.

3. Improving Crisis Coordination and Decision-Making: Learn how AI can provide real-time visibility, streamline communication, and strengthen coordination between security teams, business leaders, and key stakeholders during a cyber crisis.

4. Building a Living Cyber Resilience Strategy: Discover how to move beyond static continuity plans to a more adaptive, intelligence-driven approach that strengthens organizational preparedness, response, and recovery.

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.

Key Takeaways

1. Embedding Governance Across the AI Application Lifecycle: Understand how to integrate security, risk management, and compliance from initial development through deployment and ongoing operations.

2. Moving AI Applications Safely into Production: Explore strategies for addressing model risk, third-party vulnerabilities, secure architecture, and operational resilience while accelerating AI innovation.

3. Breaking Down Silos Between Technology, Security, and Risk: Learn how cross-functional collaboration can strengthen accountability, streamline governance, and reduce friction in application development and deployment.

4. Building Secure, Scalable, and Resilient AI Applications: Discover practical approaches to continuous monitoring, risk assessment, and governance that enable financial institutions to innovate confidently while meeting regulatory expectations.

Humans want access. AI agents want access. Banks can't tell the difference, so many are blocking agents outright. But agents are already inside customer accounts, and attackers are using them too. So: Who are you? Can we trust you? What should you access, and for how long? We'll show how to verify human and AI identities, govern access across the lifecycle, and shut down a rogue agent instantly, because in the age of AI, "trust me" really isn't a strategy.

As AI becomes more deeply embedded in security operations, leaders face a critical challenge: how to realize its potential while maintaining trust, oversight and accountability. In this lightning talk, Embed Security shares how a global IT and security provider is transforming its SOC by scaling expert security reasoning with AI, dramatically reducing analyst workload while improving operational effectiveness.

We'll explore what this shift means for the future of security operations and why accuracy, consistency and transparency are essential to adopting AI with confidence.

Key Takeaways

1. Scaling Security Expertise with AI: Discover how AI-driven security reasoning can reduce analyst workloads and improve SOC efficiency.

2. Building Trust in AI-Driven Decisions: Understand why accuracy, consistency, transparency, and human oversight are essential to adopting AI in security operations.

3. Transforming the Modern SOC: Explore how AI can strengthen threat detection, accelerate decision-making, and improve operational effectiveness.

Artificial intelligence is advancing at a remarkable pace, but separating genuine breakthroughs from industry hype has never been more challenging. With new models, autonomous agents, and bold predictions emerging almost daily, business and technology leaders face a critical question: Where is AI really headed, and what should organizations be doing today to prepare for the next five years?

In this closing keynote, John K. Thompson, author of The Path to AGI, draws on nearly four decades of experience building AI environments, applications, and platforms to deliver a practical, real-world perspective on the current state and future direction of artificial intelligence. He will explore what today's AI can realistically accomplish, where the technology still falls short, and how emerging capabilities such as agentic AI and increasingly autonomous systems could reshape business, cybersecurity, and enterprise operations.

Moving beyond the hype, John will examine the opportunities, risks, and strategic decisions organizations must navigate as AI continues to evolve. Attendees will gain insights into where to invest, what to approach with caution, and how to position their organizations for the next wave of AI innovation. Expect a candid, thought-provoking discussion that challenges assumptions and offers practical guidance for the road ahead.

Key Takeaways

1. Separating AI Reality from Hype: Understand what today's AI technologies can truly deliver, where limitations remain, and which advancements deserve attention.

2. Looking Ahead to the Next Five Years: Explore how agentic AI, autonomous systems, and progress toward artificial general intelligence could transform business, cybersecurity, and enterprise operations.

3. Making Smarter AI Investments: Gain practical guidance on where organizations should focus their resources, how to evaluate emerging technologies, and how to prepare for an increasingly AI-driven future.