AI & Data Analytics for Corporate Compliance

As organizations move beyond AI pilots and isolated use cases, leaders face increasing pressure to scale AI responsibly, strengthen data governance, and demonstrate measurable business value. This session explores how Chief AI Officers are building the foundations for sustainable adoption, aligning governance with innovation, and transforming AI investments into operational efficiencies, stronger decision-making, and measurable organizational outcomes.

Participants will gain practical insights into the strategies, frameworks, and lessons learned from organizations successfully transitioning from experimentation to enterprise-scale implementation.

As organizations increasingly deploy AI across their operations, regulators, enforcement agencies, and corporate boards are paying closer attention to how these systems are governed and managed. Compliance leaders must balance innovation with effective oversight to address legal, ethical, operational, and reputational risks. This session will explore evolving regulatory expectations and provide practical strategies for building accountable AI governance programs.

• Understand how regulators and enforcement agencies are defining AI accountability and what compliance programs must demonstrate to meet evolving expectations.
• Learn how to incorporate AI governance, risk assessments, monitoring, and controls into existing corporate compliance frameworks.
• Explore the expanding role of boards and senior executives in overseeing AI strategy, managing risk, and ensuring organizational accountability.
• Identify practical steps for preparing for AI-related audits, investigations, and enforcement actions through effective documentation and oversight practices.

As regulatory expectations around internal reporting, self-disclosure, and proactive risk detection continue to evolve, organizations are rethinking how they identify misconduct before it escalates into an enforcement, legal, or reputational event. This session will explore how compliance leaders are leveraging AI to transform whistleblower hotlines, culture analytics, case management systems, and employee reporting data into actionable intelligence that strengthens investigations, accelerates response times, and enhances organizational oversight.

• Leveraging AI to analyze whistleblower reports, hotline data, employee feedback, and culture indicators to identify emerging misconduct risks at earlier stages
• Enhancing investigative triage, case prioritization, and escalation workflows through intelligent risk scoring and pattern detection
• Measuring and monitoring organizational culture over time using behavioral, reporting, and engagement data to surface hidden compliance concerns
• Building AI-powered early warning systems that support regulatory expectations, strengthen speak-up programs, and enable faster, more informed decision-making

As AI evolves from content generation to task execution, compliance leaders must determine where intelligent agents can create value and where human oversight remains essential. This highly interactive workshop will guide participants through the process of identifying, designing, and governing AI agents capable of supporting core compliance activities while maintaining accountability, transparency, and control.

• Identifying high-value opportunities for AI agents across compliance monitoring, investigations, third-party due diligence, training, and risk assessments
• Applying a practical framework to determine when AI agents should summarize, analyze, recommend, escalate, or initiate action
• Designing human-in-the-loop controls, approval workflows, and governance guardrails to ensure responsible deployment
• Exploring real-world examples of agentic AI implementations and the operational, risk, and compliance considerations associated with scaling them across the enterprise

As AI becomes embedded across compliance, legal, risk, and operational functions, organizations must balance innovation with security, governance, and accountability. This session will explore how companies are protecting sensitive data, managing emerging AI risks, and establishing controls for increasingly autonomous AI-driven workflows.

• Protecting sensitive data while enabling AI adoption, establishing appropriate access controls, and building governance frameworks that support secure, compliant use of AI across the enterprise
• Identifying AI risks and defining oversight mechanisms, including strategies to manage bias, hallucinations, cybersecurity vulnerabilities, third-party model risk, and regulatory exposure
• Determining where autonomous AI workflows create value, implementing human-in-the-loop controls, and ensuring clear accountability for AI-driven decisions and actions
• Preparing for evolving regulatory expectations, aligning AI governance with compliance requirements, and building a defensible framework that can withstand regulatory and stakeholder scrutiny

Many organizations have identified promising AI use cases but continue to struggle with implementation, adoption, governance, and demonstrating measurable value. This practitioner-led case study provides a candid, end-to-end look at how one organization successfully moved from experimentation to enterprise deployment, highlighting the operational, technical, and organizational lessons learned along the way.

• Identifying the compliance challenge, defining the use case, and building a business case that resonated with executive leadership
• Establishing the data foundations, governance framework, and cross-functional partnerships required for successful implementation
• Navigating rollout, change management, stakeholder adoption, and integration with existing compliance workflows
• Measuring success through compliance outcomes, risk reduction, productivity gains, and other key performance indicators to demonstrate sustainable value and impact

Many compliance organizations possess vast amounts of data across hotline systems, investigations, monitoring platforms, third-party due diligence tools, and business operations, yet struggle to transform that information into meaningful intelligence. This session will examine how leading organizations are breaking down data silos, strengthening data governance, and leveraging advanced analytics to create a more connected, proactive, and predictive approach to compliance risk management.

• Building the data foundations required to support AI, advanced analytics, and enterprise-wide compliance intelligence
• Leveraging machine learning, anomaly detection, and predictive analytics to identify emerging risks and compliance trends earlier
• Connecting fragmented data sources across investigations, hotline programs, monitoring activities, and third-party risk functions to create a unified view of risk
• Developing scalable analytics infrastructures that enable continuous monitoring, stronger decision-making, and more actionable compliance insights

1. What’s Keeping You Up at Night? AI Challenges, Lessons Learned, and Peer Solutions
   
   Jorn Wegter
   Senior Counsel, Institutional Affairs – AI and Data Governance
   The World Bank


2. AI Governance | Governing the Agentic Enterprise: Policy, Readiness, and Impact
   
   Gabriela Voloschin
   Assistant Vice President – Senior Legal AI Analyst
   T. Rowe Price



3. The AI Accountability Gap | Who Is Responsible When Autonomous AI Agents Cause Harm?
   
   J.B. Branch
   Director of Federal AI Governance and Technology Policy
   Public Citizen


4. Winning the Business Case for AI: Securing Budget, Executive Sponsorship, Board Confidence, and Measurable Results
   
   Zorina Alliata
   Director of AI Enablement
   Wolters Kluwer
   
   Helina Mohammed
   AI Strategy & Governance,
   Readiness & Adoption Committee
   GovAI Coalition