Responsible AI Summit North America: Day 1 - Tuesday, June 23, 2026

As AI systems grow in complexity, governance tooling needs to evolve too. This session explores how new AI governance practices can boost productivity through automated insights and recommendations. This helps teams analyze AI use cases, compare approaches, proactively identify risks, define controls, and support compliance planning, all while mapping governance requirements to tangible business outcomes. Discover how AI‑enhanced governance reduces manual effort, drives consistency, and enables enterprises to scale responsible AI without sacrificing oversight. 

Marc Cassagnol

Senior Product Manager
IBM

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AI adoption is outpacing governance, particularly in the U.S., where AI regulation is inconsistent across states and remains unsettled at the federal level. In this session, Matt Bedsole, Director of AI Governance at Lowe’s, shares how Lowe’s is building a scalable Responsible AI governance program anchored in practical software lifecycle controls, security and privacy reviews, and end-to-end traceability. Matt explains how Lowe’s is enabling rapid AI adoption while maintaining accountability and trust, with a clear focus on empowering the workforce and driving business value, not reducing headcount.

Matt Bedsole

Director of AI Governance
Lowe's

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Wendy Siemers

Senior Director of AI Governance, Data Governance, and Consumer Privacy
Lowe's

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Invisible AI introduces a governance challenge: systems that continuously sense, infer, and act without explicit user interaction stretch traditional privacy and Responsible AI frameworks. When AI operates ambiently, responsibility shifts from user choice to organizational accountability, requiring stronger internal controls, oversight, and risk ownership. This panel focuses on how leaders can operationalize Responsible AI to manage privacy at scale when AI is embedded everywhere.

• Governance replaces user-led consent.

• Responsible AI requires inference accountability.

• Privacy must be enforced systemically.

Shana Morgan

Global Head of AI / Privacy
L3Harris Technologies

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Irene Mo

Senior Counsel, Privacy, Cybersecurity and AI
Rivian

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Aveen Sufi

Director Privacy Operations and Legal & AI Governance Co-Chair
SCAN

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As AI systems become embedded in everyday tools and automated workflows, securing them has become a real and often misunderstood challenge. This talk examines what can go wrong when AI models are secured using assumptions and controls that do not hold up in real deployments, creating gaps between perceived safety and actual behavior.

Kasimir Schulz

Director, Security Research
HiddenLayer

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Unlike traditional IT, AI systems don’t just follow rules. They learn, adapt, and change based on how we interact with them. In launching Mars’ AI for product innovation, Brandel has firsthand experience overcoming governance setbacks and unanswered questions that traditional IT models were never designed to handle. She shares hard-won learnings from monitoring, controlling, and governing learning AI at scale at Mars.

Brandel Kremer

Director of Data Governance
Mars

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As organizations deploy AI agents and models to interact directly with external users, risk exposure increases across security, compliance, and trust. These systems often operate without human oversight, raising challenges around bias, prompt injection, data leakage, and unpredictable behaviour. This session focuses on governing public-facing agentic AI across the full lifecycle, with emphasis on pre-deployment testing, adversarial evaluation, and continuous monitoring post-deployment. Panellists also discuss challenges and the tension between scaling ROI and ensuring systems are secure, reliable, and accountable.

• Identifying and mitigating risks in public-facing, autonomous AI systems.
• Strengthening pre-deployment testing and adversarial evaluation practices.
• Balancing ROI with security, compliance, and lifecycle governance.

Parisa Lak

Director AI Model Risk Management
Manulife

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Veer Yedlapalli

Director of Product Security, Security Engineering and AI Security
Grainger

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John O'Donnell

Chief Revenue Officer
Alice

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As AI systems move from experimentation to embedded business tools, accountability often becomes unclear. This roundtable brings leaders together to discuss who owns risk across AI design, deployment, and day-to-day use. The conversation will explore how organizations are defining accountability, building AI literacy, and aligning incentives so responsibility is clear when things go wrong.

• Assigning accountability across AI development, deployment, and use.

• Clarifying ownership between technical, legal, and business teams.

• Building AI literacy to support responsible decision-making.

Jeanne Michele Mariani

Senior AI & Data Governance Counsel
General Motors

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Hayley Antoniuk

Head of Technology Compliance
Garner Health

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Ankita Mohapatra

AI Governance Advisor
Credo AI

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Autonomous AI in highly regulated industries such as healthcare and finance introduces complex risks that demand rigorous governance. This session explores how organizations implement responsible AI frameworks in critical sectors, balancing innovation, compliance, and accountability. It will highlight strategies for managing risk, ensuring regulatory alignment, and embedding oversight into AI systems that operate in high-stakes environments.

Aneta Osmola

Vice President Data and AI Risk
Scotiabank

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Rajiv Avacharmal

Director of Responsible AI
Prudential Financial

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Simeen Mirza

Founder & Director | Observer
Argentia Impact, WHO Working Group: Regulatory Considerations on AI for Health

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Last year’s discussion focused on building the governance guardrails for GenAI. This year, Citi’s focus has shifted to translating those guardrails into operational outcomes - moving from model governance to the practical realities of launch and adoption. That shift requires clear prioritization of use cases, consistent navigation of risk tiers, credible proof of business value, and effective performance monitoring after go-live. As customer-facing GenAI grows more autonomous, compliance and safety assurance must evolve alongside it. The strongest programs position post-deployment monitoring as an enabler of speed, safety, and scale - not a brake on innovation.

• Prioritize the step change Gen AI driven business initiatives using practical, outcome-driven ROI metrics ($ impact driven by self- serve rate increase, AHT reduction, quality uplift, for example).

• Build launch teams tailored to each use case. These tend to be very interdisciplinary, but the structure of the “pod” varies by initiative.

• Establish governance based on risk tiers and the respective testing requirements.

• Post launch on-going, and “near real time”

• Measuring business value using the KPIs above.

• Monitoring post-deployment adherence to ensure approvals remain valid as systems scale and evolve.

Sami Huovilainen

Managing Director - Head of Next Gen Analytics
Citi

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How to evolve from ad hoc reviews into an embedded, repeatable framework integrating the existing risk assessment in SDLC, Model Risk Management, and Third-Party Risk Management and beyond. This session examines how acceptable-use gates and structured triage enable consistent risk decisions across diverse AI use cases, including internally developed and agentic systems that fall outside traditional MRM assumptions. It explores how acceptable risk is operationalized and recalibrated as conditions change.

Cindy Tu

VP, Head of Tech & Cyber Governance
LPL Financial

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Generative AI is reshaping cybersecurity by accelerating attack cycles, expanding the threat surface, and creating risks traditional controls were not built to manage. In this session, Vineet Agrwal, Head of Cybersecurity & Technology Risk (Second line), at American Express explores how leading financial institutions are moving from static, point-in-time risk frameworks to dynamic, continuous governance models that operate at AI speed. It will examine how organizations are redefining vulnerability management and adopting approaches such as continuous threat exposure management. The discussion will also examine the evolving role of Boards in cyber risk governance, from passive oversight to active stewardship, in setting risk appetite, driving accountability, and aligning AI adoption with cyber resilience and long-term trust.

Attendees will leave with practical insights into governing AI-enabled cyber defenses responsibly, ensuring governance acts not as a constraint, but as an enabler of secure innovation and competitive advantage.

• Shifting governance models to match AI-speed risk exposure.
• Strengthening Board accountability for AI cyber resilience.
• Enabling secure innovation through continuous AI governance. 

Vineet Agrwal

Head of Cybersecurity & Technology Risk (Second line)
American Express

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