EXECUTIVE SUMMARY 1 From Enterprise Innovation to Accountability: Governing AI, Data, and Risk at Scale
- jmalrakeem
- 1 hour ago
- 3 min read
Elevate Xchange JUNE 4th Mastermind Collaboration Roundtable Forum
As organizations rapidly scale artificial intelligence initiatives, the focus is shifting from
experimentation and innovation to governance, accountability, and risk management. While AI
presents significant opportunities to enhance operational efficiency, decision-making, and customer experiences, it also introduces new challenges related to data quality, regulatory compliance, model transparency, security, and ethical use.
This session explores how enterprises can establish effective governance frameworks that balance innovation with responsible AI adoption. Attendees will learn strategies for managing AI and data risks across the enterprise, implementing controls for model oversight, ensuring regulatory readiness, and fostering cross-functional collaboration between business, technology, risk, and compliance teams. Through practical insights and real-world examples, the discussion will highlight how organizations can build trust, improve accountability, and create scalable governance structures that support sustainable AI-driven transformation.
By embedding governance and risk management into the AI lifecycle, enterprises can accelerate innovation while maintaining transparency, resilience, and stakeholder confidence in an increasingly complex digital landscape.
This version is suitable for a conference agenda, webinar description, executive roundtable, or thought-leadership event.
AI Governance and Oversight LED BY James Beeson, R1 RCM, CISO
Defined governance structure, including an AI Governance Committee, decision rights, and escalation paths.
Policies and controls for AI usage, covering model selection, acceptable use, data handling, and security.
Ongoing oversight and risk management, assessing models pre-deployment and
monitoring performance, bias, and compliance.
AI Use Cases – Design, Build, and Why It Worked LED BY Amish Chadha, Global
Technology Strategy
Problem definition and success criteria, tying use cases to real business needs and measurable outcomes.
Design and architecture choices, including tech stack, model selection, and operational
guardrails.
Demonstrated business impact, highlighting lessons learned, replicable elements, and ROI realized.
Proving ROI and Managing Risk LED BY Juhi Chawla, VP Division CIO, Brinks
Business case validation, connecting AI initiatives to revenue, efficiency, and operational
metrics.
Risk identification and impact assessment, covering legal, security, data, operational, and
reputational risks.
Ongoing monitoring and course correction, ensuring benefits are realized while
continuously managing risk.
Technology, Stack, and Agentic AI Capabilities LED BY George Kanyotu, AVP Global
Infrastructure Services, GM Financial
Tech stack evaluation, selecting infrastructure and platforms aligned with business needs.
Agentic AI use, defining autonomous capabilities, guardrails, and operational boundaries.
Scalability and integration, ensuring AI solutions fit enterprise architecture and drive
measurable outcomes.
Third-Party and Vendor Risk Management LED BY Manmohan Singh, Deputy CISO, UT Southwestern
Due diligence on AI vendors, assessing security, compliance, and reliability before adoption.
Ongoing vendor monitoring, including SOC 2 reports, audits, and contractual compliance obligations.
Contingency planning, preparing fallback options if vendors fail or risk thresholds are
exceeded.
Security and Data Protection for AI LED BY Manvinder Benipal, BISO, Toyota Financial Services
Data encryption and access controls, protecting sensitive and regulated information
throughout the AI lifecycle.
Incident detection and response, establishing protocols for data breaches or misuse
involving AI systems.
Integration with enterprise cybersecurity, ensuring AI systems comply with broader security frameworks and standards.
Securing the Workforce with AI LED BY Thomas Donnley Co-Founder & CTO, Amplifier Security
Device attribution / ownership and security tooling coverage, closing visibility gaps that
stall incident response and audit readiness.
Employee device vulnerability remediation, reducing mean time to patch by engaging end users directly instead of routing through IT.
Safe AI usage and shadow AI governance, enforcing acceptable-use policies in real-time
as AI adoption scales across the organization.
CMMC and Federal Acquisition Changes LED BY Tom Sweet, CIO, IR Pros
Third-party validation and audit readiness for compliance, shifting from self-attestation.
Expanded scope for systems and data, including SaaS and AI-enabled platforms handling
Controlled Unclassified Information (CUI).
Supply-chain accountability, requiring monitoring and enforcement of security controls
across vendors and subcontractors.
Risk Management for Agentic and Autonomous AI Systems LED BY Todd Ellison, Field CISO, Nile Secure
Identifying new risk categories introduced by autonomous agents
Establishing guardrails for delegation, autonomy, and decision authority
Monitoring emergent behaviors and unintended consequences
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