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AI Governance & Risk
Ensuring AI systems are trustworthy, accountable, and aligned with enterprise risk management.
Executive Summary
AI governance is not a compliance exercise—it is a strategic capability that enables organizations to scale AI responsibly while protecting enterprise value, reputation, and stakeholders. I work with boards and executive leadership to design AI governance and risk frameworks that balance innovation with control, ensuring AI systems are transparent, defensible, and aligned with business and regulatory expectations.
Why AI Governance Matters at the Executive Level
As AI becomes embedded in core business processes, unmanaged risk can quickly translate into financial, regulatory, operational, and reputational exposure. Common challenges include:
Limited visibility into AI decision-making and outcomes
Inconsistent model oversight across the enterprise
Regulatory and compliance uncertainty
Weak accountability for AI-related decisions
Fragmented governance between technology, risk, and business teams
Effective AI governance provides leadership with confidence that AI systems are operating within defined risk tolerances and ethical boundaries.
My AI Governance & Risk Framework
My approach integrates AI governance directly into existing enterprise risk and control structures, rather than treating it as a standalone initiative.
1. Governance Structure & Accountability
1. Governance Structure & Accountability
Clear ownership, decision rights, and escalation paths are established across business, technology, risk, and compliance functions. This ensures AI decisions are governed at the appropriate executive level.
2. Responsible AI Principles
2. Responsible AI Principles
AI systems are guided by principles such as transparency, fairness, robustness, and human oversight. These principles are operationalized—not aspirational—through policies, standards, and controls.
3. Model Risk Management
3. Model Risk Management
AI and advanced analytics models are treated as enterprise assets subject to lifecycle management, validation, monitoring, and documentation, consistent with model risk management best practices.
4. Regulatory & Compliance Readiness
4. Regulatory & Compliance Readiness
Governance frameworks are designed to align with evolving regulatory expectations, internal audit requirements, and external scrutiny—particularly in regulated industries.
5. Monitoring, Controls & Assurance
5. Monitoring, Controls & Assurance
Ongoing monitoring, performance thresholds, and auditability are embedded to ensure AI systems continue to operate within defined risk parameters as they scale.
How I Work with Executive Leadership
Governance and risk engagements typically include:
AI governance maturity assessments
Design of enterprise AI governance frameworks
Integration of AI risk into ERM and model risk programs
Board and executive briefings on AI risk exposure
Development of policies, standards, and oversight processes
All work is tailored to organizational complexity, industry context, and risk appetite.
Who This Is For
This work is most relevant for boards, CEOs, Chief AI Officers, CIOs, CROs, and senior leaders responsible for enterprise transformation, risk management, and long-term value creation.
Call to Action
Interested in developing or refining your enterprise AI strategy?
Start a conversation.
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