DESCRIPTION & INTENTIONS OF THE AI REGRISK™ READINESS PROGRAM
Wealth management & wealthtech firms operate in a highly regulated environment and handle sensitive client data, making responsible AI adoption essential. The AI RegRisk™ Readiness Program helps firms comply with evolving regulations, mitigate algorithmic bias and data privacy risks, and maintain fiduciary trust. By aligning governance, risk assessment, and AI-focused compliance, these firms can harness AI for portfolio optimization, personalized advice, and operational efficiency—while minimizing legal, financial, and reputational threats.
The AI RegRisk™ Readiness Program offers a comprehensive, adaptive framework to manage AI-related risks responsibly. Organized into five pillars with supporting domains, it aligns governance, risk management, and ethical AI practices with emerging standards, laws, and regulatory guidance. By systematically addressing regulatory expectations and industry best practices, the program enables organizations to balance innovation with compliance, maintain transparency, and ensure accountability across all stages of AI implementation.
OVERALL BENEFITS OF THIS PROGRAM
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PILLARS & DOMAINS
Pillar 1: Agile Governance
Definition:
Agile Governance is adaptive, human-centric oversight that continuously evolves through iterative reviews, ensuring that AI initiatives are transparent, accountable, and aligned with both internal standards and external regulatory expectations.
Intention:
Enabling organizations to swiftly adapt governance frameworks to evolving risks, technologies, executive and board oversight obligations, and regulatory expectations.
Domain 1: Enterprise-Wide AI Governance Policies
Domain 2: Clear Roles, Responsibilities, and Training
Domain 3: Integration with Broader Enterprise Risk Frameworks
Domain 4: Board Defined Scope and Active Oversight
Domain 5: Audit Processes and Continuous Governance Improvement
Domain 6: Resource Alignment and Capability Building
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Pillar 2: Risk-Informed System
Definition:
A risk-informed system is a repeatable process defining how to identify, assess, manage, and communicate AI-related risks. It leverages a formal methodology to establish risk tolerance and prioritize the most significant risks for timely decision-making.
Intention:
Enabling organizations to make informed decisions by prioritizing risks based on clearly defined tolerances and ensuring proactive risk management.
Domain 7: Standardized AI Risk Assessment Framework
Domain 8: Defined Risk Appetite and Thresholds
Domain 9: Comprehensive AI Risk Mapping
Domain 10: Periodic Risk Assessments and Transparent Reporting
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Pillar 3: Responsible AI (Trusted AI)
Definition: Responsible AI integrates ethical, transparent, and accountable principles into AI development and deployment. It ensures model trustworthiness, reliability, and regulatory compliance, promoting stakeholder confidence and meeting evolving societal expectations.
Intention:
Fostering trust by ensuring AI systems operate in a manner that is ethical, transparent, and reliable while meeting regulatory and stakeholder expectations.
Domain 11: Model Risk Management
Domain 12: Data Governance & Risk Management
Domain 13: AI Agent Management and Accountability
Domain 14: Prompting Guardrails & Iterative Fine-Tuning Protocols
Domain 15: Enhanced Explainability & Comprehensive Transparency
Domain 16: Adversarial Robustness & AI Cybersecurity
Domain 17: Robust Assurance & Cybersecurity Testing for AI Systems
Domain 18: Comprehensive Assurance & Testing
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Pillar 4: Risk-Based Strategy and Execution with Continuous Monitoring
Definition:
A risk-based strategy embeds AI risk management into strategic planning and the broader AI roadmap. By focusing on acceptable risk levels and continuous monitoring, organizations ensure risk is never an afterthought—allowing for more effective resource allocation, oversight, and goal achievement.
Intention:
Optimizing AI deployment by ensuring that risk management is a core element of strategy, budgeting, execution, and continuous monitoring.
Domain 19: Risk-Based Strategic Planning and Budget Alignment
Domain 20: Execution of AI Initiatives to Meet Risk Thresholds
Domain 21: Continuous Monitoring and Performance Metrics
Domain 22: Third Party and Ecosystem Risk Management
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Pillar 5: Risk Escalation and Disclosure
Definition:
Risk escalation and disclosure define how critical risks are communicated internally and externally, ensuring compliance, transparency, and public trust.
Risk Escalation: Alerts senior executives, boards, or governance bodies when specific thresholds are exceeded.
Risk Disclosure: Informs external stakeholders—such as regulators, shareholders, or the public—about material risks or incidents, as required by law or stakeholder expectations.
Intention:
Establishing structured processes for risk escalation and disclosure to ensure prompt, transparent responses that uphold accountability and regulatory adherence.
Domain 23: Structured Risk Escalation Protocols
Domain 24: Transparent Risk Disclosure Processes
Domain 25: Regular Testing and Auditing of Escalation Processes
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