AI Governance Regulatory Advisory

Govern AI Deployment with the Rigor Supervisors Are Beginning to Require

Artificial intelligence has moved from a strategic experiment to operational infrastructure in financial services, and supervisory expectations have begun to follow suit. The Financial Stability Oversight Council identified AI as a significant area of regulatory focus in its 2024 Annual Report. The U.S. Department of the Treasury released a dedicated Financial Services AI Risk Management Framework in early 2026. 

The OCC, Federal Reserve, and FDIC continue to apply model risk management expectations to AI and machine learning systems. FINRA and the SEC have made clear that existing supervision, documentation, and governance obligations apply to AI tools without exception.

The challenge for financial institutions is not a lack of AI ambition. It is the absence of a governance architecture that can demonstrate to boards, examiners, and the public that AI deployment is transparent, accountable, explainable, and consistent with existing regulatory obligations. Institutions that treat AI governance as a technical function rather than a regulatory governance discipline will face supervisory consequences that technical remediation alone cannot resolve.

RIG designs AI governance frameworks that satisfy current supervisory expectations, anticipate the direction of emerging AI regulation, and position institutions to deploy AI at scale without creating the governance gaps that attract examiner scrutiny.

RIG provides structured analysis of the current and emerging AI regulatory landscape, including model risk management guidance from the OCC, Federal Reserve, and FDIC; FINRA and SEC expectations for AI-enabled communications and decision-making; state-level AI requirements; and international frameworks, including the EU AI Act. 

We evaluate how the institution’s current AI deployment map intersects with this regulatory landscape and identify where governance gaps create material supervisory exposure.

RIG designs enterprise-level AI governance frameworks that establish clear accountability structures, model inventory requirements, validation protocols, bias and fairness controls, and board-level oversight mechanisms across the institution’s AI portfolio. 

Our frameworks are built on the foundation of existing model risk management expectations, including SR 11-07 and the NIST AI Risk Management Framework. This extends those foundations to address the governance dimensions specific to machine learning, generative AI, and algorithmic decision-making.

Not all AI applications carry the same level of supervisory risk. Credit decisioning, fraud detection, consumer communications, and algorithmic trading carry substantially higher regulatory exposure than internal operational tools. 

RIG applies a structured use-case risk assessment methodology aligned with the Treasury’s Financial Services AI Risk Management Framework and the EU AI Act’s risk-based classification approach to evaluate each AI application against its regulatory, consumer, and operational risk profile and calibrate governance controls accordingly.

Financial institutions are fully responsible for the regulatory compliance of AI tools deployed through third-party vendors, regardless of whether the model was built internally or licensed externally. 

RIG advises institutions on vendor due diligence frameworks, contractual governance requirements, and ongoing oversight protocols for third-party AI systems, ensuring that vendor relationships do not become unmonitored vectors of supervisory exposure.

As regulators develop AI-specific supervisory expectations, institutions that engage proactively through comment letters, supervisory dialogues, and industry forums have a meaningful opportunity to shape the governance standards they will ultimately be required to meet. 

RIG supports institutions in developing structured AI regulatory engagement strategies that position them as credible, governance-mature contributors to the policy dialogue shaping the AI regulatory environment.

AI governance operates as a specialized extension of the integrated architecture. Strategic regulatory intelligence surfaces emerging AI regulatory signals and supervisory expectations. Change management provides the governance infrastructure for implementing AI controls with the traceability and documentation rigor that supervisors require. 

Government relations and thought leadership positions the institution as a credible contributor to AI policy development. The integrated approach ensures that AI governance is not treated as an isolated technical initiative but as a fully governed discipline within the broader regulatory architecture.

Supervisory Readiness

AI governance frameworks that satisfy current model risk management expectations and anticipate the direction of emerging AI-specific regulation.

Board-Level AI Oversight

Structured reporting and inventory governance that enable boards to exercise meaningful oversight of AI deployment without requiring technical expertise.

Reduced Examination Exposure

Documented AI governance evidence, including classification, validation, bias controls, and vendor oversight, that closes the gaps examiners are beginning to probe.

Proactive Regulatory Positioning

Engagement strategies that position the institution as a credible, governance-mature voice in the AI regulatory debates that will shape future supervisory expectations.