Precision Engagements
Structured for
Regulated Institutions
No generic solutions. Every engagement is fit and tailored to your institution's strategic objectives, examination timeline, and risk posture.
Engagement 01
AI Governance Training
Scope
- AIGP certification preparation curriculum
- ISO 42001 AIMS implementation & Audit training
- Auditing non-deterministic “Black Box” models
- AI Impact Assessment (AIA) methodology
- EU AI Act compliance requirements for institutions
Deliverables
- Structured training modules for risk, audit, and compliance teams
- Self-sustaining internal AI governance capability
- AIGP exam readiness assessment & practice materials
- Customized case studies from regulated financial services
AIGPISO 42001EU AI ActNIST AI RMF
Representative Engagement
- Delivered risk-tailored training programs for model owners across the enterprise, ensuring comprehension of governance frameworks commensurate with their model risk ratings.
- Established company-wide taxonomy and definitions for AI/ML models to meet stringent regulatory requests, effectively communicating boundaries and risk mitigation controls to cross-functional teams.
- Led quality assurance initiatives to standardize validation procedures and templates, fostering a self-sustaining internal culture of rigorous, documented oversight.
Engagement 02
ISO 42001 AI Management System Implementation
Scope
- Maintained and enhanced comprehensive model risk governance frameworks, directing the continuous lifecycle of risk policies, standards, and operating procedures.
- Executed targeted gap assessments of model risk policies against strict regulatory requirements (SR 11-7 and SR 15-18), aligning internal controls with examiner expectations.
- Engineered preventative quality control programs to capture, monitor, and remediate data gaps within the enterprise model inventory, ensuring complete visibility for risk treatment.
Deliverables
- Designed and executed risk-based audit approaches for complex models, rigorously testing conceptual soundness, outcome analysis, and ongoing monitoring controls to address black-box vulnerabilities.
- Oversaw the validation and automation of machine learning benchmarking platforms (including Random Forests, GBM, and Neural Networks) to ensure empirical evidence of model stability.
- Identified and remediated critical control deficiencies, such as unmasking performance degradation disguised by overly weighted sample periods, ensuring audit-ready transparency.
ISO 42001ISO 27001 mappingCOSO
Representative Engagement
- Designed and deployed end-to-end process automation utilizing GRC platforms and workflow tools to streamline risk-reporting and establish clear escalation paths to executive risk committees.
- Orchestrated systemic change management for model risk standards, structured around the three pillars of committee evaluation, risk alignment, and comprehensive board reporting.
- Integrated an automated ML selection and benchmarking engine into the enterprise management system, utilizing Random Forests and Neural Networks to provide objective, algorithm-driven model challenges.
- Executed targeted gap assessments of model risk policies against SR 11-7 and SR 15-18 requirements, bridging the gap between legacy financial regulations and modern AI Management Systems (AIMS).
- Engineered preventative quality control programs to remediate data gaps within the enterprise model inventory, ensuring the total traceability of risk objects across the AI lifecycle.
Engagement 03
EU AI Act / NIST RMF / ISO 42001 Audit Readiness
Scope
- EU AI Act risk classification and compliance mapping
- NIST AI RMF alignment and implementation
- MRM framework extension for AI/ML models (SR 11-7)
- Validation governance design for non-deterministic models
- Bias testing protocols & fairness measurement
Deliverables
- Cross-framework compliance matrix (EU AI Act × NIST × ISO 42001 × SR 11-7)
- Model validation standards for AI/ML
- Examination readiness self-assessment toolkit
- Regulatory response templates for examiners
EU AI ActNIST AI RMFSR 11-7FHFA 2022-02OCC 11-12
Representative Engagement
- Provided independent assurance for an enterprise-wide performance monitoring program, architecting a “Hub-and-Spoke” oversight structure that mirrors the EU AI Act’s Provider-Deployer-User value chain.
- Transitioned oversight from siloed, technical-only metrics to integrated user-engagement evaluations, ensuring model efficacy is measured by real-world business outcomes at the end-user level.
- Designed and executed risk-based audit approaches for complex models, rigorously testing conceptual soundness and ongoing monitoring controls to address black-box vulnerabilities.
- Identified and remediated critical control deficiencies, such as unmasking performance degradation disguised by overly weighted sample periods, ensuring audit-ready transparency.
Engagement 04
Board-Level AI Governance Architecture
Scope
- AI governance policy frameworks & committee charters
- Risk appetite statements for AI/ML deployment
- Board reporting dashboards & metrics design
- Kinetic control planes — real-time governance for agentic AI
- Constitutional AI logic & post-training safety tuning
Deliverables
- Board-ready AI governance charter & oversight framework
- 3-Lines-of-Defense model adapted for AI risk
- Risk appetite statement calibrated to regulatory expectations
- Automated control plane architecture documentation
Automated Controls & Tooling
- NVIDIA NeMo Guardrails — programmable safety rails
- AWS Bedrock Guardrails — content filtering & PII redaction
- Llama Guard — input/output safety classification
- Guardrails AI — structural output validation
Tracing & Sandbox
- LangSmith / LangFuse — LLM tracing & observability
- Weights & Biases — experiment tracking & model registry
- Prompt Sandbox — red-teaming & adversarial testing
- MLflow — model lifecycle management & governance
COSOISO 42001Constitutional AIAgentic AI
Representative Engagement
- Established automated mapping and workflow processes for model-related risk objects, streamlining risk-reporting and formalizing escalation paths to executive committees.
- Applied N-version programming (N-programming) and rigorous coverage testing to guarantee implementation and deployment accuracy for mission-critical GSE models.
- Critically reviewed model change control protocols within live production environments to ensure governance traceability and prevent unauthorized execution drift.
The Approach
Every Engagement Follows the Same
Four-Phase Discipline
01
Diagnostic
Current-state assessment against applicable regulatory frameworks and institutional risk appetite.
02
Scope
Precision scope definition. Fixed deliverables, clear timeline, no scope creep. Approved before work begins.
03
Deliver
Examination-ready artifacts, control documentation, and implementation support. Senior practitioner throughout.
04
Exit
Knowledge transfer, internal capability building, and clean disengagement. No vendor lock-in.