Governance Alignment Translator

Overview

Translate technical AI capability documentation into language governance bodies understand. The goal is documentation that enables informed approval decisions by risk committees, compliance officers, and auditors.

Core principle: Governance doesn't need to understand HOW it works technically. They need to understand WHAT it does, WHAT could go wrong, and HOW risks are controlled.

Governance Tier Classification

Classify capability before writing documentation:

Tier	Criteria	Governance Requirements
Tier 1 - High Impact	Customer-facing decisions, regulatory reporting, material financial impact	Full MRM review, Risk Committee approval, quarterly attestation
Tier 2 - Operational	Internal process automation, decision support, efficiency tools	MRM notification, Operations leadership approval, annual review
Tier 3 - Low Risk	Analytics, reporting, non-decision tools	Technology approval, documented procedures

yaml

tier_classification:
  tier: "Tier 2 - Operational"
  rationale: |
    - Automates internal matching process (not customer-facing)
    - Supports human decision, doesn't replace judgment
    - Settlement impact is detectable and reversible
  mrm_requirement: "Model Risk notification + documentation"

Output Format

yaml

governance_package:
  capability: "[Name]"
  tier: "[Tier 1/2/3]"
  tier_rationale: "[Why this tier]"
  owner: "[Business owner]"
  prepared_for: "[Governance body]"

executive_summary:
  business_description: |
    [What it does in plain business terms - no technical jargon]

  business_case: |
    [Why we're doing this - efficiency, risk reduction, etc.]
    [Quantified benefits where possible]

  risk_comparison_to_status_quo: |
    [Current process also has risks - compare]
    [Is AI risk higher, lower, or different?]

  decision_requested: |
    [Specific approval being sought]

risk_profile:
  risk_category: "[Operational/Credit/Market/Compliance/etc.]"

  identified_risks:
    - risk: "[Description]"
      impact: "[HIGH/MEDIUM/LOW] - [$ range or description]"
      likelihood_before_controls: "[HIGH/MEDIUM/LOW]"
      likelihood_after_controls: "[HIGH/MEDIUM/LOW]"
      residual_rating: "[Result]"

  comparison_to_manual: |
    [Manual process error rate vs AI error rate]
    [Which risks are reduced, which are introduced?]

control_framework:
  controls:
    - control_id: "[C1]"
      control: "[Description]"
      risk_addressed: "[Which risk]"
      owner: "[Named role]"
      monitoring: "[How verified]"
      effectiveness: "[Design: Strong/Moderate/Weak, Operating: Tested/Not tested]"

  control_gaps:
    - "[Any gaps identified]"

regulatory_alignment:
  applicable_regulations:
    - regulation: "[Citation]"
      specific_requirements: |
        [What exactly does regulation require?]
      compliance_approach: |
        [How does this capability comply?]
      evidence: "[What documentation exists]"

  model_risk_management:
    mrm_tier: "[Tier 1/2/3 per OCC 2011-12]"
    tier_rationale: "[Why this tier]"
    documentation_status: "[Submitted/Approved/N/A]"
    validation_approach: "[How model is validated]"

governance_requirements:
  approvals_required:
    - body: "[Name]"
      status: "[Pending/Approved/N/A]"
      condition_link: "[Which approval condition]"

  ongoing_oversight:
    - mechanism: "[What oversight]"
      frequency: "[How often]"
      owner: "[Who]"

  approval_conditions:
    - condition: "[Specific condition]"
      risk_addressed: "[Which risk this mitigates]"
      verification: "[How compliance verified]"

audit_considerations:
  audit_trail:
    - "[What is logged]"

  evidence_available:
    - "[What can be examined]"

  examination_approach: |
    [How auditors would test this capability]

stakeholder_summaries:
  cro_one_pager: |
    [Risk-focused summary for CRO - 1 page max]

  compliance_checklist:
    - requirement: "[Regulation/requirement]"
      status: "[Compliant/In progress/Gap]"
      evidence: "[Documentation reference]"

  audit_work_program:
    - test: "[What to test]"
      method: "[How to test]"
      expected_evidence: "[What to look for]"

Translation Rules

Technical → Governance Language

Technical	Governance
BERT/transformer/model	"AI system" or "automated system"
Confidence score	"Certainty measure (0-100%)"
False positive	"Incorrect match"
Inference latency	"Processing time"
Training data	"Historical examples the system learned from"
Fine-tuned	"Customized for our data"
Embeddings	"Internal representation" (or omit)
Cosine similarity	"Matching algorithm" (or omit)

Risk Translation

Technical Risk	Governance Risk
Model accuracy 97.2%	"~3% of items may require correction"
False positive rate 0.4%	"~20 per day incorrectly matched, caught in reconciliation"
P95 latency 28s	"95% complete within 30 seconds"

Risk Comparison to Status Quo

Always compare to current state:

yaml

risk_comparison_to_status_quo:
  current_manual_process:
    error_rate: "~2-5% (variable by analyst)"
    failure_modes:
      - "Fatigue-related errors in afternoon"
      - "Inconsistent interpretation across staff"
      - "Peak volume creates backlog"

  proposed_ai_process:
    error_rate: "~3% (consistent)"
    failure_modes:
      - "Edge cases (split fills, new formats)"
      - "Extraction errors on poor quality documents"

  net_risk_change: |
    AI reduces variability and peak-volume risk.
    AI introduces edge case failure modes.
    Overall: Similar error rate with better consistency.

MRM Tier Classification

Per OCC 2011-12 Supervisory Guidance on Model Risk Management:

MRM Tier	Criteria	Requirements
Tier 1	Material financial impact, regulatory reporting, customer decisions	Full validation, ongoing monitoring, annual revalidation
Tier 2	Operational impact, internal decisions, moderate complexity	Documentation, periodic validation, performance monitoring
Tier 3	Low impact, simple models, minimal risk	Documentation, basic testing

yaml

model_risk_management:
  mrm_tier: "Tier 2"
  tier_rationale: |
    - Operational impact (settlement matching)
    - Not customer-facing decision
    - Error detectable in reconciliation
    - Moderate model complexity
  documentation_status: "Submitted - pending review"

Approval Condition Mapping

Every condition must link to a risk:

yaml

approval_conditions:
  - condition: "30-day pilot at 50% volume"
    risk_addressed: "Unknown production behavior"
    verification: "Accuracy metrics during pilot"

  - condition: "Manual fallback tested quarterly"
    risk_addressed: "System unavailability"
    verification: "Documented test results"

  - condition: "Accuracy monitoring with 95% threshold"
    risk_addressed: "Model degradation"
    verification: "Real-time dashboard + alert logs"

Stakeholder-Specific Outputs

CRO One-Pager

•What is it? (2 sentences)
•What's the risk? (top 3 risks with ratings)
•How is risk controlled? (key controls)
•What approval is needed? (specific ask)
•What's the residual risk? (after controls)

Compliance Checklist

Requirement	Status	Evidence
FINRA 4511	✓ Compliant	Decision log design doc
SEC 17a-4	✓ Compliant	WORM storage cert
OCC 2011-12	○ In Progress	MRM submission

Audit Work Program

Test Objective	Procedure	Evidence Required
Verify accuracy	Recalculate from sample	Raw data + calculations
Test routing	Review low-confidence items	Queue logs
Validate controls	Walk through each control	Control documentation

Common Mistakes

Mistake	Why It's Wrong	Do This Instead
Technical jargon	Governance doesn't understand ML	Plain business language
AI risks only	Ignores current state also has risks	Compare to manual process
Generic "compliant"	Doesn't prove compliance	Cite specific requirements
One document for all	Different audiences need different depth	Stakeholder-specific summaries
Conditions without rationale	Why these specific conditions?	Link each to a risk
MRM mentioned but not classified	Unclear what's required	Explicit MRM tier

Red Flags in Your Output

If your documentation has these, it's not ready:

•Technical terms without translation
•No tier classification rationale
•Risk comparison to status quo missing
•Regulations cited without specific requirements
•Controls without effectiveness assessment
•Conditions without risk mapping
•No stakeholder-specific summaries

Financial Services Context

Financial services governance translation requires:

Regulatory Precision

•FINRA Rule 4511 - books and records (6 year retention)
•SEC Rule 17a-4 - electronic records preservation
•OCC 2011-12 - model risk management
•SOX 404 - internal controls (if public company)

Risk Committee Expectations

•Impact/likelihood matrix format
•Residual risk ratings
•Control owner accountability
•Ongoing oversight mechanisms

Audit Readiness

•What evidence exists
•How to test controls
•What sampling approach is appropriate
•What findings would trigger concerns

Governance Package Checklist

Before submission:

• Tier classification with rationale
• Business description in plain language
• Risk comparison to current process
• Control framework with owners and effectiveness
• Regulatory mapping with specific requirements
• MRM tier classification
• Approval conditions linked to risks
• Stakeholder-specific summaries (CRO, Compliance, Audit)
• Audit trail and evidence specified