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Governance Processes — Solution Lifecycle & Responsible AI Framework

Full governance pipeline: proposal → responsible AI review → hallucination audit → FinOps → stage-gate progression → continuous monitoring · 10+ regulatory licences

1 Solution lifecycle management
Ideation & proposal
Business case document
Problem statement
AI suitability assessment
Data availability check
ROI projection
POC phase
Scope definition (4-6 wks)
Success criteria (KPIs)
Data subset validation
Technical feasibility
Stakeholder demo
POC success rate: 68%
MVP phase
Production data (limited)
User testing (N=100)
Compliance review
Performance benchmarks
Feedback integration
MVP → PROD: 72%
Production phase
Full deployment
SLA enforcement
24/7 monitoring
Incident runbooks
Continuous improvement
Retirement criteria
Performance degradation
Regulatory change
Cost exceeds value
Superseded by new solution
Data dependency removed
2 Responsible AI — Bias, fairness & ethics
Bias testing
Disparate impact analysis
Protected class testing
Demographic parity
Equalized odds
Calibration across groups
Fairness metrics
Statistical parity difference
Equal opportunity diff
Theil index
Four-fifths rule (EEOC)
Intersectional analysis
Protected attributes
Race / ethnicity
Gender / sex
Age
National origin
Religion
Remediation
Pre-processing (resampling)
In-processing (constraints)
Post-processing (threshold)
Human override capability
Adverse action explanations
Ethics board
Quarterly review
External advisory panel
Community feedback
Whistleblower channel
Public transparency report
3 Hallucination audit & accuracy framework
Detection methods
Ground truth comparison
Self-consistency check
Cross-reference validation
Confidence calibration
Human evaluation sample
Domain-specific checks
Financial figures accuracy
Interest rate verification
Credit score validity
Regulatory reference check
Date/deadline accuracy
Benchmarking
Monthly eval set (500 queries)
Domain expert scoring
Automated fact-check pipeline
Regression comparison
Model-to-model comparison
Metrics & thresholds
Hallucination rate
< 0.5%
Factual accuracy
> 99%
Citation accuracy
> 95%
Financial accuracy
100%
Remediation
Immediate: block response
Short-term: prompt fix
Medium-term: fine-tune
Long-term: model swap
Disclosure to affected users
4 FinOps — AI cost management & optimization
Cost visibility
Per-model cost tracking
Per-service attribution
Per-user cost (marginal)
Token usage dashboards
Trend analysis (weekly)
Budget controls
Daily budget: $1,200
Monthly budget: $35K
Alert at 80% burn
Auto-throttle at 95%
Emergency override (CTO)
Optimization levers
Prompt compression
Semantic caching (23% hit)
Model downgrade (non-critical)
Batch processing (off-peak)
Output length limits
Savings: 34% vs naive
Unit economics
Cost/conversation
$0.08
Cost/credit check
$0.12
Cost/remittance
$0.04
Revenue/cost ratio
18:1
Forecasting
User growth → cost projection
Model price changes
Feature launch impact
Quarterly budget planning
Board reporting (monthly)
5 Regulatory compliance & foundational model governance
Financial regulations
OCC (national bank)
CFPB (consumer)
State regulators (50)
FDIC (deposit)
SEC (securities)
AI-specific regulation
EU AI Act (high-risk)
NIST AI RMF
NYC Local Law 144
Colorado SB 205
White House AI EO
Model governance
Model inventory registry
Version control (all models)
Performance monitoring
Drift detection (weekly)
Re-training governance
Audit readiness
SOC 2 Type II (annual)
PCI-DSS (quarterly)
State exam prep
Model risk (SR 11-7)
Evidence auto-collection
Incident & disclosure
Breach notification (72h)
Model failure disclosure
Regulatory filing
Customer notification
Root cause → remediation
6 Continuous governance monitoring & reporting
Real-time dashboards
Model performance (live)
Fairness metrics (daily)
Cost burn rate
Compliance status
Risk heatmap
Alerting framework
P0: Bias detected
P0: Hallucination spike
P1: Cost anomaly
P2: Drift warning
P3: Scheduled review due
Review cadence
Daily: automated checks
Weekly: team review
Monthly: exec report
Quarterly: board review
Annual: external audit
Stakeholder reporting
Board: AI risk report
Regulators: compliance
Engineering: tech metrics
Product: user impact
Finance: cost/ROI
Governance tools
Model cards (standardized)
Data sheets (every dataset)
Impact assessments
Decision logs (immutable)
RACI matrix (all solutions)