QAG Implementation Program
Enterprise implementation of Quantitative AI Governance (QAG) Framework
3
24 months
2025-01-15
2026-12-31
Conduct AI inventory, risk audit, and establish baseline infrastructure
Duration: 3 months
Start Date: 2025-01-15
End Date: 2025-04-15
Epics
Discover and catalog all existing AI/ML assets and assess risks
User Stories
As a AI Governance Officer, I want to automatically discover all AI models in production so that we have complete visibility of our AI landscape
Story Points: 8
Acceptance Criteria
All production models identified and catalogued
Model metadata captured (owner, type, criticality)
Risk scores calculated for each model
Dashboard visualization available
Tasks
Scan Code Repositories
Implement automated scanning of GitHub/GitLab repos
Effort: 5 days
Subtasks
Configure Repository Scanner
Feature: Repository Model Discovery
Discover ML models in repositories
Access to organization's code repositories
Scanner analyzes repository contents
• All ML model files are identified
• Model types are classified (TensorFlow, PyTorch, etc.)
• Model metadata is extracted and stored
Parse Model Configurations
Feature: Model Configuration Extraction
Extract model parameters
Identified model files
Configuration parser runs
• Model hyperparameters are extracted
• Training data sources are identified
• Model dependencies are mapped
Cloud Platform Discovery
Scan AWS SageMaker, Azure ML, GCP Vertex AI
Effort: 3 days
Subtasks
AWS SageMaker Integration
Feature: Cloud Model Discovery
Discover SageMaker endpoints
AWS credentials with read permissions
API calls to SageMaker are made
• All endpoints are listed
• Model versions are tracked
• Deployment status is captured
As a Risk Manager, I want to implement the 10-dimension risk taxonomy so that we can quantify AI risks consistently
Story Points: 13
Acceptance Criteria
All 10 risk dimensions configured
Quantitative metrics defined for each dimension
Thresholds established (Red/Amber/Green)
Automated scoring implemented
Tasks
Configure Fairness Metrics
Implement demographic parity and equality of opportunity metrics
Effort: 4 days
Subtasks
Demographic Parity Calculator
Feature: Fairness Metric Calculation
Calculate demographic parity
Model predictions and protected attributes
Fairness calculation is triggered
• Demographic parity difference < 0.05
• Results are stored in metrics database
• Alerts triggered if threshold exceeded
Establish cloud infrastructure and development environments
User Stories
As a Platform Engineer, I want to provision scalable cloud infrastructure so that we have a robust platform for AI workloads
Story Points: 8
Acceptance Criteria
Kubernetes clusters deployed
MLOps pipeline configured
Monitoring stack operational
Security controls implemented
Tasks
Deploy Kubernetes Infrastructure
Set up EKS/AKS clusters for AI workloads
Effort: 5 days
Subtasks
Provision EKS Cluster
Feature: Kubernetes Deployment
Deploy production EKS cluster
AWS account with appropriate permissions
Terraform apply is executed
• 3-node cluster is created
• Auto-scaling is configured
• Network policies are applied
Establish AI Governance Office and form implementation teams
User Stories
As a Executive Leadership, I want to establish the AI Governance Office so that we have centralized AI oversight
Story Points: 5
Acceptance Criteria
CAGO hired or appointed
Governance committee formed
Charter document approved
Meeting cadence established
Tasks
CAGO Recruitment
Recruit and onboard Chief AI Governance Officer
Effort: 20 days
Subtasks
Define CAGO Role
Feature: Leadership Establishment
CAGO role definition
Organizational AI governance needs
Job description is created
• Required skills are defined
• Responsibilities are documented
• Reporting structure is established
Implement first three QAG pillars for pilot projects
Duration: 6 months
Start Date: 2025-04-16
End Date: 2025-10-15
Epics
Deploy Pillar 1 - Quantified Risk measurement system
User Stories
As a Risk Analyst, I want to automatically calculate AI risk scores so that we have real-time risk visibility
Story Points: 13
Acceptance Criteria
Unified risk score calculation implemented
RAG status (Red/Amber/Green) automated
Real-time dashboard available
Historical trending enabled
Tasks
Implement Risk Calculation Algorithm
Develop weighted risk scoring algorithm
Effort: 8 days
Subtasks
Weighted Score Calculator
Feature: Risk Score Calculation
Calculate unified risk score
Individual risk dimension scores
Weighted calculation is performed
• Score between 0-100 is generated
• RAG status is determined
• Score is persisted to database
As a Model Owner, I want continuous monitoring of model risks so that we detect issues before they impact production
Story Points: 8
Acceptance Criteria
Real-time metric collection operational
Drift detection implemented
Alerting system configured
SLA compliance tracked
Tasks
Deploy Monitoring Agents
Install and configure monitoring agents on all models
Effort: 5 days
Subtasks
Agent Deployment
Feature: Monitoring Agent Installation
Deploy monitoring agent
Production model endpoint
Agent is deployed as sidecar
• Metrics are collected every 60 seconds
• Data is sent to central monitoring
• Agent health is self-reported
Deploy Pillar 2 - Automated governance enforcement
User Stories
As a Compliance Officer, I want to encode policies as executable rules so that governance is automatically enforced
Story Points: 21
Acceptance Criteria
Policy DSL implemented
Rule engine operational
Pre-deployment gates active
Circuit breakers configured
Tasks
Implement Policy Engine
Deploy Open Policy Agent for policy enforcement
Effort: 10 days
Subtasks
OPA Integration
Feature: Policy Engine Setup
Execute policy validation
Policy rules in Rego format
Model deployment is requested
• Policies are evaluated
• Pass/fail decision is made
• Audit log is created
As a AI Operations Engineer, I want to deploy automated guardian agents so that models are continuously governed
Story Points: 13
Acceptance Criteria
Monitor Agent operational
Auditor Agent scanning models
Enforcer Agent taking actions
Agent coordination verified
Tasks
Deploy Monitor Agent
Implement continuous monitoring agent
Effort: 8 days
Subtasks
Monitor Agent Logic
Feature: Monitor Agent Operation
Detect performance degradation
Model performance metrics
Accuracy drops below threshold
• Alert is generated
• Incident ticket created
• Enforcer agent notified
Deploy Pillar 3 - Single source of truth dashboard
User Stories
As a Executive Stakeholder, I want a unified governance dashboard so that I have real-time visibility of AI risks
Story Points: 13
Acceptance Criteria
Model inventory visible
Risk heat map operational
Drill-down capability (3 clicks)
Mobile responsive design
Tasks
Build Dashboard Frontend
Develop React-based governance dashboard
Effort: 10 days
Subtasks
Portfolio View Component
Feature: Portfolio Risk Visualization
Display risk heat map
Risk scores for all models
Dashboard is loaded
• Heat map shows risk distribution
• Models are color-coded by RAG status
• Click enables drill-down
As a Auditor, I want tamper-proof audit logging so that we have verifiable compliance evidence
Story Points: 8
Acceptance Criteria
Blockchain-based logging implemented
All governance actions logged
Query interface available
Export functionality enabled
Tasks
Implement Audit Logger
Deploy immutable logging system
Effort: 6 days
Subtasks
Blockchain Integration
Feature: Immutable Logging
Log governance action
Governance action occurs
Action is completed
• Log entry is created with timestamp
• Hash is generated and stored
• Previous hash is referenced
Run pilot projects to validate QAG implementation
User Stories
As a Product Owner, I want to pilot Bandroid AI assistant so that we validate the governance framework
Story Points: 21
Acceptance Criteria
Bandroid deployed to test environment
All governance checks passing
Performance metrics met
User acceptance achieved
Tasks
Deploy Bandroid Core
Deploy Bandroid agent with full governance
Effort: 15 days
Subtasks
Agent Deployment
Feature: Bandroid Deployment
Deploy Bandroid with governance
Trained Bandroid model
Deployment pipeline executes
• All pre-deployment checks pass
• Model is deployed to staging
• Monitoring agents attached
• Dashboard shows green status
Enterprise-wide rollout with full 5-pillar implementation
Duration: 15 months
Start Date: 2025-10-16
End Date: 2026-12-31
Epics
Deploy Pillar 4 - Strategic human oversight
User Stories
As a Operations Manager, I want automated escalation for high-risk events so that human experts intervene when needed
Story Points: 8
Acceptance Criteria
Escalation rules defined
Alert routing configured
Response SLAs established
Playbooks documented
Tasks
Configure Escalation Rules
Define and implement escalation thresholds
Effort: 5 days
Subtasks
Escalation Logic
Feature: Automated Escalation
Escalate critical risk
Model risk score exceeds critical threshold
Risk is detected
• Alert sent to on-call engineer
• Incident ticket created
• 15-minute response SLA starts
As a Ethics Board Member, I want systematic ethical review process so that AI systems align with organizational values
Story Points: 5
Acceptance Criteria
Review workflow automated
Board dashboard available
Decision tracking implemented
Impact assessments integrated
Tasks
Ethics Review Workflow
Implement ethical review process
Effort: 4 days
Subtasks
Review Process Automation
Feature: Ethics Review
Submit model for ethics review
New high-risk model
Review is requested
• Review ticket created
• Board members notified
• Documentation attached
• Timeline established
Deploy Pillar 5 - Adaptive governance system
User Stories
As a AI Governance Officer, I want continuous learning from incidents so that governance improves over time
Story Points: 13
Acceptance Criteria
Post-mortem process automated
Learning database established
Policy updates automated
Improvement metrics tracked
Tasks
Incident Learning System
Build automated learning from incidents
Effort: 8 days
Subtasks
Post-Mortem Automation
Feature: Incident Learning
Learn from model failure
Model incident occurred
Post-mortem is conducted
• Root cause identified
• Lessons learned documented
• Policy updates proposed
• Similar models flagged
As a Compliance Manager, I want automatic regulatory updates so that we maintain continuous compliance
Story Points: 13
Acceptance Criteria
Regulatory feed integrated
Auto-translation to policies
Impact analysis automated
Compliance tracking updated
Tasks
Regulatory Parser
Build NLP-based regulatory parser
Effort: 10 days
Subtasks
Regulation Parsing
Feature: Regulatory Updates
Parse new regulation
New EU AI Act update
Parser processes document
• Requirements extracted
• Policies updated
• Affected models identified
• Compliance tasks created
Deploy to high-risk, high-impact models
User Stories
As a Risk Manager, I want all credit models under QAG governance so that we ensure fair lending practices
Story Points: 21
Acceptance Criteria
All credit models inventoried
Fairness metrics implemented
Bias detection operational
Regulatory compliance verified
Tasks
Migrate Credit Models
Onboard all credit scoring models to QAG
Effort: 15 days
Subtasks
Model Migration
Feature: Credit Model Governance
Onboard credit model
Legacy credit scoring model
Migration process executes
• Model registered in inventory
• Fairness metrics calculated
• Monitoring agents deployed
• Compliance verified
Deploy to core business functions
User Stories
As a Marketing Director, I want personalization models governed so that we respect customer privacy
Story Points: 13
Acceptance Criteria
Recommendation engines integrated
Privacy controls implemented
Consent management automated
GDPR compliance verified
Tasks
Integrate Marketing Models
Onboard personalization and recommendation models
Effort: 10 days
Subtasks
Privacy Controls
Feature: Marketing Model Privacy
Enforce privacy constraints
Customer data for personalization
Model makes recommendations
• Consent is verified
• Data minimization applied
• Audit trail created
• Opt-out honored
Deploy unified ONE governance platform
User Stories
As a Platform Architect, I want unified governance platform operational so that all AI assets are centrally managed
Story Points: 34
Acceptance Criteria
Platform infrastructure deployed
All pillars integrated
Multi-tenant capability enabled
Performance SLAs met
Tasks
Deploy ONE Core Services
Deploy core platform services and APIs
Effort: 20 days
Subtasks
Platform Services
Feature: ONE Platform Services
Platform service availability
ONE platform deployed
Health check performed
• All services responding
• 99.9% uptime achieved
• Latency < 100ms
• Scaling verified
As a Integration Engineer, I want Bandroid fully integrated with ONE so that Bandroid operates under full governance
Story Points: 21
Acceptance Criteria
Bandroid APIs connected
Governance policies applied
Monitoring integrated
Performance optimized
Tasks
API Integration
Connect Bandroid to ONE platform APIs
Effort: 12 days
Subtasks
API Connection
Feature: Bandroid-ONE Integration
Bandroid governance integration
Bandroid agent running
Request is processed
• Request logged in ONE
• Policies evaluated
• Response monitored
• Metrics collected
Evolve toward self-governing AI systems
User Stories
As a AI Governance Officer, I want governance system to self-optimize so that it continuously improves without intervention
Story Points: 21
Acceptance Criteria
ML-based optimization implemented
Threshold auto-tuning operational
Performance improvements measured
Human oversight maintained
Tasks
Implement Meta-Governance
Build self-optimizing governance capabilities
Effort: 15 days
Subtasks
Auto-Optimization Logic
Feature: Self-Improving Governance
Optimize risk thresholds
Historical governance data
Optimization algorithm runs
• Optimal thresholds calculated
• False positive rate reduced
• Detection accuracy improved
• Changes logged for audit