EA Governance Enhancement to enable AI/ML & GenAI

As Enterprise Architect , would extend the existing EA governance by embedding AI/ML and GenAI governance within the same strategic, tactical, and operational layers — not as a parallel body but as an integrated stream. This includes setting up an AI Ethics Subcommittee, AI/ML/Gen AI CoE under the tactical layer, AI reviewers in SARB, and BU AI champions for federated execution. We’d define clear RACI mappings, conduct stakeholder workshops, and ensure Responsible AI principles are enforced through architecture reviews and lifecycle governance.”

──────────────────────────────────────────────

STRATEGIC LAYER

──────────────────────────────────────────────

• Steering Committee

AI Strategy & Ethics Subcommittee

- Defines AI/ML & GenAI strategy aligned with business goals

- Approves Responsible AI principles (fairness, transparency, accountability)

- Oversees AI ethics, regulatory & risk compliance

- Allocates funding for strategic AI initiatives

• EA Office / EA Lead

- Owns enterprise-wide AI governance integration

- Appoints AI/ML Architecture Lead

- Monitors alignment of AI/ML roadmap with enterprise strategy

- Reports AI progress to Steering Committee

↓ (Strategy → Standards & Frameworks)

Metrics / KPIs:

• % of AI initiatives aligned with business KPIs

• Responsible AI compliance score

• AI maturity index across BUs

──────────────────────────────────────────────

TACTICAL LAYER

──────────────────────────────────────────────

• EARB (Enterprise Architecture Review Board)

- Includes AI/ML reviewers for solution assessments

- Ensures architecture consistency, reusability, and Responsible AI compliance

✅ (A) AI Governance Board

Members: EA, Data Science, Legal, Risk, InfoSec, Business, Compliance.

Reviews:

• AI solution architecture

• Model lifecycle

• Data risks

• Regulatory compliance (RBI, SEBI, GDPR-like)

• Audit readiness

✅ (B) Model Review Board (MRB)

Reviews every model:

• Training datasets

• Model architecture

• Versioning

• Drift monitoring

• Bias, fairness

• Explainability reports

✅ (C) Prompt Engineering Review Board (PERB)

Required for GenAI use cases:

• Prompt templates

• System prompts

• Guardrails

• Hallucination checks

• Evaluation benchmarks (BLEU, ROUGE, RAGAS, etc.)

• CoEs (Centers of Excellence)

- Platform / Cloud / DevOps CoE

→ Support AI workloads, MLOps pipelines, scalable infra

- Data CoE

→ Manages data governance, lineage, and model training datasets

- **AI/ML & GenAI CoE (New)**

→ Defines AI reference architectures & technology standards

→ Recommends approved AI platforms & frameworks (Azure ML, LangChain, etc.)

→ Defines lifecycle governance (model registry, retraining, drift monitoring)

→ Partners with Data & Security CoEs for privacy & compliance

• Technology Council

• Extend EA Meta-Model

  • Add AI capabilities, model cards, prompt catalog, vector DB, and data lineage views.

  • You add new artifacts to your EA repository:

    ✅ New Baseline & Target AI Artifacts

    • AI capability map

    • AI operating model

    • AI data catalog

    • Model cards

    • Feature store catalog

    • Prompt catalog

    • RAG architecture blueprint

    • GenAI patterns (Chatbot, Summarization, Classification, RAG, Agents)

    ✅ New Technology Reference Models

    • Azure OpenAI / AWS Bedrock

    • Vector DB (PGVector, OpenSearch, Redis Vector)

    • Feature Store (Feast)

    • Model registry (Azure ML, MLflow)

    • Pipeline & MLOps (Airflow, Azure ML Ops)

- Evalaute/assese/approve AI Standard,Principles,framework

• AI Principles

  • ✅ Fairness & Bias Control

    Models must be tested across gender, age, income, geography with context-aware rules.Example:

    • Loan approval is risk-based, not equality-based.

    • If a low-income customer applies for a high loan amount → rejection is risk-based, not bias.

    • Fairness checks ensure:

      • model did not reject because of gender, age, region, surname, demographic tags

      • model explains decisions based on financial features (income, obligations, credit history)

    ✅ Explainability & Auditability

    Every model decision must have an audit trail & human-readable explanation (SHAP, LIME).

    ✅ Security & Data Privacy First

    PII anonymization, encryption, governance aligned to RBI/SEBI guidelines.

    ✅ Human-in-the-Loop for Critical Decisions

    Used especially for credit, fraud, compliance, and disputes.

    ✅ Responsible Use of GenAI

    • No hallucinations for regulated content (loans, KYC, compliance).

    • Guardrails + policy-based filtering.

  • Technology Standards & Blueprints

    You define approved architecture for:

    ✅ RAG Blueprint

    • Document Ingestion

    • Chunking

    • Embeddings

    • Vector DB

    • Guardrails

    • Audit & observability

    ✅ LLM & Model Hosting Standards

    • Serverless model APIs

    • Azure ML endpoints

    • Model monitoring

    • Cost monitoring

    ✅ GenAI Integration Patterns

    • Prompt-chaining

    • Multi-Agent patterns

    • Token usage governance

    • Data masking rules for prompts

    ✅ ML Pipeline Standards

    • Feature pipelines

    • Training pipelines

    • Deployment pipelines

    • Drift monitoring

- Explores GenAI use cases, LLMOps, AI-assisted engineering

- Defines integration standards for AI APIs, embeddings, and agent frameworks

↓ (Standards → Implementation & Review)

Metrics / KPIs:

• % of AI projects using approved architecture patterns

• Reusability index of AI components/models

• % of AI models with explainability and bias audits

──────────────────────────────────────────────

OPERATIONAL LAYER

──────────────────────────────────────────────

• SARB (Solution Architecture Review Board)

- Reviews all AI/ML & GenAI solutions before production

- Verifies adherence to ethical and technical guardrails

- Validates reusability and alignment with enterprise reference architectures

• Domain Architects / BU Leads

- Identify and drive domain-specific AI use cases (KYC, Fraud, Risk Scoring)

- Ensure alignment with enterprise AI/ML standards

- Collaborate with AI CoE for reusable components

• Project Architects

- Design and implement AI-driven solutions using approved frameworks

- Enforce model explainability, bias testing, and governance checkpoints

- Ensure audit trails for all model-related decisions

↓ (Implementation → Monitoring & Feedback)

 Add→ MLOps + LLMOps

Your governance must create an ML + GenAI lifecycle, not just SDLC.

✅ Add 3 Pipelines

1. Data pipeline

2. Model pipeline

3. Prompt/LLM pipeline

✅ Add CI/CD Checks

• Bias test → must pass

• Explainability → must generate SHAP report

• Security → prompt injection tests

• Hallucination test → RAGAS score ≥ 80

• Data privacy → PII redaction

Metrics / KPIs:

• % of solutions passing AI compliance reviews

• Audit readiness score (documentation, lineage)

• Model drift incidents detected & remediated

──────────────────────────────────────────────

FEDERATED GOVERNANCE MODEL

──────────────────────────────────────────────

• BU EA Committees

- Include BU AI Champions for localized governance

- Prioritize AI/ML initiatives within their business unit

- Ensure compliance with central AI/ML CoE frameworks

- Provide continuous feedback to EA Office for improvements

↓ (Execution → Continuous Improvement)

──────────────────────────────────────────────

SUPPORTING GOVERNANCE ELEMENTS

──────────────────────────────────────────────

• **Workshops & Alignment**

- Conduct multi-level workshops to clarify new AI roles, responsibilities, and controls

- Update EA Charter and Governance Operating Model

• **RACI Matrix**

- Maps accountability across layers (Steering Committee, EA Office, CoEs, SARB, BU EAs)

• **Metrics & Continuous Improvement**

- AI Governance Scorecard: adoption, compliance, model reuse, audit readiness

- AI Maturity Index: capability evolution, drift management, Responsible AI adoption

- Quarterly reviews with Steering Committee to refine standards and principles

──────────────────────────────────────────────

Let’s take your existing EA governance setup (Strategic, Tactical, Operational layers) and enrich it for AI/ML + GenAI enablement — step by step.

🔷 1. Objective

Enhance existing EA Governance to:

• Govern AI/ML and GenAI initiatives under existing enterprise structures.

• Ensure ethical, secure, compliant, explainable, and value-driven AI adoption.

• Integrate AI governance with business, data, and technology governance.

🔷 2. Updated EA Governance Structure (with AI/ML + GenAI)

Strategic Layer: Steering Committee, EA Office / EA Lead

New inclusions:

• AI Strategy & Ethics Subcommittee under Steering Committee.

• Members: CIO, CDO, CISO, Head of AI/ML, Chief Risk Officer, EA Lead.

• Charter:

  • Define enterprise-wide AI/ML & GenAI strategy aligned with business goals.

  • Approve AI principles (Responsible AI, fairness, transparency, accountability).

  • Define AI Risk and Compliance framework (e.g., bias testing, model explainability, GDPR/DPDP adherence).

  • Allocate budget and approve high-value AI initiatives.

• EA Office adds AI/ML Architecture Lead role responsible for AI tech radar and capability alignment.

Tactical Layer: EARB, CoEs (Platform, Cloud, DevOps, Data, AI/ML), Technology Council

New inclusions:

• Establish AI/ML & GenAI CoE (Center of Excellence) under Tactical layer.

  • Responsible for:

    • Defining AI reference architectures, MLOps/LLMOps blueprints.

    • Recommending AI platforms, frameworks, and tools (Azure ML, Vertex AI, AWS Sagemaker, LangChain, VectorDB, etc.).

    • Setting AI solution review checkpoints as part of EARB review process.

    • Evaluating AI model lifecycle governance (training data lineage, drift monitoring, retraining policy).

    • Coordinating with Security & Data CoEs for privacy, PII handling, and secure model deployment.

• Technology Council includes GenAI innovation stream.

  • Responsible for exploring AI-assisted engineering (e.g., Copilot), LLM integration, and AI agent frameworks.

  • Define integration standards for AI APIs, embeddings, and RAG services.

Operational Layer: SARB, Domain Architects, BU Leads, Project Architects

New inclusions:

• SARB (Solution Architecture Review Board) must include an AI/ML Reviewer for:

  • Reviewing AI model architecture, ethical compliance, and data sourcing.

  • Ensuring reusability of models and alignment with enterprise data platform.

• Domain Architects / BU Leads:

  • Identify domain-specific AI use cases (e.g., Credit scoring, Fraud detection, KYC automation).

  • Map each use case to approved AI components and model repositories.

• Project Architects:

  • Apply approved AI patterns (e.g., RAG, multi-agent collaboration, AI API gateway).

  • Ensure all AI implementations adhere to Responsible AI principles and are auditable.

Federated Model: BU EA Committees

New inclusions:

• Each BU EA Committee includes a BU AI Champion.

• Responsible for:

  • Local AI use case intake and prioritization.

  • Alignment with central AI CoE frameworks and reusable components.

  • Providing feedback to central EA office on domain-specific AI challenges.

🔷 3. Stakeholder Workshops

Conduct workshops with:

• Steering Committee → Align on AI principles and risk.

• EA/Tactical Teams → Define AI architectural standards and patterns.

• BU Leaders → Identify and prioritize business AI opportunities.

• Data Governance Team → Ensure compliance with Responsible AI and privacy laws.

Deliverables:

• Updated EA Charter to include AI/ML/GenAI.

• Updated Governance Operating Model.

• AI/ML Decision Flow (from use case ideation to production approval).

🔷 4. Updated RACI Matrix (Sample Snapshot)

Governance Body	Key Responsibility	Accountable (A)	Responsible (R)	Consulted (C)	Informed (I)
Steering Committee	Approve AI/GenAI strategy & ethics charter	CIO/CDO	EA Lead	CRO, CISO	All BUs
EA Office	Define AI governance model & standards	EA Lead	AI/ML Arch Lead	Data CoE	Project Teams
AI/ML CoE	Define tools, frameworks, reference architectures	EA Lead	CoE Head	DevOps, Data, Cloud CoEs	BU Architects
SARB	Review AI solutions for compliance	EA Lead	AI Reviewer	Project Architects	BU Leads
BU EA Committee	Identify local AI use cases	BU Head	BU AI Champion	Domain Architects	EA Office

🔷 5. Integration with Existing Governance

• No disruption to existing EA layers; AI governance is embedded as new sub-committees and roles.

• EA Repository updated to include:

  • AI/ML architecture blueprints

  • Model lifecycle templates

  • AI/ML data lineage and fairness audit checklists

• Add AI architecture review gates in the EA lifecycle.

🔷 6. Continuous Improvement

• Quarterly review of AI/ML projects via EA scorecard (value realized, bias audit results, drift incidents).

• Update standards based on regulatory changes (e.g., EU AI Act, RBI/SEBI AI advisories).

• Measure adoption via AI Maturity Index (governance, reuse, automation).

5️⃣ Responsible AI & GenAI Principles

These become part of the EA Principles Library, just like “Cloud-First” or “APIs over Integrations.”

Principle	Description
Fairness	AI systems must be free from unjust bias related to gender, age, income, or geography. Business logic (e.g., credit scoring) must be explainable and data-driven, not discriminatory.
Transparency	All AI/ML and GenAI decisions must be explainable and traceable; model lineage and versioning must be auditable.
Accountability	Clear ownership for model outcomes — human oversight must be defined for all critical AI decisions.
Privacy & Security	Protect PII and sensitive data during training, inference, and prompt interactions. Use enterprise DLP and tokenization.
Explainability	Business users should be able to understand why a model or LLM produced a given outcome.
Reliability & Robustness	AI/GenAI systems must operate consistently under normal and adverse conditions; drift detection must be in place.
Ethical Use	Models must comply with enterprise ethical policies and not generate harmful or inappropriate content.

RACI Matrix – EA Governance for AI/ML & GenAI

#	Key Responsibility	Steering Committee	EA Office / EA Lead	EARB	AI/ML & GenAI CoE	Data / Cloud / DevOps CoEs	Technology Council	SARB	Domain / BU Architects	Project Architects	BU EA Committee / AI Champion
1	Define AI/ML & GenAI strategy aligned with business goals	A	R	C	C	C	C	I	I	I	I
2	Approve Responsible AI principles (Fairness, Transparency, Ethics)	A	R	C	C	C	C	I	I	I	I
3	Establish AI Governance Operating Model	C	A/R	C	R	C	I	I	I	I	I
4	Define AI Architecture Standards & Reference Blueprints	I	A	C	R	C	C	I	I	I	I
5	Define AI/ML Platform, Framework, and Tooling Standards	I	C	C	A/R	R	C	I	I	I	I
6	Review and approve AI/ML solution architectures	I	C	A/R	C	C	I	R	C	C	I
7	Define AI Model Lifecycle Governance (training, retraining, drift)	I	C	C	A/R	R	I	C	C	I	I
8	Define AI/ML Risk, Bias, and Compliance Framework	A	R	C	R	C	C	I	I	I	I
9	Manage AI Data Governance (lineage, consent, security)	I	C	C	C	A/R	I	I	C	I	I
10	Evaluate AI/ML Infrastructure Readiness (MLOps/LLMOps)	I	C	C	R	A/R	I	I	I	I	I
11	Run AI Solution Architecture Reviews (SARB Checkpoints)	I	I	C	C	C	I	A/R	C	C	I
12	Identify and Prioritize AI Use Cases per BU	I	I	I	C	I	I	I	A/R	R	C
13	Ensure Responsible AI Compliance in Implementation	I	C	C	C	C	I	A/R	C	R	I
14	Conduct Fairness / Bias / Explainability Testing	I	I	C	A/R	R	I	C	C	R	I
15	Maintain Enterprise AI Model Registry and Repository	I	C	I	A/R	C	I	I	C	R	I
16	Monitor AI Model Performance and Drift	I	C	I	A/R	R	I	C	C	R	I
17	Define Security & Privacy Controls for AI/GenAI	I	C	C	R	A/R	I	C	I	I	I
18	Drive GenAI Innovation and Pilot Programs	I	C	I	R	I	A/R	I	C	R	C
19	Manage AI Talent, Training, and Awareness Programs	I	A/R	I	R	C	C	I	I	I	I
20	Federated Governance and Feedback Loop	I	A/R	I	C	C	I	I	C	I	R

Legend

• A (Accountable) → Final authority, owns the outcome.

• R (Responsible) → Performs the work or drives execution.

• C (Consulted) → Provides inputs or subject matter expertise.

• I (Informed) → Needs to be kept aware of decisions or progress.

Interpretation

“This RACI matrix ensures every governance layer has clear accountability for AI strategy, standards, compliance, and execution.” “Strategic defines direction; Tactical defines guardrails; Operational ensures compliance; Federated scales adoption across business units.” “AI/ML CoE and EA Office are jointly accountable for standards and lifecycle governance, while SARB enforces compliance before go-live.” “Domain and Project Architects are key for responsible implementation, ensuring bias-free, explainable AI aligned to enterprise standards.”

Governance Decision Flow

How AI use case moves from ideation → approval → build → review → production → monitoring)?

It shows how an AI use case progresses through each governance layer — from idea to production and continuous monitoring — mapped to the committees and roles we defined earlier.

EA Governance Decision Flow – AI/ML & GenAI

──────────────────────────────────────────────────────────────
      1️⃣  USE CASE IDEATION & BUSINESS ALIGNMENT
──────────────────────────────────────────────────────────────
[ Business Unit / Domain Team ]
   ↓
• Identify potential AI/ML or GenAI use cases (e.g., KYC automation, fraud prediction)
• Perform initial business value and feasibility assessment
• Engage BU AI Champion for validation
• Submit AI Use Case Proposal to EA Office

Governance Involved:
→ BU EA Committee (Responsible)
→ BU AI Champion (Responsible)
→ EA Office (Consulted)
→ Steering Committee (Informed)
──────────────────────────────────────────────────────────────


──────────────────────────────────────────────────────────────
      2️⃣  STRATEGIC REVIEW & PRIORITIZATION
──────────────────────────────────────────────────────────────
[ EA Office + Steering Committee ]
   ↓
• Evaluate proposal against enterprise AI strategy and principles
• Assess alignment with Responsible AI framework and compliance
• Approve funding and assign sponsor
• Route approved initiatives to Tactical Governance for architecture definition

Governance Involved:
→ Steering Committee (Accountable)
→ EA Office / EA Lead (Responsible)
→ Data CoE / Risk / Legal (Consulted)
──────────────────────────────────────────────────────────────


──────────────────────────────────────────────────────────────
      3️⃣  ARCHITECTURE DESIGN & STANDARDIZATION
──────────────────────────────────────────────────────────────
[ Tactical Layer – EARB + AI/ML CoE + Data/Cloud CoEs ]
   ↓
• Define AI solution architecture and model lifecycle
• Validate platform, frameworks (Azure ML, LangChain, VectorDB)
• Ensure reusability of models and components
• Approve architecture blueprint for implementation
• Record decision and architecture artifacts in EA repository

Governance Involved:
→ EARB (Accountable)
→ AI/ML CoE (Responsible)
→ Data, Cloud, DevOps CoEs (Consulted)
→ EA Office (Informed)
──────────────────────────────────────────────────────────────


──────────────────────────────────────────────────────────────
      4️⃣  SOLUTION IMPLEMENTATION & REVIEW
──────────────────────────────────────────────────────────────
[ Operational Layer – SARB + Project / Domain Architects ]
   ↓
• Develop AI models and integrate with approved enterprise components
• Conduct fairness, bias, and explainability testing
• Review solution architecture through SARB checkpoints
• Ensure alignment with Responsible AI, data, and security policies

Governance Involved:
→ SARB (Accountable)
→ Project Architects (Responsible)
→ Domain / BU Architects (Consulted)
→ AI/ML CoE (Consulted)
──────────────────────────────────────────────────────────────


──────────────────────────────────────────────────────────────
      5️⃣  DEPLOYMENT & PRODUCTION MONITORING
──────────────────────────────────────────────────────────────
[ Operational + Tactical Governance ]
   ↓
• Deploy AI/ML models through approved MLOps / LLMOps pipelines
• Register model in enterprise model registry
• Enable observability (drift, performance, retraining triggers)
• Report to EA Office for quarterly compliance review

Governance Involved:
→ AI/ML CoE (Accountable)
→ DevOps / Data CoE (Responsible)
→ SARB (Consulted)
→ EA Office (Informed)
──────────────────────────────────────────────────────────────


──────────────────────────────────────────────────────────────
      6️⃣  FEEDBACK & CONTINUOUS IMPROVEMENT
──────────────────────────────────────────────────────────────
[ Federated + Strategic Governance ]
   ↓
• Collect feedback from BU teams, data scientists, and compliance officers
• Evaluate model performance, drift, and ethical impact
• Update AI standards, architecture blueprints, and RACI roles as needed
• Present improvement summary to Steering Committee quarterly

Governance Involved:
→ EA Office (Accountable)
→ BU EA Committees (Responsible)
→ AI/ML CoE (Consulted)
→ Steering Committee (Informed)
──────────────────────────────────────────────────────────────

✅ Key Talking Points (for interview):

“This flow ensures that AI/ML and GenAI initiatives follow the same disciplined EA governance pipeline as any enterprise initiative — but with added checkpoints for fairness, bias, explainability, and compliance.” “Each stage has a clear accountable body — Steering for strategy, EARB for architecture, SARB for solution assurance, and AI/ML CoE for lifecycle governance.” “This model promotes federated innovation while maintaining centralized control — empowering BUs to innovate responsibly within enterprise guardrails.”

🧪 AI Testing within EA Governance Framework

Testing AI models isn’t just about accuracy — it’s about trust, fairness, robustness, and regulatory compliance.We test at three levels:

1️⃣ Model Development Stage

2️⃣ Model Integration / Pre-Production Stage

3️⃣ Model Monitoring / Production Stage

1️⃣ Model Development Stage (AI/ML / GenAI Lab Testing)

Purpose:

Ensure that the model behaves as intended — accurate, fair, unbiased, explainable.

Key Tests & Validation Areas:

Test Type	Description	Performed By
Data Quality & Bias Testing	Check for unbalanced datasets (e.g., gender, region, age). Use tools like Fairlearn, IBM AI Fairness 360.	AI/ML CoE, Data Science Team
Feature Correlation Audit	Ensure model doesn’t use proxy variables that cause bias (e.g., ZIP code indirectly encoding ethnicity).	Data Science / Model Risk
Model Accuracy & Precision	Validate using train/test/validation splits, cross-validation, confusion matrix.	AI CoE
Explainability (XAI)	Validate model interpretability using LIME, SHAP, or Explainable Boosting Machine.	AI CoE + EARB Reviewer
Security Testing	Ensure model endpoints and data pipelines are secure from prompt injection, data leakage, or adversarial attacks.	Security CoE
Ethical Testing (GenAI)	For GenAI (LLMs, RAG): validate for hallucination rate, prompt safety, and PII leakage using red-teaming and safe prompt templates.	GenAI CoE + Security

✅ Governance Checkpoint:EARB reviews test evidence before approving model promotion to pre-production.

2️⃣ Pre-Production / Integration Testing Stage

Purpose:

Validate the AI system within the full enterprise ecosystem — integration, performance, guardrails, and explainability in end-to-end flows.

Test Type	Description	Performed By
Integration Testing	Validate end-to-end flow — API gateway, data lake, feature store, model registry, MLOps pipeline.	DevOps & Cloud CoE
Performance & Scalability	Load tests for real-time inference, batch scoring latency, and concurrency.	Engineering / AI Platform Team
Explainability in Business Context	Ensure model outputs are understandable by business reviewers (human-in-the-loop).	EARB / BU SMEs
Guardrail Validation (GenAI)	Validate LLM guardrails — prompt moderation, toxicity filter, contextual grounding, content policy adherence.	GenAI CoE
Audit Trail Readiness	Validate model lineage and version tracking in model registry (MLflow, Azure ML).	AI/ML CoE

✅ Governance Checkpoint:SARB signs off at the solution level before go-live, ensuring compliance with EARB-approved architecture and Responsible AI principles.

3️⃣ Production Stage (Model Monitoring & Governance)

Purpose:

Continuously monitor for model drift, data drift, bias emergence, security, and regulatory compliance.

Test / Monitor Type	Description	Performed By
Model Drift Detection	Detects performance degradation over time — accuracy drop, concept drift.	AI Ops / AI CoE
Bias & Fairness Re-Testing	Regularly re-evaluate model outcomes by segment (gender, geography, income).	Model Risk Management
Explainability Dashboards	Provide business stakeholders visibility into model behavior and rationale.	AI CoE / Reporting
Adversarial Testing	Simulate malicious or manipulated inputs (prompt injection, adversarial examples).	Security CoE
Regulatory Audit Review	Prepare audit evidence — dataset lineage, retraining records, risk reports.	Compliance / Risk
GenAI Prompt Safety Monitoring	Monitor user prompts and responses for policy violations, PII exposure, toxicity.	GenAI Ops + Security

✅ Governance Checkpoint:EARB & Technology Council review quarterly model health reports and compliance dashboards to ensure Responsible AI adherence.

🎯 Tooling Stack for AI Testing ()

Testing Area	Tools / Frameworks
Data Quality & Bias	Fairlearn, AI Fairness 360, TensorFlow Data Validation
Explainability	SHAP, LIME, EBM, Captum
Drift Detection	Evidently AI, WhyLabs, Azure ML Monitor
Model Registry / Lineage	MLflow, Azure ML Model Registry
Security & Robustness	Microsoft Presidio, Guardrails AI, Rebuff (for prompt injection)
GenAI Testing	Prompt Flow (Azure), LangSmith (LangChain), Traceloop, Bedrock Guardrails
MLOps/LLMOps Pipelines	Azure ML, Kubeflow, Databricks MLflow, Jenkins / GitHub Actions

🧭 Governance Integration

Layer	What Happens Here
Technology Council	Defines AI testing policies, approved tools, and Responsible AI framework.
EARB	Reviews AI testing evidence during design-time (model card, test results, explainability proof).
SARB	Validates test execution and sign-off during deployment.
AI CoE / GenAI CoE	Executes AI/ML and GenAI testing frameworks, maintains testing automation pipelines.

🧩 Summary

“AI testing is part of our end-to-end governance lifecycle.During model development, we test for fairness, bias, explainability, and robustness using standardized frameworks like Fairlearn and SHAP.Before deployment, we validate integration, guardrails, and performance via the MLOps pipeline.Once in production, we continuously monitor for model drift, fairness violations, and hallucinations in GenAI systems.All test evidence is reviewed by EARB and SARB to ensure compliance with our Responsible AI principles — fairness, transparency, and accountability.”