Enterprise AI & GenAI Principles (12 Principles)

1. Responsible & Ethical AI

AI/GenAI systems must operate in a way that aligns with organizational values and ethical standards.

Why: Prevent misuse, ensure trust, align with RBI/SEBI expectations.

2. Transparency & Explainability

All AI decisions must be explainable using XAI frameworks (SHAP, LIME, model cards, confidence scores).

Why: Regulatory “Right to Explanation”, audit, risk management.

3. Fairness, Bias Control & Inclusivity

AI/GenAI models must be tested for fairness across gender, age, income, geography.

Why: Prevent discrimination in credit, KYC, compliance, and onboarding.

4. Data Quality, Provenance & Lineage

Every dataset must be cataloged, versioned, and trackable end-to-end.

Why: AI performance depends on data; audit requires lineage.

5. Human-in-the-Loop (HITL) for Critical Decisions

High-impact decisions (credit, onboarding, fraud, compliance) must involve human oversight.

Why: Reduce risk of erroneous AI decisions.

6. Privacy, Security & Confidentiality by Design

No PII should go into GenAI prompts without masking; enforce LLM security patterns.

Why: Prevent data leakage, maintain compliance.

7. Hallucination Control & Output Verification

GenAI outputs must pass quality checks (RAGAS, grounding, moderation, rule checks).

Why: GenAI may hallucinate — architecture must contain guardrails.

8. Model Lifecycle Governance (ML Ops + LLM Ops)

All models must follow standard lifecycle:

• Design & approval

• Training

• Testing

• Deployment

• Monitoring

• Drift detection

• Decommission

Why: Standardize governance across teams & auditors.

9. Prompt Governance & Guardrails

All prompts, system messages, and agent instructions must be versioned and validated.

Why: Prompts directly impact safety, compliance, and hallucination behavior.

10. Cost Efficiency & Token Optimization

GenAI architectures must optimize token usage, caching, content filtering, and model selection.

Why: LLM cost can escalate rapidly in production.

11. Observability, Monitoring & Auditability

Collect and monitor:

• Model accuracy

• Drift

• Bias

• Latency

• Token cost

• Hallucination rate

• Incident logs

• Prompt changes

Why: AI must be fully auditable and observable.

12. Interoperability & Reusability

AI/GenAI architectures must use reusable components:

• RAG pipelines

• Vector store

• Document ingestion pipeline

• Prompt catalog

• Agent orchestration framework

Why: Prevent duplication and accelerate delivery.

✅ Summary

Here is a polished answer:

“Our AI/GenAI principles ensure responsible, explainable, secure, auditable, cost-optimized AI aligned with regulatory compliance, with strong governance on data, models, prompts, and human oversight.”