Reading the RBI's draft AI guidance — implications for engineering teams

Four lines that change everything

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The draft guidance reads simply enough. But each clause carries architectural implications that most teams have not yet confronted.

Regulatory text is compressed. Every word unpacks into engineering decisions.

Clause 1: Explainability requirements

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What this means technically

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Clause 2: Human oversight mandates

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Designing escalation paths

// Example: Human oversight checkpoint
interface OversightCheckpoint {
  decision_id: string;
  ai_recommendation: AIRecommendation;
  confidence_score: number;
  requires_human_review: boolean;
  review_reason?: string;
  escalation_path: EscalationPath;
}

function shouldEscalate(decision: AIDecision): boolean {
  return (
    decision.confidence < THRESHOLD ||
    decision.risk_level === 'high' ||
    decision.amount > AMOUNT_THRESHOLD
  );
}

Clause 3: Data governance obligations

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Data governance is not a policy document. It's an architecture.

Clause 4: Model risk management

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The model card discipline

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What to do now

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1. Audit your current AI systems against each clause
2. Identify gaps in explainability and oversight
3. Build the infrastructure before the guidance becomes mandatory