# RBI mandates kill switch for bank AI models > Source: > Published: 2026-06-24 14:16:58.628177+00:00 # RBI mandates kill switch for bank AI models According to The Economic Times, the Reserve Bank of India has released a draft model risk framework that mandates a **"kill switch"** for AI models used by banks and financial entities. The Economic Times reports the draft requires documented human oversight, customer disclosure when AI is used, and risk management for third-party AI providers. The draft also emphasizes **board-level accountability** and a **risk-based approach** to model oversight, per The Economic Times. The Economic Times article frames the rules as part of broader efforts to tighten model governance in the financial sector. ### What happened According to The Economic Times, the Reserve Bank of India released a draft **model risk framework** for banks and financial entities that mandates implementation of a **"kill switch"** for AI models to allow immediate shutdown in case of errors. The Economic Times reports the draft also requires documented human oversight, disclosure to customers when AI influences decisions, and controls for risks arising from third-party AI providers. The Economic Times further reports the draft assigns **board-level accountability** and prescribes a **risk-based approach** to model oversight. ### Technical details According to The Economic Times, the framework focuses on runtime controls such as the mandated **kill switch**, governance processes for model deployment, and vendor risk management for third-party AI systems. The Economic Times describes the approach as covering lifecycle governance. ### Industry context Editorial analysis: Regulators globally are increasingly codifying operational controls for high-risk AI systems; comparable guidance in other jurisdictions has emphasized human-in-loop controls, vendor due diligence, and executive accountability. For practitioners, this pattern raises the bar on production monitoring, traceability, and contractual controls with AI suppliers. ### Implications for practitioners Editorial analysis: Data science and ML engineering teams in regulated finance will likely need to harden observability, implement safe rollback/runbook procedures, and produce auditable evidence of human oversight and disclosure practices. Industry-standard tooling for real-time monitoring, feature-store lineage, and model explainability will become more relevant in compliance workflows. ### What to watch For practitioners: Monitor the final text of the RBI framework for definitions of covered models, thresholds triggering the kill switch, and specific audit/recordkeeping requirements. Also watch for follow-up guidance or timelines that would affect vendor contracts and deployment roadmaps. ## Scoring Rationale This draft from a major national regulator materially raises compliance requirements for AI in banking, changing operational controls and governance needs for practitioners in financial institutions. Practice with real Banking data 90 SQL & Python problems · 15 industry datasets [Suspicious Online TransactionsEasy](/problems/sql/suspicious-online-transactions) [Delinquent Loans Over 30 DaysMedium](/problems/sql/delinquent-loans-over-30-days) [Credit Card Utilization Risk ReportHard](/problems/sql/credit-card-utilization-risk-report) 250 free problems · No credit card [See all Banking problems](/problems/datasets/banking)