Summary
- RBI set up an expert committee (chaired by Prof. Pushpak Bhattacharyya, IIT Bombay) which published the FREE-AI report on 13 Aug 2025.
- It rests on 7 Sutras (principles): Trust is the Foundation; People First; Innovation over Restraint; Fairness and Equity; Accountability; Understandable by Design; Safety, Resilience and Sustainability.
- It makes 26 recommendations under 6 pillars: Infrastructure, Policy, Capacity (innovation side) and Governance, Protection, Assurance (risk side).
- Recommendations 14 to 25 are the ones that land on regulated entities (REs): board-approved AI policy, data and model governance, product approval, consumer protection, cyber, red teaming, AI BCP, incident reporting, AI inventory, audit and disclosures.
- Status (as of 2026-10-19): still a committee report. RBI has not issued binding Directions implementing it. The closest follow-up is RBI's draft model risk guidance of 24 Jun 2026, which cites FREE-AI as background and covers AI/ML models; it was not final at the time of checking.
Who it applies to
- Entities regulated by RBI: banks, NBFCs, payment system operators, all-India financial institutions; FinTechs are named in some recommendations (sandbox, incident reporting).
- Many recommendations are addressed to regulators and industry bodies (Recs 1 to 9, 11 to 13, 26), not to REs.
- Not extraterritorial. A foreign AI vendor is reached only indirectly, through the RE's third-party and outsourcing obligations.
Key dates
- RBI announces the committee in its Statement on Developmental and Regulatory Policies
- FREE-AI Committee constituted
- Committee report published on rbi.org.in (Press Release 2025-2026/902)
- RBI issues draft Guidance on Regulatory Principles for Model Risk Management, citing FREE-AI as background (Press Release 2026-2027/528)
- Comment deadline for the draft model risk guidance
Full date record
| Date | Event |
|---|---|
| 2024-12-06 | RBI announces the committee in the Statement on Developmental and Regulatory Policies |
| 2024-12-26 | Committee constituted |
| 2025-08-13 | Report released (Press Release 2025-2026/902) |
| 2026-06-24 | RBI issues draft Guidance on Regulatory Principles for Model Risk Management (Press Release 2026-2027/528), citing FREE-AI |
| 2026-07-24 | Comment deadline on the draft model risk guidance; no final version found as of 2026-10-19 |
Penalties and enforcement
- No penalties in the report. It is a set of recommendations.
- Recommendation 8 proposes a graded liability approach: REs stay liable for customer losses, but supervisors should go easy on a first-time lapse if the RE followed safeguards like incident reporting, audits and red teaming. That leniency should not apply to repeat breaches, gross negligence or failure to fix issues.
- Para 4.4.64: where a customer is harmed, the RE should compensate; reporting an incident should not by itself trigger penal action if corrective steps are timely.
- Enforcement in practice would come later, through RBI Directions or guidance (for example the draft model risk guidance) and supervisory inspection.
What it means for an Indian company
- Banks, NBFCs, payment companies: treat Recs 14 to 25 as the checklist RBI supervisors will reference. Most items reappear in the June 2026 draft model risk guidance (board-approved framework, inventory, red teaming, customer AI disclosure, human switch, kill switch).
- AI vendors selling to BFSI: expect customers to ask for model documentation, red-team results, incident cooperation and audit rights, because REs remain accountable for third-party AI.
- FinTechs partnering with REs: your AI features will be pulled into the RE's product approval and inventory.
- It overlaps heavily with ISO 42001; an AIMS covers much of the governance pillar.
Obligations
Recommendations are addressed to REs unless noted. "Para" numbers are from Chapter 4 of the official report.
| ID | Obligation | Reference | Applies to | Controls |
|---|---|---|---|---|
| RBI-FREEAI-01 | Adopt a board-approved AI policy covering governance, accountability, risk appetite, safeguards, auditability, consumer protection, AI disclosures, model lifecycle and liability | Rec 14; Para 4.4.44 | Deployer | |
| RBI-FREEAI-02 | Classify AI use cases as low, medium or high risk in that policy, and review the classification periodically | Rec 14; Para 4.4.44 | Deployer | |
| RBI-FREEAI-03 | Give the Risk Management Committee (or similar) ownership of AI risk; consider a cross-functional AI adoption committee | Para 4.4.45 | Deployer | |
| RBI-FREEAI-04 | Bring staff use of third-party and off-the-shelf AI tools (such as GenAI apps) under the internal AI policy | Para 4.4.46 | Deployer | |
| RBI-FREEAI-05 | Keep the internal AI policy aligned with national AI governance and regulatory frameworks | Para 4.4.46 | Deployer | |
| RBI-FREEAI-06 | Build AI governance competence at Board and C-suite level and run ongoing AI training for staff who use AI (two to three year glide path for AI expertise) | Rec 10; Paras 4.4.35 to 4.4.36 | Deployer | |
| RBI-FREEAI-07 | Govern AI data across its lifecycle (collection, access, use, retention, deletion) in line with the DPDP Act | Rec 15; Para 4.4.47 | Both | |
| RBI-FREEAI-08 | Use data that is relevant, fairly representative and ethically sourced; keep sensitive data inside controlled environments when using external or open-source models | Para 4.4.47 | Both | |
| RBI-FREEAI-09 | Govern models from design to decommissioning, with documentation of objectives, design, context and benchmarks | Rec 16; Para 4.4.48 | Both | |
| RBI-FREEAI-10 | Validate and periodically test all models, built in-house or bought, before and during use | Rec 16; Para 4.4.48 | Both | |
| RBI-FREEAI-11 | Monitor for drift, degradation, bias and unexplained behaviour, with defined fallbacks | Rec 16; Para 4.4.48 | Deployer | |
| RBI-FREEAI-12 | Before using autonomous AI, set safeguards, testing protocols and SOPs; define what AI may do alone; keep human oversight for medium and high risk tasks | Rec 16; Para 4.4.49 | Deployer | |
| RBI-FREEAI-13 | Put every AI-enabled product through product approval, with AI-specific checks (fairness, bias, understandability, cyber) by a team independent of the developers | Rec 17; Paras 4.4.50 to 4.4.51 | Deployer | |
| RBI-FREEAI-14 | Adopt a board-approved consumer protection framework for AI built on transparency, fairness and recourse | Rec 18; Para 4.4.53 | Deployer | |
| RBI-FREEAI-15 | Tell customers when they are dealing with AI and always offer a switch to a human | Para 4.4.54 | Deployer | |
| RBI-FREEAI-16 | Provide grievance redress for AI interactions and decisions, with easy escalation to human staff | Paras 4.4.54 to 4.4.55 | Deployer | |
| RBI-FREEAI-17 | Run AI only through verifiable channels (for example 1601-series numbers for voice, watermarked or clearly labelled digital interfaces); never falsely claim AI use | Para 4.4.54 | Deployer | |
| RBI-FREEAI-18 | Run awareness campaigns on safe AI use, customer rights and how to complain | Rec 18; Para 4.4.55 | Deployer | |
| RBI-FREEAI-19 | Identify AI-specific security risks (adversarial attacks, data poisoning, model manipulation) before deployment and keep assessing after go-live | Rec 19; Paras 4.4.56 to 4.4.57 | Both | |
| RBI-FREEAI-20 | Be able to shut an AI system down instantly if it risks significant harm | Para 4.4.58 | Deployer | |
| RBI-FREEAI-21 | Red team AI: at least half-yearly for medium and high risk, at least pre-deployment for low risk, plus trigger-based runs; document findings for audit and supervisors | Rec 20; Para 4.4.60 | Both | |
| RBI-FREEAI-22 | Extend BCP to AI failures (including silent degradation), with fallbacks, drills and sample human checks of AI decisions | Rec 21; Paras 4.4.61 to 4.4.62 | Deployer | |
| RBI-FREEAI-23 | Record and report AI incidents promptly through the channel regulators set (indicative form in Annexure VI); compensate harmed customers | Rec 22 (REs and regulators); Paras 4.4.48, 4.4.63 to 4.4.64 | Deployer | |
| RBI-FREEAI-24 | Keep an AI inventory (models, use cases, target groups, dependencies, risk tier, grievances), update it at least half-yearly, and make it available to supervisors | Rec 23; Paras 4.4.68 to 4.4.69 | Deployer | |
| RBI-FREEAI-25 | Run a risk-based AI audit covering input data, model and algorithm, and outputs, aligned to the board-approved risk tiers | Rec 24(a); Paras 4.4.72 to 4.4.74 | Deployer | |
| RBI-FREEAI-26 | Commission independent third-party audits for high-risk or complex AI | Rec 24(b); Para 4.4.74 | Deployer | |
| RBI-FREEAI-27 | Audits should confirm stop, pause or unwind mechanisms, an AI BCP and human override for critical decisions | Para 4.4.74 | Deployer | |
| RBI-FREEAI-28 | Review the AI audit framework at least every two years | Rec 24(c) | Deployer | |
| RBI-FREEAI-29 | Publish AI disclosures in the annual report and website (governance, use areas, consumer protection, complaints handled) | Rec 25; Para 4.4.76 | Deployer | |
| RBI-FREEAI-30 | Optionally use an industry AI compliance toolkit to benchmark fairness, transparency and robustness (voluntary; does not replace internal validation) | Rec 26; Para 4.4.78 | Deployer |
Controls that do the most work here
- SC-AI-037 Internal audit of AI4 obligations
- SC-AI-026 Human oversight and override3 obligations
- SC-AI-001 AI governance policy approved by the board or top management2 obligations
- SC-AI-005 AI risk assessment2 obligations
- SC-AI-016 Independent model validation2 obligations
- SC-AI-020 Performance monitoring and drift detection2 obligations
Evidence an auditor will ask for
- Board resolution approving the AI policy, plus the policy itself (compare with the outline in Annexure V of the report).
- Risk classification method and the current tier of each AI use case.
- Committee charters or minutes showing who owns AI risk.
- AI inventory export with the fields in Para 4.4.68 and a change log showing half-yearly updates.
- Model documentation, validation reports and drift monitoring dashboards.
- Product approval records for AI products, showing the independent evaluator.
- Customer-facing screens or scripts that disclose AI and offer a human option.
- Grievance logs tagged for AI, with resolution outcomes.
- Red-team reports with dates, scope, findings and remediation.
- AI BCP, fallback runbooks and drill results.
- AI incident register and any reports filed.
- AI audit plan, internal and third-party audit reports.
- AI section of the annual report and website.
- Training records for Board, senior management and staff.
Common mistakes
- Calling FREE-AI "in force". It is a committee report; cite it as expected practice and watch for RBI Directions.
- Writing an AI policy that never gets to the Board.
- Leaving staff use of ChatGPT-style tools out of scope of the policy.
- An inventory that only lists in-house models and skips vendor AI, or is never refreshed.
- Red teaming only once before launch, even for high-risk models.
- Chatbots that do not say they are AI or have no route to a human.
- BCP plans that only cover outages and ignore silent model degradation.
- Assuming a vendor's certificate shifts liability; the RE stays responsible.
Glossary
- RE (Regulated Entity): a bank, NBFC or other body supervised by RBI.
- Sutra: a guiding principle; the Sanskrit word means "thread".
- Red teaming: deliberately attacking your own AI to find weaknesses before others do.
- Model drift: a model getting less accurate because real-world data has shifted away from its training data.
- BCP (Business Continuity Plan): the plan for keeping a service running when a system fails.
- Autonomous AI: AI that can act (for example move money) without a human approving each step.
- 1601 series: a phone number series the report names as a verified channel for AI voice interactions with customers.
- EmTech Repository: a repository run by the Reserve Bank Innovation Hub that the report suggests expanding into a sector-wide AI register.
Key takeaways
- A committee report, not a Direction: status is voluntary or expected practice until RBI issues rules.
- 30 RE-facing obligations come from Recs 10 and 14 to 26; they read like an ISO 42001 plus AI security checklist.
- RBI's June 2026 draft model risk guidance already carries several items forward (board framework, inventory, red teaming, AI disclosure, human switch, kill switch), so preparing now is low regret.
Official sources
- FREE-AI Committee Report, RBI publications page (13 Aug 2025), accessed 2026-10-19
- FREE-AI report PDF, accessed 2026-10-19
- Annexure VI, AI Incident Reporting Form (indicative), accessed 2026-10-19
- RBI Press Release 2025-2026/902, report released, accessed 2026-10-19
- RBI Press Release 2026-2027/528, draft Guidance on Regulatory Principles for Model Risk Management, accessed 2026-10-19
- Draft model risk guidance text, accessed 2026-10-19
This is general information, not legal advice. Last verified 19 Oct 2026 against the official source. Laws change; confirm the current position with qualified counsel before acting.