RBI FREE-AI and India's AI governance guidelines

India has chosen not to pass a single, horizontal AI statute for now. The government's position, set out in MeitY's India AI Governance Guidelines, is that most AI risks can be handled under existing laws, applied by the regulators that already supervise each sector, supported by common principles and new institutions. For banks, NBFCs and other regulated entities, that means the Reserve Bank of India sets the expectations, and its main AI document is the FREE-AI committee report.

This matters for how you read the documents. Neither FREE-AI nor the MeitY guidelines is a law you can be fined under directly. But a supervisor that has published a framework will examine regulated entities against it, and the binding obligations that already exist, in data protection, cyber security, KYC and outsourcing, apply in full when AI is involved.

On 13 August 2025 the Reserve Bank published the report of its committee on a Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI). It is organised in three layers.

Some recommendations build shared infrastructure, such as an AI innovation sandbox for the financial sector. Others are addressed to regulated entities directly: board-approved policies, governance across the AI life cycle, consumer protection, cyber security, incident reporting and audit. Recommendation 8 proposes a graded liability approach: regulated entities remain liable for customer losses, but supervisors may take a more tolerant view of a first-time lapse where the entity followed sound safety practice such as incident reporting, audits and red teaming. Recommendation 18 asks each regulated entity for a board-approved consumer protection framework with accessible recourse.

Read across the governance, protection and assurance pillars, the report points to a familiar set of controls. For a bank or NBFC deploying AI, including agents, they translate as follows.

• A board-approved AI policy that defines permitted uses, risk appetite and accountability, with named senior owners.
• An inventory of AI systems, including vendor and embedded models, with their purpose, data and risk tier.
• Risk-based controls by tier: more testing, monitoring and human involvement as the impact on customers grows.
• Human oversight and accountability: a person answerable for each AI-assisted decision, and the ability to intervene.
• Consumer protection: telling customers when they are dealing with AI, explaining outcomes in terms they understand, and a grievance route that reaches a person.
• Assurance: independent audit, monitoring for drift and bias, and reporting of AI incidents.

Each item needs evidence, not only a policy: an inventory you can produce, records of decisions and overrides, and audit reports. The evidence table later in this guide lists what to keep.

Agents raise the stakes because they act rather than advise. A model that suggests a collections message is one thing; an agent that sends it, changes a repayment schedule or updates a customer's contact details is another. For agents, the expectations above become concrete: each consequential action needs a rule check, a named person accountable for it above a threshold, and a record that links the action to the decision behind it. Customer-facing actions also need to respect the RBI's existing conduct rules, such as those on recovery and fair practices, which apply whether a person or a machine acts.

On 5 November 2025 the Ministry of Electronics and Information Technology released the India AI Governance Guidelines under the IndiaAI Mission. They apply across sectors and are voluntary. The document has four parts: seven principles, recommendations across six pillars, an action plan with short-, medium- and long-term steps, and practical guidance for industry and regulators.

The seven principles are the same Sutras as FREE-AI, adapted for all sectors, which makes the two documents easy to read together. The guidelines also propose institutions to coordinate AI governance across government, including an AI Governance Group, a Technology and Policy Expert Committee and the AI Safety Institute, and they favour graded, risk-based obligations, voluntary commitments and techno-legal measures over new prohibitions.

For a business, the practical message is the same as FREE-AI's: know what AI you use, match controls to risk, keep a human accountable, protect the people affected and keep records that let someone check what happened.

Because the guidelines prefer existing law, their real effect is on how sector regulators and courts will read that law. Expect questions about AI to arrive inside familiar processes: an inspection, a grievance, a data breach inquiry, an audit. The organisation that can answer them with records rather than descriptions will be in a better position.

The Digital Personal Data Protection Act, 2023 is binding law, and its Rules were notified on 13 November 2025. They take effect in phases: provisions on the Data Protection Board applied on notification, the consent manager framework from 13 November 2026, and most obligations on Data Fiduciaries from 13 May 2027.

An AI agent that reads or changes customer records is processing personal data, so the usual duties apply: a lawful basis, purpose limitation, reasonable security safeguards, breach notification, and honouring rights to access, correction and erasure. Significant Data Fiduciaries carry more, including an annual data protection impact assessment and audit, and a duty under Rule 13 to verify that algorithmic software they use is not likely to pose a risk to the rights of Data Principals. For agents, that is a direct requirement to understand and test what the software does with personal data.

Notice and purpose deserve particular care. Personal data collected to open an account cannot simply be reused to train or run a new AI use without checking that the purpose is covered by the notice or another lawful basis. Agents also make it easy to copy data into new places, such as prompts, caches and logs, so map where personal data flows before an agent goes live, not after.

Roles matter. When a bank uses a vendor's agent on its customer data, the bank is usually the Data Fiduciary and the vendor its Data Processor, under a contract. Our page on the DPDP Act and AI agents explains how this works for elan1's deployments, and our paper on DPDP and append-only logs covers erasure requests against records that cannot be edited.

Several existing instruments apply to AI systems already, without mentioning AI.

• CERT-In's [directions of 28 April 2022](https://www.cert-in.org.in/Directions70B.jsp) under section 70B of the IT Act require specified cyber incidents to be reported within six hours and ICT system logs to be kept for 180 days within India. Logs from AI agents and the systems they touch are part of that estate.
• RBI's KYC directions (Master Direction on KYC, 2016, as amended) govern customer due diligence, whether a person or a model does the first review.
• RBI's outsourcing and IT governance directions apply when an AI capability is bought from a vendor or runs on a third party's infrastructure.
• SEBI and IRDAI have their own supervisory expectations for the entities they regulate; check their current circulars rather than relying on summaries.

Supervisors and auditors will ask to see that controls operate. This table maps the main expectations to the records worth keeping. It is a starting point, not legal advice.

For decisions made with AI, such as alert triage or credit pre-screening, the per-decision record is the hardest to produce after the fact. Build it in from the start: the input, the model's answer and confidence, the route it took, the reviewer and the final decision.

Keep the institution-level records current too. An inventory that was accurate at the board meeting and stale three months later gives false comfort; tie updates to the change process, so a new model or vendor cannot go live without an inventory entry and a risk tier.

1. Days 1–30: inventory and ownership. List every AI system and agent in use, including models inside vendor products, with an owner, purpose, data and customer impact. Assign each a risk tier. Draft or refresh the board AI policy.
2. Days 31–60: controls by tier. For high-impact uses, such as credit, collections, fraud and AML, confirm human accountability per decision, the explanation given to customers, the grievance route and the records kept. Check data processing against the DPDP Act and log retention against CERT-In's directions.
3. Days 61–90: assurance. Commission an independent review of one high-impact use. Set up AI incident reporting into existing operational risk processes. Report the inventory, tiers and findings to the board.

The plan does not need new systems to begin. Most of the first month is finding out what is already running, and that alone is usually the most useful finding.

For banks and NBFCs, elan1 Decide and elan1 Govern implement maker-checker AI for banks. elan1 Decide gives each answer a calibrated confidence and routes it by risk; answers below your threshold reach a person. AML and KYC decision packs package common cases. Every answer is kept with its route and reviewer in the AI decision audit log, and any write that follows goes through elan1 Govern's rule check, named approver and proof record.

Records stay in India: elan1 runs in AWS Mumbai, and proof records never leave the residency domain they were written in (see data residency in India). The collect1 pack configures elan1 Govern for RBI collection rules. elan1 helps produce evidence for these expectations; it does not make an institution compliant on its own.