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Build the checklist as a living control system for every AI model, feature, prompt workflow and vendor your business uses—not as a one-time form. Start with an inventory and named owners, classify each use by harm and affected people, map the data and applicable Indian laws, then verify privacy, security, human review, transparency, complaints and monitoring before launch. Keep evidence of each decision and revisit it after a material model, data, vendor or legal change.
India does not currently have one all-purpose AI regulator or a single AI statute. Duties are distributed across the Digital Personal Data Protection (DPDP) framework, the Information Technology Act and Rules, consumer and criminal law, copyright and contracts, and sector regulators. The 2026 India AI Governance Guidelines add a principle-based expectation that organisations can demonstrate responsible governance when asked.
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What laws and regulators should an Indian AI checklist cover?
Map each use case to all laws that could apply; do not assume that calling a system a “pilot” removes legal duties. The following map is a starting point, not a substitute for a use-case-specific legal review.
| Law, rule or authority | Why it can matter to an AI system | Checklist evidence |
|---|---|---|
| Digital Personal Data Protection Act and Digital Personal Data Protection Rules, 2025 | Notice, consent or another permitted processing basis, data-principal rights, processor governance, security and breach duties where personal data is processed. | Data map, notices, consent or other-basis record, rights workflow, retention and deletion logs, processor contract and incident records. |
| Information Technology Act and Information Technology Rules | Intermediary, security, due-diligence, content and grievance obligations can attach to online services and synthetic or manipulated content. | Role analysis, published policies, moderation and grievance procedures, access logs and escalation records. |
| Consumer Protection Act and advertising rules | AI claims, recommendations, pricing, dark patterns and automated customer interactions must not mislead or unfairly disadvantage consumers. | Claims substantiation, interface review, disclosure text, complaint metrics and corrective-action records. |
| Bharatiya Nyaya Sanhita | AI-assisted conduct can still create criminal exposure, including harms involving fraud, impersonation, harassment or vulnerable people. | Abuse scenarios, escalation contacts, evidence-preservation procedure and incident decisions. |
| Copyright law and contracts | Training, retrieval, input and output may involve licensed material, confidential information, employee inventions or customer restrictions. | Licence and permission register, contract clauses, provenance checks and output-use restrictions. |
| Telecommunications Act and communications rules | Voice, messaging, caller-identification or network use may trigger telecom-specific requirements. | Service classification, consent and calling controls, provider approvals and audit trail. |
| Sector rules | RBI, SEBI, IRDAI, telecom authorities, NCIIPC and other bodies may impose additional controls for finance, securities, insurance, communications or critical infrastructure. | Sector applicability memo, regulator guidance review, control crosswalk and accountable sector owner. |
The Government of India’s 2026 India AI Governance Guidelines describe this as a coordinated, principle-based approach. They call for compliance with all applicable Indian laws, evidence of compliance when requested, grievance redressal, transparency reporting, audits and techno-legal safeguards. MeitY coordinates policy, while existing regulators retain their mandates; there is no single comprehensive AI regulator in the current legal architecture.
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How do I create an inventory of every AI system?
Assign one accountable owner and record every system, including tools employees turn on without an IT project. Inventory embedded features in ordinary software, public APIs, chatbots, scoring models, content generators, retrieval systems, copilots, fine-tuned models and vendor-operated automations.
Record these fields for each system
- Purpose and owner: business objective, accountable executive, product owner and technical operator.
- Model identity: provider, model or feature name, version, deployment environment, API endpoint and review date.
- People affected: employees, applicants, customers, children, patients, borrowers, policyholders, citizens or members of the public.
- Geography and suppliers: countries where the provider, support team, storage and subprocessors operate; identify cross-border transfers.
- Data activity: whether the system collects, infers, stores, transfers, retrieves or makes decisions from personal, confidential or regulated data.
- Decision role: drafting assistance, recommendation, ranking, eligibility decision, safety action or fully automated action.
- Lifecycle status: experiment, approved pilot, production, suspended or retired, with the date and reason for each change.
Require employees to register “shadow AI” discovered through expense claims, browser extensions, procurement records, code repositories, network logs and security reviews. A system that is not in the inventory cannot receive a reliable privacy or security assessment.
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Use a documented internal triage so that scarce review time goes to systems capable of causing the greatest harm. The categories below are practical governance tiers, not statutory labels.
| Internal tier | Typical examples | Minimum decision |
|---|---|---|
| Routine assistance | Internal drafting, summarisation or coding with no personal or confidential data and no action on a person. | Owner approval, approved tool list, data-use rules and basic security checks. |
| Customer or workforce impact | Customer chatbot, recruitment screening, employee monitoring, personalised pricing or support prioritisation. | Privacy and legal review, representative testing, human override, clear notices and complaint route. |
| High-consequence use | Credit, insurance, health, safety, children, essential services, critical infrastructure or decisions that can deny a material opportunity. | Executive sign-off, sector review, documented impact assessment, independent testing where feasible, continuous monitoring and a tested rollback plan. |
For every tier, name a business owner, privacy or legal reviewer, security owner and operational operator. Record who can approve launch, who can pause the system and who handles complaints. Do not leave accountability solely with the engineering team.
How do I map DPDP Act and Rules 2025 requirements to an AI workflow?
Trace personal data from collection to deletion, including prompts, context windows, retrieval indexes, fine-tuning sets, logs, backups and vendor support channels. The DPDP Rules 2025 were notified by MeitY on 14 November 2025 and have a phased commencement timeline; check the applicable commencement date for each obligation before setting a deadline.
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Define the role and purpose
For each flow, state whether your business is acting as the organisation deciding the purpose, as a processor or vendor, or in another relationship recognised by the DPDP framework. Write a specific purpose such as “answer authenticated account questions” rather than “improve AI.” A change from support to model training is a new purpose that requires a fresh review.
Provide notice and capture the right permission
Give a clear notice describing what data enters the AI workflow, why it is used, whether a provider or subprocessor receives it, how long it is retained and how a person can exercise rights or complain. Collect and record consent when the selected processing basis requires it; do not treat a pre-ticked box, an employee’s silence or a vendor’s generic terms as proof. Store the notice version, timestamp, channel, scope and withdrawal event.
Operationalise rights and deletion
Build a way to find a person’s records across prompts, conversation history, retrieval stores, evaluation data and backups. Route access, correction, erasure, grievance and consent-withdrawal requests to a named team, apply the result to downstream processors and preserve a completion record. Set retention periods by purpose and configure deletion rather than relying on an engineer to remember it.
Minimise what enters the model
- Remove identifiers and free-text details that are not needed for the task.
- Block secrets, passwords, payment data, health details and confidential contracts from consumer-grade tools unless the use is expressly approved.
- Use masking, tokenisation or a controlled retrieval layer when the model needs context but not direct identifiers.
- Separate production data from development and evaluation environments.
Contract with processors and providers
Put purpose limits, confidentiality, security measures, subprocessor disclosure, assistance with rights requests, deletion or return, audit evidence and incident notification in writing. Confirm whether prompts and outputs are retained, used to train a shared model, reviewed by humans or stored outside India. If the provider cannot answer, treat that as an unresolved risk rather than an assumption of compliance.
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| Vendor question | Evidence to request |
|---|---|
| Can it support DPDP duties? | Configurable notices, consent and withdrawal records, rights-request workflow, retention controls and exportable audit logs. |
| Where does data go? | Hosting regions, transfer mechanism, subprocessor list, support-access locations and deletion timetable. |
| Is customer data used for training? | Contractual opt-out or prohibition, technical setting, model-isolation description and proof of enforcement. |
| How are security events handled? | Identity and access controls, encryption, secrets management, logging, backup, vulnerability process, incident severity matrix and notification commitments. |
| Can we test and supervise outputs? | Evaluation tools, red-team support, version pinning, change notices, human-review queues, override controls and rollback capability. |
| Does it support Indian contexts? | Language and accessibility testing, sector features, Indian data-residency or transfer options and support for local complaint routes. |
| What will implementation cost internally? | Integration effort, migration work, administrator time, training, ongoing monitoring and exit or data-export assistance—not only the subscription price. |
Make procurement conditional on legal, privacy, security and business-owner approval. Reassess the provider when its model, subprocessors, retention terms or data location changes.
How do I secure an AI system and prepare for incidents?
Apply ordinary cyber controls and add tests for model-specific abuse. Security is a lifecycle obligation, not a checkbox completed at procurement.
Baseline controls
- Use single sign-on or strong identity controls, least privilege and separate administrator, developer and user roles.
- Encrypt data in transit and at rest; store API keys and secrets in a managed vault, never in prompts, source code or tickets.
- Log prompts, tool calls, model versions, administrative actions and high-risk outputs while filtering unnecessary personal data from logs.
- Maintain backups, secure configuration baselines, dependency updates, vulnerability management and a tested recovery procedure.
- Restrict tools that can send email, move money, change records, execute code or access a network; require confirmation for consequential actions.
Abuse and resilience tests
Before launch, test prompt injection, malicious files, data exfiltration, indirect instructions in retrieved documents, unsafe tool calls, model abuse, denial-of-service patterns, supply-chain compromise and leakage of one customer’s context to another. Test in Indian languages and realistic business workflows, not only in English toy examples. Define severity, containment, evidence preservation, internal escalation, regulator contact and customer communication. Align the plan with applicable CERT-In directions and, where relevant, NCIIPC or sectoral cyber requirements.
When must a human review an AI decision?
Specify the decisions that require a trained person and prohibit fully automated action where the likely harm is unacceptable. A human who merely clicks “approve” is not meaningful oversight; the reviewer needs authority, time, relevant information and a way to reject the model.
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- Show the input factors, model output, confidence or uncertainty indicator where reliable, applicable policy and known limitations.
- Give the reviewer an override, correction and escalation option, with reasons captured in an audit log.
- Notify the affected person when AI materially influences an outcome, explain the role of human review and provide a complaint or appeal channel.
- Set service levels for review and escalation so that a queue does not silently become an automated denial.
Test fairness and reliability
Evaluate accuracy, false positives and false negatives, disparate impact, accessibility, language performance and failure modes against representative Indian contexts. Include regional languages, varied names and addresses, disability-related interactions and low-connectivity conditions when they matter to the use case. Re-test after a material change to training data, prompts, provider, model version or decision rule; retain the test set, results, limitations and remediation decision.
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How should we disclose AI use and protect consumers?
Tell people when they are interacting with AI if that fact affects trust, rights or an outcome. The disclosure should identify the system’s purpose, important limits, expected human involvement, relevant data use and how to complain, using language the audience can understand.
Review claims and interfaces
Have legal and product teams verify that demonstrations, accuracy claims, “unbiased” statements and promises of savings or safety are supported by evidence for the actual deployment. Check for dark patterns, forced consent, confusing opt-outs, hidden ranking criteria and customer-service flows that make it difficult to reach a person. Consumer law can apply even when an error came from a third-party model.
Handle synthetic and manipulated content
If the business publishes or distributes synthetic or manipulated public-facing content, check the current Information Technology Rules requirements that apply to the service, including labelling, metadata, declarations and grievance duties. Record the classification decision, the label used, where it appears and who handles complaints. Requirements can differ by service role and content, so avoid a one-size-fits-all label.
What documentation proves that the checklist was followed?
Create an AI system card or equivalent file for every production use. Keep it current and link each control to an owner and evidence.
| Record | What to capture |
|---|---|
| Purpose and scope | Business objective, users, affected groups, prohibited uses and deployment boundaries. |
| Data and model | Sources, categories, flows, provider, version, prompts or retrieval design, retention and countries involved. |
| Risk decision | Impact tier, legal mapping, alternatives considered, residual risk and approval conditions. |
| Testing | Privacy and security tests, fairness and language results, red-team findings, limitations and fixes. |
| Operations | Human-review procedure, access list, monitoring metrics, incident contacts, rollback steps and change history. |
| People and vendors | Accountable executive, operators, processors, subprocessors, contracts, training and audit evidence. |
| Complaints and incidents | Notice version, consent or other-basis record, requests, grievances, incidents, notifications and remediation. |
The 2026 India AI Governance Guidelines recommend audits, self-certification, transparency reporting and grievance redressal. For a high-impact system, prepare a proportionate public or regulator-facing summary while protecting confidential security details. Preserve evidence in a controlled repository with access logging and a retention rule.
What is the correct launch gate and monitoring process?
Do not move to production until the legal or privacy, security, product and business owners have signed the same release record. A launch gate should answer these questions:
- Is the purpose narrow, lawful and documented?
- Are data flows, providers, subprocessors, retention and deletion understood?
- Are notices, permissions, rights handling and complaint routes live?
- Have misuse, security, fairness, accessibility and representative-language tests passed or received an explicit risk acceptance?
- Are human review, override, rollback and incident contacts operational?
- Are user disclosures and marketing claims accurate for the deployed version?
After launch, monitor drift, complaints, harmful outputs, access anomalies, incidents, review overrides, model-version changes, vendor notices and regulatory updates. Set thresholds that trigger investigation or suspension. Pause or roll back when a control fails, preserve the relevant evidence and record who authorised resumption.
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Use this sequence for a new system or to bring an existing one under control:
- Inventory: register models, features, APIs, chatbots, data stores and shadow tools; appoint owners.
- Classify: identify affected people, decision impact, vulnerable groups, sector context and personal-data use.
- Map: document data flows, providers, countries and applicable laws, regulators and contractual duties.
- Close critical gaps: minimise data, set retention and deletion, fix vendor terms, enforce access controls and establish incident handling.
- Test: run privacy, security, abuse, fairness, accessibility and Indian-language evaluations using representative scenarios.
- Approve: activate notices, consent or other lawful basis, human review, complaint handling and rollback before production sign-off.
- Monitor: review metrics and complaints on a defined cadence and after every material model, data, vendor or legal change.
Keep a dated decision log explaining why a requirement applies or does not apply. That explanation, together with test results and operating evidence, is more defensible than a checklist marked “complete” without proof.
Frequently Asked Questions
Do I need a separate data-protection impact assessment for every AI tool?
The cited Indian framework does not establish one universally named assessment document for every use. A documented, proportionate impact analysis is still prudent, especially where an AI system affects eligibility, employment, health, children, safety or essential services; record the method you used and why the depth of review fits the risk.
Can an Indian company send personal data to an overseas AI provider?
Cross-border use is not something to approve by default or ban by assumption. Identify every destination and subprocessor, verify the DPDP requirements and any sector or contract restriction that applies, minimise the data, and obtain written retention, deletion, security and incident commitments before transfer.
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Treat it as an operational incident: preserve the prompt, output, model version and relevant logs; contain access or disable the feature; assess affected people and notification duties; investigate the cause; and document corrective action. Re-test before restoring service and update the system card and training.
How often should an AI compliance checklist be reviewed?
Choose a cadence proportionate to the system’s impact and write it into the approval record. Review immediately after a material change to the model, data, prompt, provider, subprocessor, decision rule or applicable law, and sooner when complaints, incidents or monitoring thresholds indicate a problem.
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