Legal AI data residency in India: what firms must check

TL;DR: Indian law firms and in-house legal teams uploading client documents to AI tools take on real data-protection and privilege risk. The Digital Personal Data Protection Act 2023 (DPDP Act) regulates how personal data is processed and transferred across borders - it does not mandate blanket localisation, but it does give the government power to restrict transfers to certain countries. Separately, confidentiality obligations and privilege doctrine mean your retainer or employment contract may already prohibit sharing client data with offshore processors without consent. Before you upload a single brief or contract, you need to know exactly where that data goes, who can see it, how long it stays, and whether the vendor trains its models on your files.

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The excitement around legal AI is legitimate. Tools that can surface relevant precedent from thousands of judgments, draft standard clauses at speed, or translate a Marathi order into English in seconds genuinely change what a small team can accomplish in a day. But the moment a lawyer uploads a client’s contract, a confidential settlement memo, or a set of board minutes to an AI platform, they have handed that document to a third-party processor. Where that processor stores it, who at the company can read it, whether it flows to subprocessors overseas, and whether the model provider uses it to train the next version of its system - these are not abstract compliance questions. They are live professional-risk questions.

The Indian legal profession operates under several overlapping confidentiality frameworks: the professional conduct rules under the Bar Council of India Rules, common law duties of confidentiality, legal professional privilege (LPP), and now the DPDP Act 2023 whenever personal data is involved. None of these frameworks disappear when you open a browser tab and paste text into an AI tool. The duty travels with you.

Data residency - where data is physically stored and processed - is one piece of the puzzle, but it is not the only one. A vendor might store your data in India while still routing it through a US-based AI inference API. A vendor might store it offshore in a jurisdiction with strong data-protection laws while never training its model on your files. The geography matters, but the contractual protections and technical controls matter just as much.

What the DPDP Act 2023 actually says

The Digital Personal Data Protection Act 2023 received Presidential assent in August 2023. It is India’s first comprehensive data-protection statute and replaces the patchwork of sectoral rules that existed under the IT Act 2000. The central architecture of the DPDP Act is built around the concept of a “Data Fiduciary” - any person who determines the purpose and means of processing personal data. A law firm that instructs an AI tool to process a client’s personal information is, in that transaction, the Data Fiduciary.

The obligations the Act places on Data Fiduciaries are not trivial. In broad terms, they include:

  • Lawful basis and consent - personal data may only be processed for a lawful purpose with the data principal’s consent, or under one of the limited “legitimate uses” recognised by the Act (which include legal proceedings and compliance with law, but these provisions need careful reading).
  • Purpose limitation - data collected for one purpose cannot simply be repurposed.
  • Data minimisation - only data that is necessary for the stated purpose should be processed.
  • Notice - data principals must receive a clear notice about what is being collected and why.
  • Data principal rights - the Act grants rights of access, correction, erasure, and grievance redressal.
  • Security safeguards - Data Fiduciaries must implement reasonable technical and organisational measures to prevent breaches.
  • Breach notification - significant breaches must be notified to the Data Protection Board and, in some cases, to affected data principals.

The Draft Digital Personal Data Protection Rules 2025 - released for public consultation - begin to operationalise these obligations: they provide detail on how consent records must be maintained, how notices must be given, and how the Data Protection Board will function. As of the time of writing, the Rules have not been formally notified, so their exact final form is not settled. Firms should monitor developments closely, and the DPDP rules 2025 coverage on this blog is worth bookmarking for updates.

What the DPDP Act does not do - and this is a point worth underscoring because it is frequently misrepresented - is require that all personal data be stored within India’s borders as a matter of general law. The localisation model it adopts is different.

Cross-border transfers: the restriction model, not blanket localisation

This is the section most blog posts on this topic get wrong, so it deserves careful treatment.

The DPDP Act 2023 permits the transfer of personal data outside India by default, but it gives the Central Government the power to notify a list of countries or territories to which personal data may not be transferred. This is a restriction or “blacklist” model - transfers are permitted unless the government blocks a specific destination. It is not the mandatory localisation model that appeared in earlier drafts of the legislation (such as the Personal Data Protection Bill 2019, which required data mirroring for all personal data and exclusive storage for sensitive and critical data).

The practical implication: as of now, the government has not notified any restricted countries. Transfers to data processors hosted in the US, EU, or elsewhere are not prohibited per se under the DPDP Act, provided the Data Fiduciary has complied with the other obligations (lawful basis, contractual protections with the processor, etc.). The transfer restrictions may change - the government can act on this by notification rather than by Parliament, which means the list can move quickly. Any firm relying on offshore processing for client data should have a mechanism to stay current on the restricted-country list once it is published.

What does this mean in practice for legal AI procurement? It means the question is not simply “is this vendor’s server in India?” It means asking:

  1. Is the transfer currently permitted to the country where the vendor’s infrastructure sits?
  2. Does the vendor have adequate contractual protections with its own subprocessors (particularly AI model providers)?
  3. If the government restricts a country where your data resides tomorrow, what is the vendor’s migration plan?

Firms that have adopted a policy of “India-only storage” as a matter of internal risk management - rather than strict legal compulsion - are taking a conservative and defensible position. Whether that is the right call for a given firm depends on the nature of the client data, the client’s own expectations, and the firm’s risk appetite. But do not confuse internal policy with what the Act strictly mandates today.

Client confidentiality and privilege: the older, sharper risk

The DPDP Act is new. The duty of client confidentiality is not.

Under the Bar Council of India Rules, advocates are prohibited from disclosing client communications. The common law duty of confidentiality extends beyond formal legal proceedings and attaches to any information communicated in the course of a professional relationship. Legal professional privilege protects communications between a lawyer and client made for the purpose of obtaining legal advice, and it can be waived if confidential materials are unnecessarily shared with third parties.

The question of whether sharing client documents with an AI tool constitutes a waiver of privilege, or a breach of confidentiality, is not fully settled in Indian jurisprudence. But the risk is real and the professional consequences are serious. Bar Council disciplinary proceedings, client claims for breach of confidence, and - if the matter involves a listed company or regulated entity - potential regulatory exposure are all live possibilities.

The analysis turns on several factors. Sharing with a processor that operates under a binding Data Processing Agreement (DPA), with appropriate confidentiality obligations and a clear “no-training” commitment, is meaningfully different from pasting a client document into a general-purpose chatbot with no professional terms. The former looks more like using a document management system; the latter looks much more like a disclosure.

The safest practical approach, before rolling out any AI tool across a practice group, is to:

  • Review your standard retainer template to check whether it permits third-party processing of client data, and if not, update it to include appropriate consent language.
  • For existing clients, consider whether a brief client communication or addendum to the retainer is warranted before processing their documents.
  • For in-house legal teams, check whether your organisation’s data-processing policies and any third-party notices already cover AI tool usage, or whether updates are needed.

This is not a reason to avoid legal AI. It is a reason to do the procurement diligence properly so that you can use it without residual risk.

The training-data question every firm must ask

Of all the data-handling concerns around legal AI, the one that most consistently surprises lawyers when they first encounter it is the training question.

Many general-purpose AI products - including some marketed loosely as “legal AI” - improve their models over time by learning from the content users submit. If your client’s confidential merger documents or their litigation strategy memo end up as training data, several bad things follow. The information is now embedded, in some encoded form, in a model that may generate outputs for other users. There is no practical way to “forget” specific training examples from a deployed model. And the confidentiality breach may be permanent.

This is not hypothetical. Major general-purpose AI providers have, at various points, used interaction data for training by default, with opt-out available only in enterprise tiers or via explicit settings. Free tiers of consumer AI products have historically carried the highest risk.

The minimum you need from any legal AI vendor is a clear, contractually binding commitment that your data will not be used to train or fine-tune any model - theirs or any third party’s. That commitment should appear in the main service agreement or DPA, not just in a marketing FAQ. If the vendor’s terms are unclear or silent on this point, assume the worst and ask directly before uploading anything sensitive.

The vetting question of AI citation accuracy is important, but the training-data question may be the more consequential one from a risk perspective.

Vetting checklist: 12 questions before you upload

The table below sets out the key questions to put to any legal AI vendor, alongside the acceptable and unacceptable answers. Use it as a starting point for your procurement due diligence. Not every “fail” on this checklist is an absolute disqualifier - context matters - but any blank or evasive answer should be treated as a red flag.

QuestionPassFail
Where is client data stored (country and cloud region)?Clear answer with named country/region”Our servers” / “the cloud” / refuses to say
Does the vendor use client data to train or fine-tune any model?Contractual “no-training” guaranteeSilent, qualified, or opt-out-only
Who are the sub-processors (including the underlying AI model provider)?Named list, updated and availableRefuses to disclose or “varies”
Is there a Data Processing Agreement available?Yes, signed DPA offered as standardNo DPA, or “contact sales for that”
What is the data retention period after account termination?Clear period with deletion confirmationIndefinite retention or no answer
Can you request deletion of specific documents mid-contract?Yes, with process describedNo self-serve deletion
Is data encrypted in transit and at rest?Yes, with protocol details (TLS 1.2+, AES-256 or equivalent)No specifics, or encryption only “where required”
What access controls limit vendor staff from reading your files?Zero-trust / no human access unless authorised for supportBroad internal access
What is the breach notification timeline?72 hours or less (DPDP-aligned)“As required by law” without specifics
Does the tool rely on a general-purpose model (GPT, Claude, Gemini) without a private API deployment?Private deployment or India-based inferenceGeneral consumer API routing
Is the product compliant with or designed around the DPDP Act 2023?Yes, with documented approachNo mention of DPDP
Has the product been independently audited for security?Yes, with report available on requestNo audit, or audit not shareable

If you are procuring for a large firm or an in-house team at a regulated entity, the DPA and sub-processor list are non-negotiable starting points. For smaller firms, at minimum get the no-training commitment and the data-location answer in writing.

The phrase “built for India” appears in a lot of legal-tech marketing. It is worth being precise about what it should actually mean, because there is a wide spectrum between “we have a few Indian cases in our dataset” and “this product was designed from the ground up for the Indian legal system.”

At minimum, a legal AI tool genuinely built for India should:

Cover the actual corpus. Indian legal research cannot be done on a dataset dominated by US or UK precedent. You need Supreme Court of India decisions, High Court judgments across jurisdictions, Tribunal orders (NCLAT, ITAT, TDSAT, NGT and others), legislative text, and statutory instruments - all accurately indexed with correct citations.

Understand Indian citation format. The standard Indian citation formats - AIR, SCC, SCR, and the neutral citation system - are meaningfully different from Neutral Citation Numbers used in England or US reporters. A tool that mixes these up or generates hallucinated citations in a format that does not exist is worse than no tool at all.

Handle Indian-language legal material. A significant proportion of State High Court orders, and almost all lower court proceedings, are in regional languages. Meaningful access to that material requires translation capability that understands legal register, not just consumer-grade machine translation.

Be designed with the DPDP Act in mind. This means the product’s data architecture was considered against Indian data-protection obligations, not retrofitted with a privacy policy paragraph after the fact.

The difference between a genuinely India-native legal AI product and a global product with an “India” marketing page matters considerably when you are trying to understand whether the tool’s data handling was designed for your regulatory environment. The comparison between native legal AI and generic GPT wrappers explores this distinction in more detail.

It would be dishonest to write this piece without acknowledging what a lot of lawyers are actually doing right now: using general-purpose chatbots - ChatGPT, Gemini, Copilot - for legal drafting and research tasks, often without a clear picture of the data implications.

These tools are genuinely useful for certain tasks. Summarising a document structure, drafting boilerplate clauses from scratch, or explaining a concept in plain language are all things a general-purpose LLM can do reasonably well. The risk is not that these tools exist or that lawyers are curious about them. The risk is the combination of: (a) uploading confidential client material, (b) using a consumer tier with broad data-use terms, and (c) assuming the tool’s legal outputs are reliable without verification.

Consumer tiers of major AI products have, historically, used interaction data for model improvement. Enterprise tiers typically offer stronger commitments, but “enterprise” licensing at the major providers comes at significant cost and still involves routing data through offshore infrastructure. The underlying models are trained on general internet data, not on Indian legal corpora - which means citation accuracy for Indian law is unreliable without retrieval mechanisms anchored to a verified Indian corpus. The comparison page on Niyam’s site sets out the specific differences in more detail.

The short version: for casual, non-client-specific legal tasks (drafting a template NDA from scratch, understanding a general legal concept), the risk profile of a general chatbot is manageable if you do not upload client documents. For anything that involves real client data - names, facts, confidential strategy - a purpose-built tool with proper data-handling commitments is the appropriate choice.

Building a private firm knowledge base safely

One of the most valuable applications of legal AI for a law firm is the creation of a searchable, AI-queryable knowledge base from the firm’s own precedents: past opinions, standard form contracts, matter files, and internal guidance notes. Done well, this dramatically shortens the time a junior associate spends locating and adapting existing work product. Done carelessly, it creates a centralised collection of every client confidence the firm has ever held, sitting in a third-party system with unclear access controls.

The architectural question is whether the AI system is allowed to store and index your documents on its own servers, or whether it operates on documents you retain control of. There are several possible models:

Vendor-hosted RAG (Retrieval-Augmented Generation) - your documents are uploaded to the vendor’s vector database, which the AI searches at query time. The documents live on the vendor’s infrastructure. This is convenient but requires strong contractual protections.

Self-hosted or private-cloud deployment - the AI model and vector database run inside your own infrastructure (on-premises or on a cloud account your firm controls). Higher implementation cost, but data never leaves your perimeter.

Session-only processing - documents are uploaded for a specific session and processed in memory, with no retention between sessions. Lower risk for one-off tasks, but limited utility for building a persistent knowledge base.

Most law firms in India are not in a position to run self-hosted AI infrastructure today - the cost and technical overhead are substantial. The vendor-hosted model is the practical option for most. The key then is ensuring the DPA is tight, the no-training commitment is absolute, and you have a clear view of which sub-processors have access to the vector-embedded representations of your documents (which are, in a meaningful sense, a compressed version of the document content).

If your firm handles particularly sensitive matters - defence, M&A, government advisory, criminal law at the higher courts - the investment in understanding your vendor’s infrastructure in detail is proportionate to the risk.

Red flags that should stop you uploading

Before pulling together the checklist responses, here are the specific patterns that should make you pause - or walk away:

No DPA available, or it requires a “custom contract.” Standard B2B AI products offer a DPA as a click-through or downloadable document. If the vendor treats a DPA as a premium negotiation rather than a standard document, that is a signal about how they think about data-protection compliance.

Terms of service that reserve the right to use content for “product improvement.” This is the training-data issue in standard contract language. Read the terms, not just the website FAQ.

Vague or inconsistent answers about the underlying model. If the vendor cannot or will not tell you which AI model powers the product and where that model’s inference runs, you cannot assess your sub-processor exposure.

No named data-protection contact or DPO. Under the DPDP Act, Significant Data Fiduciaries are required to appoint a Data Protection Officer. For a vendor serving the legal industry - handling client confidences at scale - the absence of a named contact for data-protection queries is a meaningful gap.

“We comply with all applicable laws” as the only data-protection statement. This is a legal boilerplate non-answer. Every business is required to comply with applicable laws. The question is what specific technical and organisational measures they have implemented.

Free tier with no enterprise option. If there is no enterprise or professional tier with explicit no-training commitments and a DPA, the product was not designed for professional use. The free vs paid legal AI comparison covers the specific differences in what you typically get at each tier.

Citations that cannot be verified. This is distinct from the data-residency question but often travels alongside it: a tool that fabricates citations to non-existent judgments has almost certainly not been built on a curated, verified legal corpus. That same lack of rigour often extends to data handling. How to vet legal AI citation accuracy provides a practical testing methodology.

How Niyam approaches data handling

Niyam is a legal AI platform built specifically for India. Its core features - Research across 72,000+ Indian judgments, a Citator for verifying citation standing, Drafting assistance, Notice drafting, Notices, Translation of legal material, and Matters management - are all grounded in retrieval from a curated Indian legal corpus rather than open-ended generation from a generic model.

On the data-handling side, Niyam has been designed with the DPDP Act 2023 in mind from the outset. The platform does not use your documents to train or fine-tune any model. Because this is a non-negotiable concern for legal professionals, it is a design principle rather than a configurable option.

Niyam does not make specific claims about server locations, certifications, or infrastructure details that are not publicly documented - if those specifics matter for your procurement process (and they should), the right place to get them is the security page and the responsible AI page, or directly via [email protected]. What the platform does commit to is being purpose-built for the Indian legal context, which is different from a global legal AI tool that happens to include some Indian cases.

For firms comparing options, the comparison section of the site sets out how Niyam differs from general-purpose AI tools across dimensions including data handling, citation accuracy, and Indian legal coverage.

The drafting tools are worth particular attention for contract teams doing high-volume work: the retrieval-grounded approach means drafting suggestions are anchored in verified Indian legal context rather than generated from a model’s broad (and potentially outdated) understanding of what an Indian contract looks like.

Frequently asked questions

Data residency refers to where data is physically stored and processed. For legal AI tools, it matters because Indian lawyers handle client information that is subject to confidentiality duties and, when it contains personal data, the DPDP Act 2023. If client documents are uploaded to an AI tool hosted in another country, the firm needs to understand what protections apply - both legal and contractual - and whether the cross-border transfer is permitted under current law.

No, not as a blanket requirement. The DPDP Act uses a restriction model for cross-border transfers: personal data can be transferred outside India unless the Central Government notifies that transfers to a specific country are restricted. As of now, no countries have been placed on the restricted list. However, firms should monitor developments and ensure appropriate contractual protections are in place with any offshore processor. The Draft DPDP Rules 2025 provide additional detail on consent and processor obligations but, as of the time of writing, have not been formally notified.

Using consumer tiers of general chatbots with actual client data carries real risk. Consumer products from major AI providers have historically used interaction data for model training, meaning your client’s confidential information could in principle become part of a training corpus. Enterprise tiers provide stronger data-use commitments but still involve offshore processing and inference on models not trained on Indian legal material. For client-specific work, a purpose-built legal AI tool with explicit no-training commitments is the safer choice.

A Data Processing Agreement (DPA) is a contract that governs how a processor handles personal data on behalf of a controller. Under the DPDP Act framework, if you are a Data Fiduciary instructing an AI tool to process personal data, you need appropriate contractual safeguards in place with that processor. A DPA should cover purpose limitation, security measures, sub-processor disclosure, breach notification, data return or deletion on termination, and crucially for legal AI, a no-training commitment.

What does “no training on my data” actually mean in practical terms?

A genuine no-training commitment means the vendor does not use documents you upload, or queries you submit, to update or fine-tune any AI model - their own or a third party’s. The commitment should be contractually binding, not just a website promise. It should cover not just the vendor but their sub-processors, including the underlying model provider. Ask for this explicitly in the DPA and check that the service agreement does not contain carve-outs in the “product improvement” language.

Legal professional privilege (LPP) protects confidential communications between a lawyer and client made for the purpose of seeking or giving legal advice. LPP can be waived by unnecessary disclosure of the privileged material to third parties. Whether using an AI tool constitutes waiver depends on the circumstances - sharing with a processor under a binding confidentiality agreement is different from disclosing to a third party without restriction. Careful vendor contracting, including a confidentiality clause in the DPA, is the appropriate mitigation.

Are Indian law firms required to notify clients before using AI tools to process their documents?

Strictly speaking, the DPDP Act’s consent requirements apply to the processing of personal data. Whether a particular client document contains “personal data” within the meaning of the Act depends on its content. Beyond statute, professional conduct obligations and common law confidentiality duties may independently require that clients be informed if their information is being processed by third-party tools, particularly offshore ones. The safest approach is to update retainer agreements to include appropriate disclosure and consent language for AI-assisted processing.

The most important sub-processor to identify is the underlying AI model provider - this is the company whose model inference sees your content. Major model providers include OpenAI, Anthropic, Google DeepMind, and Mistral, among others. Each has its own data-use terms that may permit or restrict training on API inputs. Beyond the model provider, ask about cloud infrastructure providers (AWS, GCP, Azure, or Indian providers), vector database providers for retrieval systems, and any third-party analytics tools. A reputable vendor should be able to provide a named sub-processor list.

Best practice - and what DPDP-aligned data handling should deliver - is deletion within a reasonable, specified period after account termination, typically 30 to 90 days, with a contractual commitment to provide written confirmation of deletion on request. Indefinite retention, or retention “as required by law” without a defined period, is not acceptable for client confidential material. Check both the main service agreement and the DPA for retention terms.

What is retrieval-augmented generation and why does it matter for data security?

Retrieval-Augmented Generation (RAG) is a technique where an AI model retrieves relevant documents from a database at the time of a query, rather than relying solely on what was embedded in the model during training. For data security, RAG matters because it means your documents can be processed without being permanently encoded into the model weights - the documents live in a controllable database, not distributed across a neural network. It also supports more accurate, citation-anchored outputs because the model is working from retrieved text rather than imperfect recall.

India-hosted storage avoids the cross-border transfer question under the DPDP Act and aligns with a conservative internal risk-management position. However, geography alone is not sufficient. A tool hosted in India could still use an overseas model API for inference, have inadequate access controls, or lack a no-training commitment. Conversely, a reputable provider hosted in a jurisdiction with strong data-protection law and robust contractual protections may represent lower actual risk than a poorly secured India-hosted product. Evaluate the full picture: location plus contractual protections plus technical controls plus no-training guarantee.

What are the consequences for a law firm of a data breach involving client documents processed by an AI tool?

Consequences can be severe across multiple dimensions. Under the DPDP Act, Data Fiduciaries face potential penalties from the Data Protection Board (the specific penalty quantum will depend on the finalised Rules and Board enforcement action). Under professional conduct rules, a breach of client confidentiality can result in Bar Council disciplinary proceedings, including suspension of enrolment in serious cases. Civil liability for breach of confidence is also possible. For in-house legal teams, exposure may extend to their employer’s data-protection regulatory obligations. The reputational damage is typically the most immediately damaging consequence.

In-house legal teams may actually face stricter considerations in some respects. Their employer - the company - may itself be a Data Fiduciary for its employees’ and customers’ personal data, adding another layer of DPDP obligation. Matters handled by in-house counsel often involve commercially sensitive information (M&A, regulatory responses, litigation strategy) where breach consequences extend beyond professional discipline to competitive and regulatory exposure. The same vetting checklist applies; if anything, in-house teams at listed companies or regulated entities (banks, NBFCs, insurance companies) should apply it more rigorously.

A DPDP-aware product has been designed with the Act’s requirements as inputs to its architecture, not as afterthoughts. In practice this should mean: consent and purpose-limitation controls built into the product flow, a data retention policy that aligns with the Act’s framework, a breach detection and notification mechanism that meets the Act’s timelines, a DPA available for professional customers, and a named contact for data-protection queries. It does not guarantee compliance - regulatory compliance is determined by the Data Protection Board in enforcement, not by marketing claims - but it does suggest the vendor has engaged with the relevant legal framework.

The starting point is to take a sample of citations generated by the tool and verify each one against an authoritative source: the official Supreme Court of India website, the judgment database you normally use, or the Bar and Bench case tracker. Check that the case name, year, court, and citation format match. Then read the relevant paragraph to confirm the tool’s summary of the holding is accurate. A tool that generates even occasional phantom citations - cases that do not exist - should not be used for any work that relies on those citations. Full methodology on vetting legal AI citation accuracy is available separately.

Yes, but with caveats. Translation of legal material from regional languages (Telugu, Tamil, Marathi, Kannada, Bengali, and others) into English is a legitimate and valuable use case for AI. The data-risk analysis is identical to any other document processing: you need to know where the uploaded document goes, whether it is used for training, and whether the sub-processor handling the translation is subject to the same contractual protections. The additional consideration for translation is accuracy in legal register - a translation that converts legal terms into everyday equivalents may obscure important distinctions.

During the demo, ask: Who can see the documents I just uploaded? Where are they stored right now? What is the name of the model provider powering this response? Do your terms allow you to use this session for training? Can I see your DPA? Who are your sub-processors? What happens to my data if I close my account today? What is your breach notification process? If the sales representative cannot answer any of these questions and does not have a clear path to getting you an answer, that is itself diagnostic.

The risk profile differs in one important way: research queries may or may not involve uploading client documents (you might simply ask “what is the law on specific performance under the Specific Relief Act?” without providing any client facts), whereas drafting tasks almost always involve providing client-specific context - party names, commercial terms, specific facts. Drafting tasks therefore typically involve more personal and confidential data flowing to the vendor’s systems, making the no-training guarantee and data-handling terms more critical for drafting use cases.

The market is moving quickly. Several Indian-focused legal AI products have launched or materially expanded in the last 18 months, and the DPDP Act’s coming into full operation will push professional-grade data handling from optional to expected. That said, the current state is uneven - a few providers have invested seriously in DPDP-aligned architecture, while others have simply applied a privacy policy update to a product not designed with Indian data-protection obligations in mind. The right approach is to apply the vetting checklist now, and re-evaluate annually as both the regulatory framework and the vendor landscape evolve.

If this piece has prompted you to think more carefully about where your client documents go, that is the right outcome. The question is not whether to use legal AI - the productivity case is compelling and the tools are genuinely improving - but which tools to use and under what conditions.

Niyam has been built for Indian legal professionals, with DPDP-aware data handling and retrieval grounded in 72,000+ Indian judgments. Research, Citator, Drafting, Notices, Translation, and Matters - all in one platform, without the data-handling compromises of general-purpose AI tools.

For more on how the platform handles data and AI responsibility: Security and Responsible AI.

For the specific comparison with general chatbots: Niyam vs ChatGPT for Indian legal work.

To understand drafting features in detail: Drafting solutions.

When you are ready to try it: Start for ₹100 - 200 credits to start, cancel anytime. Questions: [email protected].