Every legal technology vendor now claims some version of “enterprise-grade AI security.” Almost none of them mean exactly the same thing by it, and the phrase itself has become close to meaningless as a filter for procurement decisions. For a GC or legal operations lead evaluating an AI-powered contract, litigation, or notice management platform, the practical challenge is not finding a vendor who says the right words. It is knowing which specific questions actually separate a genuinely secure, compliant AI deployment from one that merely sounds like it.
This is a buyer’s guide, not an opinion piece. It sets out the specific things Indian enterprise legal teams should evaluate before deploying any AI-powered legal software, regardless of which vendor is on the table, and what a genuinely strong answer to each question looks like versus a vague or evasive one.
Why AI Security Evaluation Is Different from General SaaS Security Evaluation
Enterprise legal and IT teams already have a reasonably mature playbook for evaluating standard SaaS security: check for SOC 2, confirm encryption in transit and at rest, review the data processing agreement, and assess the vendor’s incident response history. AI-powered legal software requires all of this, plus a distinct additional layer of questions that a standard SaaS security review does not typically cover.
The reason is structural. A traditional SaaS platform stores and retrieves your data. An AI-powered platform processes your data through a model, and that processing step introduces new questions that storage and retrieval alone do not: where does the data go during inference, does the model learn from what it sees, who else’s data might that model have been trained on, and can you actually prove, after the fact, what the AI did with a specific document. None of these questions arise in a standard document storage and retrieval evaluation. All of them arise the moment a large language model is involved, and legal contracts are precisely the kind of sensitive, commercially and legally consequential content where getting these answers wrong carries real cost.
For enterprises in regulated sectors, BFSI, insurance, pharmaceuticals, this is not a theoretical concern. It is the difference between a deployment that a regulator and an internal audit function will approve, and one that creates an unresolved compliance gap the moment anyone looks closely at it.
1. Where does your data go when you query the AI?
This is the foundational question, and it needs a precise answer, not a reassuring one. Ask the vendor to describe, specifically, the full data path when a document is uploaded or a query is run: which systems the data passes through, whether it is processed by the vendor’s own infrastructure or passed to a third-party model provider (such as an underlying foundation model vendor), and where geographically that processing occurs.
What a strong answer looks like: A vendor who can name the specific cloud infrastructure and region the data is processed in, confirm whether any third-party model provider is involved in the data path, and explain what happens to the data immediately after the query completes, whether it is deleted, retained for a defined period, or stored as part of the ongoing service.
What a weak answer looks like: “Your data is secure” or “we use industry-standard AI models” without specifying which models, which infrastructure, or which geography. Vagueness at this question is the single most common red flag in vendor conversations, because it usually means either the vendor does not know the answer themselves, or the answer is one they would rather not have to explain in detail.
2. Is the model trained on your data?
This is a separate question from where data is processed, and vendors sometimes answer the first question well while leaving this one deliberately unaddressed. The question is specific: does any version of your contracts, negotiation history, or query content get used, directly or indirectly, to train or fine-tune the underlying AI model, whether that model belongs to the vendor or to a third-party foundation model provider they rely on?
This matters for a concrete reason beyond general privacy concern. If your commercial terms, pricing structures, or negotiation positions are used to train a shared model, there is a non-zero risk, however the vendor frames it, that patterns from your data could influence outputs generated for other customers, including competitors, using the same underlying model.
What a strong answer looks like: An explicit, contractual commitment that your data is not used to train or fine-tune any model, with this commitment reflected in the actual contract terms, not just a sales conversation. Some vendors offer this as a configurable setting; the strongest position is when it is the default, not an opt-out you have to remember to select.
What a weak answer looks like: “We may use aggregated or anonymised data to improve our models” without a clear, specific definition of what “aggregated” or “anonymised” actually means in this context, since these terms can be defined loosely enough to permit exactly the practice a legal team would want to avoid.
3. What certifications actually matter, and what to look for beyond the badge
SOC 2 Type II and ISO 27001 are the two certifications most enterprise legal buyers ask about, and both are meaningful, but only if you look past the badge itself.
SOC 2 Type II specifically, not Type I. Type I confirms controls existed on a single audit date. Type II confirms controls actually operated effectively over an extended period, typically six to twelve months. Always ask for the Type II report specifically, and check the audit period: a report from more than twelve months ago is not current evidence of security posture. Ask whether the report notes any exceptions, and if so, how they were remediated.
ISO 27001:2022, not the withdrawn 2013 version. The 2013 standard was formally withdrawn, with the transition deadline passing in October 2025. A vendor holding an unmigrated 2013 certificate is holding a lapsed one. Ask for the certificate and its scope statement specifically, and confirm that scope covers the actual systems and environments that will process your legal data, not just a corporate headquarters function unrelated to the product you are buying.
What a strong answer looks like: The vendor provides the actual SOC 2 Type II report (not just a summary letter) and the ISO 27001:2022 certificate with scope statement, and can speak specifically to any noted exceptions and their remediation.
What a weak answer looks like: “We are SOC 2 and ISO 27001 compliant” as a blanket statement, with reluctance to share the underlying reports, or an inability to confirm which version of ISO 27001 or which type of SOC 2 report they hold.
4. DPDPA compliance: what it means for your AI vendor specifically
The Digital Personal Data Protection Act, 2023 is not a general privacy platitude to check off; it creates specific, concrete obligations that apply directly to how an AI vendor processes personal data contained in your legal documents, employee records, or counterparty information.
Ask specifically: does the vendor’s contract include a DPDPA-compliant Data Processing Agreement, addressing purpose limitation, retention and deletion obligations, breach notification timelines, and restrictions on sub-processing? If the AI model itself is provided by a third party (a foundation model vendor separate from the legal software vendor), does that sub-processing relationship also satisfy DPDPA requirements, and is this reflected contractually?
This is where AI security evaluation intersects most directly with the previous two questions. If a vendor cannot clearly answer where data goes and whether it trains models on your data, they almost certainly cannot give you a coherent answer on DPDPA compliance either, because the DPDPA obligations depend entirely on having clear answers to exactly those questions.
What a strong answer looks like: A specific, documented DPA that names the sub-processors involved (including any underlying AI model provider), defines retention and deletion terms explicitly, and commits to breach notification timelines that let you meet your own obligations to the Data Protection Board of India.
What a weak answer looks like: “We comply with all applicable data protection laws” without a specific, reviewable DPA, or an inability to name which sub-processors are involved in the AI processing pipeline.
5. Data residency and sovereignty for Indian enterprises
For enterprises in regulated sectors specifically, and increasingly for enterprises generally, where data is physically stored and processed is a distinct question from general compliance, and it needs a specific, verifiable answer.
Ask: is data stored and processed on servers located within India, or does it flow to a data centre in another jurisdiction at any point in the pipeline, including for AI model inference specifically? For BFSI, insurance, and other RBI, IRDAI, or SEBI-regulated entities, confirm this against the specific residency requirements that apply to your sector, since these can be more stringent than general DPDPA requirements.
A subtlety that is easy to miss: a vendor can host your document storage in an Indian data centre while still routing AI inference queries to a model hosted outside India. Ask the residency question specifically about the AI processing step, not just about where your documents sit at rest.
What a strong answer looks like: A vendor who can confirm, specifically for the AI inference step and not just document storage, which region processes that data, and who offers this as a configurable, auditable setting rather than an assumption you have to take on faith.
What a weak answer looks like: A confident answer about document storage location that quietly does not address where AI queries are actually processed.
6. Audit trails: can you prove what the AI did and when
The final question is about accountability after the fact. When an AI tool flags a risk, extracts a clause, or drafts language, can you produce a record, later, showing exactly what the AI was given, what it produced, when, and which version of the model or configuration was in use at the time?
This matters for three concrete reasons. First, regulatory audits and inspections increasingly ask for evidence of how AI-assisted decisions were made, not just the outcome. Second, if an AI-flagged risk assessment is later disputed, whether internally or in a legal proceeding, the audit trail is what lets you reconstruct and defend the process. Third, for documents that may need to satisfy Section 65B / Section 63 BSA evidentiary requirements, a complete, tamper-evident record of the AI’s involvement supports that certification process rather than leaving a gap in it.
What a strong answer looks like: A dedicated, exportable audit log covering every AI interaction with a document: timestamp, user, input, output, and model version, retained for a defined period and accessible to your own compliance function without needing to request it from the vendor each time.
What a weak answer looks like: “We keep logs” without specificity on what is logged, for how long, or whether you can access and export them independently.
Red Flags to Watch for in Vendor Responses
Beyond the specific answers to the six questions above, certain patterns in how a vendor responds are worth treating as warning signs regardless of the specific content of their answer.
Marketing language substituting for technical specificity. “Bank-grade security” and “military-grade encryption” are not technical claims; they are phrases that sound impressive and mean nothing verifiable. A vendor confident in their actual architecture will answer with specifics (which encryption standard, which certification, which region) rather than adjectives.
Reluctance to put commitments in the contract. A verbal assurance that “we don’t train on your data” is worth nothing if the actual contract is silent on the point or contains language that permits exactly what was verbally denied. Any security or privacy commitment that matters should be in the DPA or the main contract, not just in a sales call.
Inability to name sub-processors. If a vendor’s AI capability depends on an underlying foundation model provider, they should be able to name that provider and explain the data relationship. An inability or unwillingness to do so suggests either a lack of internal clarity about their own architecture, or a reluctance to have that relationship scrutinised.
Certifications offered without documentation. Claiming SOC 2 or ISO 27001 compliance without being willing to share the actual report or certificate, or offering only a marketing page listing the certification rather than the underlying document, should prompt a direct request for the primary documentation before proceeding further.
Generic answers to India-specific questions. A vendor who answers DPDPA and data residency questions with the same generic response they would give a US or EU buyer, without India-specific detail, likely has not built India-specific compliance into their architecture and is retrofitting an answer rather than describing an actual deployment.
What Good Answers Actually Look Like
A genuinely strong vendor response across these six areas has a consistent character: specificity, contractual backing, and a willingness to be independently verified rather than simply trusted.
Concretely, this looks like: a named cloud infrastructure and specific Indian region for both data storage and AI inference processing; a contractual, default commitment that customer data is not used for model training; a shareable SOC 2 Type II report with a recent audit period and clear treatment of any exceptions, alongside a current ISO 27001:2022 certificate with a scope statement covering the actual product; a documented DPDPA-compliant DPA naming all sub-processors including any AI model provider; and an exportable, comprehensive audit trail covering every AI interaction with every document, accessible directly to the customer’s own compliance function.
This is the standard Legistify’s Codex platform is built against. Codex is deployed on AWS Bedrock within an architecture that keeps data processing within a defined, auditable environment, with SOC 2 Type II and ISO 27001 certification, DPDPA-compliant data processing terms, Indian data residency configuration for regulated entities, and a complete audit trail connected to the same contract, litigation, and notice management repository the legal team already works in. The point of setting out this six-question framework is not to describe Legistify specifically; it is to give any legal or IT team evaluating any AI-powered legal platform, including but not limited to Legistify, a rigorous, repeatable basis for that evaluation.
Conclusion
AI security evaluation for enterprise legal software is not a single checkbox question about whether a vendor is “secure.” It requires specific answers across six distinct areas: where data goes during processing, whether it trains the underlying model, what certifications genuinely cover, what DPDPA compliance actually requires of the vendor, where data physically resides, and whether the organisation can produce a complete audit trail after the fact. Vendors who answer these questions with specificity and contractual backing are worth serious consideration. Vendors who answer with confident-sounding generalities are asking you to trust an architecture they have not actually described. For Indian enterprise legal teams evaluating AI platforms in 2026, insisting on the specific answer, every time, is the discipline that separates a defensible procurement decision from an assumption that happens to have worked out.
