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India’s Secretary for Electronics and Information Technology, S. Krishnan, has argued that the country should use a combination of open and proprietary AI models rather than rely on one category. The reasoning he gave is about data protection and strategic autonomy: proprietary systems can involve sending data outside the country and may learn from what users submit, while some open-weight models can be run without moving data out of India. The remarks were reported on October 8, 2026, and they describe a direction of travel, not a rule.
What Krishnan said, and where
Krishnan, Secretary of the Ministry of Electronics and Information Technology (MeitY), made the remarks on the sidelines of the release of the World Development Report 2026. PTI coverage, carried by The Economic Times, places them there. ANI coverage identifies the occasion as the India launch of the report by the IndiaAI Mission and the World Bank Group. Both reports are dated October 8, 2026.
The phrase “judicious mix” refers to using open and proprietary models together. Krishnan’s stated logic has two parts: awareness that proprietary systems carry data risks, and India’s intention to stay open economically and socially while preserving its strategic autonomy.
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The quotations, as reported
PTI reported the following remarks. No official MeitY transcript of the event was available when the reports were filed, so these should be read as PTI’s rendering of what he said.
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“Where proprietary models are to be used, we need to be aware that it involves the risk of data transfer and it involves the risk of those models learning at the cost of our data, at the cost of getting trained on what we give them. So there has to be a judicious mix.”
“We are an open country, both economically and socially. So we are using a combination of models to make sure that our overall strategic autonomy is preserved.”
ANI separately reported a second remark on open models: “Some of it and it is possible for us to use open source, open weight models without transferring data out of the country. That is also something that we are working on.”
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Data transfer
Using a proprietary model usually means sending prompts, documents or records to infrastructure the user does not operate. Krishnan’s point is that this step itself is a exposure, regardless of what the provider does afterwards. Where that infrastructure sits, and under which jurisdiction data is handled, determines how much of that exposure is acceptable for a given dataset.
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Learning from submitted data
The second risk is that a model may be trained on, or otherwise retain, what users submit. The reports frame this as a risk to be aware of. They do not establish that any particular proprietary service trains on user inputs. Whether it does depends on that provider’s settings, plan and contract, and those terms are the place to check.
What open-weight models change, and what they do not
ANI’s account of Krishnan’s remarks says open-source and open-weight models can be used without transferring data out of India. That is the practical advantage he pointed to: a model that an organisation can host itself can, in principle, keep prompts and outputs inside its own environment.
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Local deployment is not the same as security or compliance. A self-hosted model still needs secure infrastructure, access control, logging and patching, and the operator carries that responsibility. The reports also do not describe the architecture or security properties of any specific open-weight system, so it would be wrong to assume that every open-weight deployment is automatically safe for sensitive data.
How the trade-offs compare
Krishnan did not announce a scoring framework, and the reports supply no benchmarks, costs or named model evaluations. The table below lists the axes the reported concerns point to, so that an organisation can ask the right questions of each option. Where the coverage is silent, the cell says so.
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| Question | Proprietary model (hosted by a provider) | Open-weight model (self-hosted) |
|---|---|---|
| Where data is processed | Depends on the provider’s infrastructure; the reports do not identify any provider’s locations | Can be kept within India if the deployment is hosted there, as ANI reported |
| Training and retention on submitted data | A risk the reports name; whether it applies depends on the service’s own terms, which the reports do not audit | Not applicable to the model’s weights in the same way; logs and retained outputs remain the operator’s responsibility |
| Control over access and operations | Limited to what the contract and settings allow | Full, but the operator must secure infrastructure and manage access |
| Capability and availability | Not stated in the reports | Not stated in the reports |
| Cost comparison | Not stated in the reports | Not stated in the reports |
A practical check before choosing a model
The remarks point to a decision that depends on the data, not a general preference for one model type. An organisation that wants to apply the “judicious mix” idea can work through the following questions.
- Classify the data first. Public information, internal business records and personal or regulated data carry different risk, and the ANI report ties data-location decisions for empanelled firms to the nature and classification of the data.
- For each proprietary service, read its data-use and retention terms, and confirm in writing whether submitted inputs are used for training.
- Confirm where the provider processes and stores prompts, files, logs and outputs, and whether that matches the data’s classification.
- For any open-weight option, decide who operates the hosting, where it runs, and who controls access, logging and updates.
- Route sensitive workloads to options whose location and control you can verify, and reserve hosted services for data that the classification allows.
What is and is not established
The reports establish that a senior MeitY official called for a mix of open and proprietary AI models, named data transfer and training on submitted data as concerns, and linked model choice to strategic autonomy. ANI reported that open-weight models can be used without transferring data out of the country and that work on this is ongoing.
The reports do not establish that India has adopted a localisation mandate for AI models, that any named proprietary provider trains on user data, or that any open-weight model meets a particular security standard. They also do not give project milestones for the AI stack Krishnan described, which the reports list as physical infrastructure, computing, data, models and applications. Treat those as statements of direction until official documents on them are published.
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