India’s MeitY Secretary S. Krishnan has called for a judicious mix of open and proprietary AI models, citing possible data-transfer and training risks when using proprietary systems. His remarks, reported on October 8, 2026, are a case for evaluating each deployment—not a claim that every proprietary service trains on user data or that every open model keeps information in India.
What did S. Krishnan say?
Krishnan made the remarks on the sidelines of the release of the World Development Report 2026. ANI described the occasion as the IndiaAI Mission and World Bank Group’s India launch of the report; PTI coverage published by The Economic Times also reported his comments. PTI coverage in The Economic Times and ANI’s report both described his support for using a combination of model types.
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PTI reported Krishnan’s concern that proprietary models can involve the risk of data being transferred and the risk of a model learning from what users submit. He argued for a “judicious mix” and linked that approach to preserving India’s strategic autonomy. These are reported concerns and objectives, not findings from an audit of a named AI provider.
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That depends on the provider, product, settings, and applicable contract. The reports do not identify a provider or establish whether any particular prompt or file is retained or used for training. Krishnan’s comments describe a possible risk; they do not establish that all proprietary models train on user data.
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Before sending sensitive information to a service, an organization should check the terms and settings for the specific product. In particular, it should establish where prompts, files, logs, and outputs are processed or stored, how long they are retained, and whether they may be used to improve or train models. The news reports do not compare any providers’ policies.
Can open-source or open-weight AI keep data in India?
ANI reported Krishnan saying that some open-source or open-weight models could be used without transferring data out of India, and that work on this was ongoing. This describes a possibility, not a guarantee about every open model or deployment.
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Using open weights does not by itself keep data local. That depends on how the system is deployed and operated: an organization must verify where inference and storage take place, including for submitted files, logs, and backups. Self-hosting can provide deployment choices, but it also makes the operator responsible for securing the infrastructure and controlling access. The reports do not identify a model, architecture, or security assessment.
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Why combine open and proprietary models?
Krishnan’s reported position is not to choose one category for every task. A mix can let an organization make decisions based on the sensitivity of its data and the arrangements for a particular system. The practical comparison is deployment-specific:
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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- Data location and transfer: Establish where inputs and outputs are processed and stored, and whether information leaves India.
- Retention and training: Check the service’s terms, settings, and contract for how inputs are handled.
- Operational control: Consider who secures the infrastructure, manages access, and maintains the system.
- Capability and availability: Evaluate whether a particular model meets the task’s needs. Krishnan’s comments did not provide benchmarks, costs, or named model comparisons.
These are practical considerations for organizations assessing a model; they are not a scoring framework announced by Krishnan.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do the remarks mean India has mandated data localization?
No blanket localization mandate is established by the reports. ANI said that data-location decisions for empanelled firms would depend on the nature and classification of the data. Krishnan also described domestic AI capability building and strategic autonomy as objectives, rather than announcing a universal rule that all AI data must stay in India.
What is known about India’s AI stack plans?
ANI reported Krishnan describing work on an AI stack in India spanning physical infrastructure, computing, data, models, and applications. The coverage does not provide project milestones or technical verification, so it supports describing this as a direction of work—not as evidence that a particular capability or local deployment is already in place.
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