Generative AI & Data Sovereignty
Decide, with evidence, which AI workloads may run on hosted APIs, which need an enterprise tenant, and which must stay on infrastructure you control.
Quick answer
Generative AI data sovereignty training helps Indian PSUs, banks and regulated enterprises classify workloads and match each tier to a deployment pattern - public API, enterprise tenant with data-residency commitments, or self-hosted open-weight models on controlled infrastructure. The programme aligns decisions to the DPDP Act, RBI, SEBI and MeitY guidance and ends with a reference architecture and a pilot plan.
The sovereignty question in plain terms
Every prompt is a data transfer. The practical question is not whether to use generative AI but which class of data may travel to which class of system, under which contract, with which audit trail. Answering that once, clearly, unblocks the whole organisation.
Three deployment patterns compared
- Public hosted APIs - fastest capability, weakest control, suitable only for non-sensitive data.
- Enterprise tenants with a data processing agreement - strong default for most regulated work.
- Self-hosted open-weight models - full control and residency, higher cost and MLOps burden.
- Hybrid routing - a gateway that sends each request to the right tier automatically.
Regulatory alignment
Sessions map decisions to the DPDP Act, RBI outsourcing and IT governance guidance, SEBI expectations for market intermediaries, CERT-In incident reporting and MeitY advisories - so architecture choices survive audit rather than being reversed after one.
8 modules built around real workflows
How LLMs handle your data
Training, retention, logging and tenancy - what actually happens to a prompt in each deployment model.
Data classification tiers
A workable four-tier model from public to restricted, with examples from your own operations.
Residency & contracts
What to demand in a DPA, where the data physically sits, and how to verify vendor claims.
Enterprise tenant patterns
Configuring ChatGPT Enterprise, Microsoft 365 Copilot and Gemini for Workspace for regulated use.
Self-hosted open models
Open-weight model selection, GPU sizing, inference stacks, evaluation and lifecycle cost.
RAG on internal data
Vector stores, access control at retrieval time, and preventing cross-department data leakage.
Governance & audit
Logging, red-teaming, model change control and evidence a regulator will accept.
Pilot design
Pick one high-value use case, define success metrics, and write the 90-day pilot plan in the room.
- CIOs, CISOs and IT heads owning the AI platform decision.
- Data protection officers and compliance leads in regulated sectors.
- Enterprise architects designing an internal AI platform.
- Programme owners running a first generative AI pilot.
- Procurement teams evaluating AI vendors and contracts.
- A four-tier data classification model agreed across IT, legal and business.
- An approved deployment pattern for each tier, written down.
- A vendor and contract checklist for AI procurement.
- A reference architecture for internal RAG with access control.
- A named-owner pilot plan with success metrics and audit evidence.
Frequently asked questions
What is AI data sovereignty?
Data sovereignty means the data your organisation processes stays subject to Indian law and, where required, inside Indian infrastructure - including the prompts, files and embeddings sent to generative AI systems.
Can PSUs use ChatGPT at all?
Yes, for non-personal, non-classified work under a written policy. Sensitive workloads move to enterprise tenants with data-residency commitments or to self-hosted open-weight models.
Do you cover self-hosted open models?
Yes. The programme compares hosted APIs, enterprise tenants and on-premise open-weight deployments on cost, capability, security and audit posture.
Who should attend?
CIOs, CISOs, IT heads, data officers and programme owners in PSUs, banks, defence-adjacent organisations and regulated enterprises.
How long is the programme?
A one-day executive and architecture workshop, or two days when a reference architecture and pilot plan are produced in the room.
What is the output?
A tiered data-classification model, an approved deployment pattern per tier, and a pilot plan with named owners.