Blog · 6 min read

Responsible AI Governance for Captive Centers

By Anupam Tandon · March 2025

TL;DR

Governance is how you keep HQ comfortable while India ships. Cover model risk, data use, and escalation before scale makes mistakes expensive.

Responsible AI for a captive, without the theater

HQ will ask how you use models with customer data, how you test for harm, and who gets paged when something goes wrong. If your answer is “we have a Slack channel,” trust will stall.

You do not need a fifty-page ethics manifesto. You need a short, enforced set of rules that match your industry and your parent’s risk appetite.

Minimum useful governance

Inventory of AI systems in use (including shadow tools)
Allowed data classes and banned uses
Review gates for customer-facing or high-impact models
Named owners for model risk, security, and business outcomes
Incident path that includes HQ stakeholders

Keep it light enough to ship

Governance that blocks every experiment will drive people underground. Time-box reviews. Separate low-risk internal copilots from high-risk customer systems. Document exceptions.

30 / 60 / 90

30: One-page AI use policy for the center.
60: Inventory and risk tiers for current tools.
90: First high-impact use case through the full review path.

Takeaway

Responsible AI is an operating habit.

Write the rules early, enforce them calmly, and keep shipping inside the lines.

Counsel and security partners matter here. We can help you sequence the first policy with your setup plan.

“Structure matters, but the first three or four hires decide whether your India team becomes a capability or an expensive supplier.”
— Anupam Tandon, ContextDelta

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