Regulatory & Enterprise Compliance

The regulatory landscape for AI is moving fast. What was optional guidance two years ago is becoming enforceable law. And product managers who treat compliance as a legal team problem rather than a product design problem are going to find themselves in uncomfortable conversations when requirements catch up with their shipping roadmaps.

AI compliance is not merely regulatory but the core ability to build an enterprise of the future. Organizations need to build strong guardrails to protect data, ensure transparency, and remove bias. Working together and ensuring compliance throughout development stages can reduce the chances of legal exposure and enhance credibility.

1. Digital Personal Data Protection Act, 2023 (DPDPA), which governs how user data is collected, stored, and processed similar to GDPR.
2. The IT Act 2000 and IT Rules 2021 regulate digital intermediaries, content moderation, and cybersecurity obligations.
3. For fintech and payments products, the RBI oversees digital lending, wallets, and payment systems (recently overhauled under the Payments Regulatory Board Regulations, 2025)
4. SEBI governs investment and securities platforms.
5. The Competition Commission of India (CCI) is increasingly active in scrutinising digital market practices, having already penalised major tech firms for antitrust violations.

On the emerging tech side, India currently has no dedicated AI law, but the government launched the IndiaAI Safety Institute in early 2025 and is developing a national AI governance framework. ESG reporting standards are also tightening, with regulators pushing for greater transparency from businesses. Product teams should also be aware of TRAI rules for telecom and messaging products, and Consumer Protection (E-Commerce) Rules for marketplace platforms. The overall regulatory environment is evolving rapidly, and organizations are expected to increasingly leverage AI and automation tools to proactively identify compliance risks and streamline regulatory processes as India moves toward aligning its standards with global benchmarks.

It is a design input that needs to be present from the beginning of any AI feature development. Specifically, this means:

1. Understanding which regulatory category your product falls into before you design it
2. Documenting the decisions made about model selection, training data, and system design in ways that can be audited
3. Building explainability into the product where required, so users and regulators can understand how outputs were generated
4. Ensuring human oversight is built into workflows where automated AI decisions carry significant consequences for users