Module 9 · DPIA — Data Protection Impact Assessment Under DPDP

Manish Garg
Manish Garg Associate of (ISC)² · RingSafe
Apr 27, 2026
3 min read
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Last updated: April 29, 2026

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Why this module exists. §10(2)(c) requires Significant Data Fiduciaries (SDFs) to conduct DPIAs. The Rules (when published) will likely extend DPIA expectations to high-risk processing by all Data Fiduciaries. Most Indian businesses have never done one. The methodology is more practical than the legal text suggests.

Why this module exists. §10(2)(c) requires Significant Data Fiduciaries (SDFs) to conduct DPIAs. The Rules (when published) will likely extend DPIA expectations to high-risk processing by all Data Fiduciaries. Most Indian businesses have never done one. The methodology is more practical than the legal text suggests.

What a DPIA is

A structured assessment of a processing activity:

  1. What data do you process, why, how?
  2. Is the processing necessary and proportionate?
  3. What risks does it pose to data principals?
  4. What mitigations bring the risks to acceptable levels?
  5. What residual risk remains?

Output: a document. Reviewed periodically. Available to the regulator on request.

When DPIA is required

Currently mandatory for SDFs. Best-practice triggers for any Data Fiduciary:

  • New product or service that processes personal data at scale
  • Processing of children’s data (§9 — verifiable parental consent required)
  • Processing of sensitive categories: health, biometric, financial, location, sexual orientation, caste, religion
  • Automated decision-making with significant effect (loan approval, hiring)
  • Large-scale monitoring (CCTV at scale, employee monitoring, public-area analytics)
  • Use of AI / ML on personal data (model training, inference)
  • Cross-border transfers to non-routine destinations
  • New vendor / data processor handling sensitive data

The 10-section DPIA template

  1. Project description — what business activity, why, who’s the stakeholder.
  2. Data inventory — categories, volumes, sources, recipients.
  3. Lawful basis — consent, contract, legal obligation, legitimate use under §7.
  4. Data flow diagram — where data goes, including processors and cross-border.
  5. Necessity and proportionality — is this the minimum data needed? Is there a less-intrusive alternative?
  6. Risk identification — what could go wrong (re-identification, breach, discrimination, surveillance creep, lock-in).
  7. Risk assessment — likelihood × impact for each risk.
  8. Mitigation — controls that reduce risk.
  9. Residual risk — what remains after mitigation; sign-off by accountable executive.
  10. Review schedule — when DPIA will be revisited (annually or on material change).

The Significant Data Fiduciary designation

The Central Government can notify entities as SDFs based on:

  • Volume and sensitivity of personal data processed
  • Risk to electoral democracy / sovereignty
  • Risk to national security / public order
  • Other relevant factors

SDFs have additional obligations:

  • Appoint a Data Protection Officer (DPO) based in India
  • Conduct annual independent audit
  • DPIA + periodic risk assessment
  • Other measures as may be prescribed

As of 2026 mid, no SDF list has been notified. Likely candidates: large e-commerce, social media, banks, telecom, healthtech with population-scale data.

The risk-assessment matrix

Risk Likelihood (1-5) Impact (1-5) Score Mitigation
Re-identification of pseudonymised data via link to other datasets 3 4 12 k-anonymity ≥ 5; differential privacy on aggregates
Breach of stored personal data via cloud misconfiguration 2 5 10 Encryption at rest; CSPM scanning; MFA on console
Vendor sub-processor compromise 3 3 9 DPA with notification; sub-processor list audit
Discrimination from automated decision (credit scoring) 3 4 12 Fairness testing pre-launch; human-in-loop for adverse decisions
Disproportionate surveillance via location collection 4 3 12 Reduce granularity; opt-in; on-device processing

Score > 12 = explicit executive sign-off required. Score > 16 = redesign before proceeding.

Real-world case study

Hypothetical: Indian healthtech building an AI model that predicts disease risk from patient records.

DPIA findings:

  • Processing: highly sensitive (health data per ABDM)
  • Lawful basis: consent + legitimate use for healthcare (§7)
  • Risks: model bias by demographic, re-identification of “anonymised” training data, third-party processor (cloud GPU vendor) gaining access
  • Mitigations: differential privacy on training; on-device or India-region inference; specific consent UX with revocation; DPA with cloud GPU provider including audit right
  • Residual risk: medium; quarterly review
  • Sign-off: CTO + Chief Medical Officer + DPO

Defender’s checklist

  • DPIA template documented and approved by legal + privacy team.
  • DPIA triggered automatically as part of new-feature / new-vendor / major-change process.
  • Reviewed annually for the same processing activity.
  • Available on request to DPB during inspection.
  • Linked to risk register — DPIA risks tracked in your enterprise risk system.
  • DPO sign-off on every DPIA (if SDF; recommended for all).
  • Public summary for high-stakes processing — increasingly an industry expectation.
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