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DPDP 3.6.2: DPDP & RPA – Data erasure failed due to untracked bot copies

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He asked for erasure. The hospital cited regulatory retention. But the bot had created fourteen data instances across staging environments, output logs, and a marketing file — none covered by that retention basis.

The Scenario

Ramesh is a 52-year-old former patient of a corporate hospital chain in Hyderabad. He had been treated at the hospital for a minor procedure in 2021 and had not returned since. In 2024, the hospital implemented an RPA solution to automate its patient record management — including pulling records from the clinical management system, flagging inactive accounts, generating discharge summaries, and triggering follow-up communications.

Ramesh received an automated follow-up message in 2025, referencing his 2021 procedure. Concerned, he submitted a request for erasure of his personal data under the DPDP Act. The hospital’s patient services team replied that his records had to be retained for medical and regulatory purposes — a position that was partially accurate for core clinical records under applicable health law.

However, Ramesh’s personal data had also been ingested into the RPA bot’s active processing queue, the bot’s intermediate staging environment, the output logs of every follow-up communication run, and a marketing segmentation file generated by the bot for the hospital’s patient re-engagement programme. None of these secondary processing instances were covered by the regulatory retention obligation cited by the hospital.

Ramesh’s erasure request was thus effectively denied for his entire data footprint — when, in reality, only a narrow subset of it was covered by any lawful retention basis.

The DPDP Act Position

This scenario illustrates one of the most practically important DPDP challenges in RPA deployments: the gap between data that is lawfully retained and data that is incidentally retained across multiple processing environments created by bot execution.

First — Section 12(3) erasure rights apply to every processing instance. Section 12(3) of the DPDP Act requires the Data Fiduciary to erase personal data upon request, unless retention is necessary for the specified purpose or for compliance with any law in force. This obligation applies to all instances of personal data — not merely the primary source record. Ramesh’s personal data in the bot’s staging environment, the output logs of follow-up communications, and the marketing segmentation file had no regulatory retention basis. Each instance was subject to his erasure request. Declining to erase these instances while citing a regulatory obligation applicable only to core clinical records is a misapplication of the retention exception.

Second — Rule 8 purpose limitation for retained data. Rule 8 of the DPDP Rules governs the time period after which personal data must be erased when its specified purpose is served. Clinical data retained under health law does not carry a blanket licence for use in marketing automation or patient re-engagement analytics. The specified purpose of clinical record retention is regulatory compliance — not marketing. Processing retained data for marketing through an RPA bot is a purpose drift violation under Rule 8, compounded by the absence of any fresh consent for that use.

Third — RPA-generated output logs as personal data. The DPDP Act’s definition of processing in Section 2(x) includes “recording, organisation, structuring, storage, adaptation, retrieval, use, alignment or combination.” Every time the bot read Ramesh’s record, the act of retrieval was processing. Every output log generated by that retrieval stored personal data in a new location. The hospital’s RPA deployment had created multiple secondary personal data stores — none of which the hospital had mapped, none of which it could manage in response to a Data Principal rights request.

The IS Audit 3.0 Perspective

The study material on IS Audit 3.0 by ICAI specifically flags vaguely defined business continuity and maintenance plans as an RPA risk. In the context of DPDP compliance, this observation extends to data lifecycle management: organisations that deploy RPA without mapping the full data footprint of every bot process — including staging environments, output files, logs, and downstream data stores — create invisible personal data inventories that are unresponsive to Data Principal rights.

IS Audit 3.0 Governance and Controls requires deployment frameworks that account for data security and operational risk across the full RPA lifecycle. Data lifecycle governance — specifically, the ability to trace and erase personal data from every processing environment a bot touches — is a core component of that framework under the DPDP Act.

The Compliance Fix

Healthcare organisations and any entity deploying RPA processes on personal data must:

→ Conduct a full data footprint mapping for every RPA process before deployment. The map must identify every system the bot accesses, every intermediate file or staging environment it creates, every output it generates, and every downstream system that receives its outputs.

→ Apply Section 12(3) erasure obligations to every node in the data footprint — not merely the primary source system. A Data Principal rights response must be capable of reaching and clearing every instance of the individual’s data across the full bot execution chain.

→ Apply Rule 8 purpose limitation controls technically: personal data retained for regulatory compliance must be firewalled from RPA processes used for marketing, re-engagement, or analytics. These are different purposes with different lawful bases.

→ Maintain a personal data register that is updated whenever a new RPA process is added or modified, so that the full scope of automated processing is always visible for compliance management.

→ Designate a clear accountability owner for each RPA process who understands both its operational function and its data privacy obligations under the DPDP Act.

Ramesh’s hospital retained his data for good reasons. However, it processed it for additional reasons it had neither disclosed nor obtained consent for. The DPDP Act makes that distinction matter.

The compliance deadline is 13 May 2027. Regulatory retention is a defence for what it covers. It is not a blanket exemption for the entire data trail a bot leaves behind.

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors: This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.6.2: DPDP & RPA – Data erasure failed due to untracked bot copies

DPDP 3.6.1: DPDP & Robotic Process Automation (RPA)

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INTRODUCTION

RPA and DPDP compliance is a pairing that most organisations have not yet considered — and that oversight is becoming a serious liability.

Robotic Process Automation, as defined by study material on IS Audit 3.0 by ICAI, refers to software tools that automate human activities that are manual, rule-based, and repetitive. RPA bots replicate the actions of a human interacting with software applications — logging in, reading data, copying records, filling forms, sending communications, and closing cases — all without human involvement at the point of execution. They work across functions and applications, often with privileged system access, processing thousands of transactions per hour in the background.

Here is the compliance problem that this efficiency creates: the DPDP Act, 2023 defines “processing” in Section 2(x) as any wholly or partly automated operation or set of operations performed on digital personal data. This definition covers collection, recording, organisation, structuring, storage, adaptation, retrieval, use, alignment, combination, indexing, sharing, disclosure, restriction, erasure, and destruction. Every time an RPA bot touches a field containing personal data — a name, a PAN, an address, a bank account number, a medical record — that action is processing under the Act.

Furthermore, Section 2(b) of the DPDP Act defines “automated” as any digital process capable of operating automatically in response to instructions given or otherwise. RPA is, by definition and by design, automated. Consequently, RPA bots are not merely productivity tools. They are automated personal data processors — and the Data Fiduciary that deploys them is fully responsible for every record they touch.

The Study material on IS Audit 3.0 by ICAI identifies operational and execution risk as a primary RPA challenge: when bots are deployed into operations without a proper operating model, roles and responsibilities become blurred. Under the DPDP Act, blurred roles in data processing are not a technology governance gap — they are a compliance failure. The responsibility remains clearly with the Data Fiduciary, regardless of whether a human or a bot performed the processing.

Three stories make this concrete.

Story 1 — Priya’s Salary Data and the Bot That Shared It Across Departments

The Scenario

Priya works as a senior executive at a manufacturing company in Pune. Her employer had deployed an RPA solution to automate its monthly payroll reconciliation process. The bot was configured to pull salary, tax deduction, and bank account data from the HR system, cross-reference it with the finance system, and generate a reconciliation report that was emailed to a shared distribution list covering the finance team, the CFO’s office, and — due to a misconfiguration in the bot’s output routing — the entire operations management team as well.

For seven months, Priya’s salary, tax deductions, advance loans, and bank account details were visible to approximately forty colleagues who had no business need for that information. No human reviewed the bot’s output routing. No alert was raised. The misconfiguration was discovered only when an operations manager mentioned a colleague’s salary in a team meeting.

Priya filed a complaint with HR. The company’s response was that the error had been in the bot’s configuration, implying that the technology, rather than the organisation, bore responsibility.

The DPDP Act Position

The company’s response was legally incorrect in two distinct ways.

First — Data Fiduciary liability is non-derogable under Section 8(1). Section 8(1) of the DPDP Act makes the Data Fiduciary responsible for compliance with the provisions of the Act in respect of processing carried out by it or on its behalf by a Data Processor. An RPA bot deployed by the company, on the company’s systems, processing the company’s HR and payroll data, is unambiguously acting on the company’s behalf. Consequently, the misconfiguration was not the bot’s fault. It was the company’s compliance failure.

Second — Unauthorised disclosure constitutes a personal data breach under Section 2(u). The DPDP Act defines a personal data breach as any unauthorised processing of personal data, or accidental disclosure, acquisition, sharing, use, alteration, destruction, or loss of access to personal data that compromises the confidentiality, integrity, or availability of personal data. Priya’s salary and bank account data being shared with forty employees who had no authorisation to receive it is a textbook personal data breach — accidental, but real and attributable.

Under Section 8(6) and Rule 7 of the DPDP Rules, the company was required to notify both the Data Protection Board of India and each affected Data Principal — including Priya — of the breach, without delay. A description of the breach, its nature and extent, the consequences likely to arise from it, the measures being taken to mitigate risk, and the safety measures that Priya could take were all required to be communicated promptly. None of this was done.

Third — Rule 6 security safeguard failure. Rule 6(1)(b) of the DPDP Rules requires the Data Fiduciary to implement appropriate measures to control access to the computer resources used by it or by its Data Processor. Rule 6(1)(c) requires visibility on the accessing of personal data through appropriate logs, monitoring, and review. The RPA bot’s output routing was misconfigured for seven months. No access control review detected this. No log monitoring flagged the anomaly. Both requirements were violated — not as a single incident, but as a persistent governance failure sustained across thirty-plus payroll cycles.

The IS Audit 3.0 Perspective

IS Audit 3.0 study material identifies operational and execution risk as one of the most significant RPA challenges. Specifically, when bots are deployed without a proper operating model and without clearly defined roles and responsibilities, problems in production environments can go undetected for extended periods. Priya’s case is exactly this: a production bot operating outside its intended parameters for seven months, with no human oversight mechanism in place to detect the deviation.

IS Audit 3.0 material on, Governance and Controls is equally direct. Governance of RPA must cover operational risk and data security through a cross-functional oversight team. Legal, risk, and IT teams must all be involved in the processes being automated. In Priya’s case, neither legal nor data privacy considerations appear to have been included in the bot’s deployment framework — an omission that allowed a structural data breach to persist across a full financial year.

The Compliance Fix

Organisations deploying RPA for payroll, HR, or any process involving personal data must:

→ Treat every RPA bot as an automated data processor and apply Rule 6 access controls and logging standards to every process it executes. Bot-level access must be scoped to the minimum data necessary for the specific task — not system-wide access because it is convenient.

→ Configure mandatory output routing reviews before any bot deployment that involves personal data in its output. Distribution lists must be verified against a need-to-know register, not simply inherited from historical email groups.

→ Implement bot execution monitoring logs under Rule 6(1)(c) that flag anomalies — such as data being routed to recipients outside the specified access list — and trigger human review.

→ Establish a breach identification and notification workflow specifically for RPA processes. When a bot misconfiguration results in unauthorised disclosure, the organisation must be capable of identifying it, classifying it as a breach, and notifying under Rule 7 — all within the required timeframe.

→ Designate an RPA governance owner who is responsible for DPDP Act compliance across all bot deployments — not merely for technical performance.

Priya’s employer blamed the bot. Under the DPDP Act, the bot has no liability. The organisation does.

The compliance deadline is 13 May 2027. The bot had no liability. The organisation did — from the first payroll cycle.

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors: This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.6.1: DPDP & Robotic Process Automation (RPA)

DPDP 3.5.3: DPDP and Data Analytics – Retail Chain Loyalty Points

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Nalini’s Retail Loyalty Programme and the Profile She Never Agreed To

The Scenario

Nalini is a 29-year-old working professional in Mumbai. She had enrolled in a large retail chain’s loyalty programme several years ago. At the time of enrolment, she provided her name, phone number, email address, and date of birth in exchange for discount points.

Over the following years, the retail chain built an increasingly sophisticated analytics capability. It combined Nalini’s purchase history — over 400 transactions across groceries, personal care, clothing, and home products — with her app browsing data, location signals from in-store beacons, and inferred demographic data purchased from a data enrichment vendor. The analytics platform then classified her into behavioural segments — “health-conscious millennial,” “premium spender,” “likely expecting” — and used these segments to target her with personalised advertising and to adjust the pricing of certain dynamic offers.

Nalini had not been informed that her purchase history would be used to infer personal attributes. She had not consented to location tracking via in-store beacons. She had no knowledge of the data enrichment vendor or the categories of inferred data it supplied. Furthermore, when she eventually tried to delete her account, the platform told her that her historical transaction data had to be retained for seven years under applicable tax and commercial regulations — and that her behavioural segments were derived data and therefore could not be erased.

The DPDP Act Position

Nalini’s situation is the most layered of the three stories, because it involves multiple compliance failures running simultaneously across the full analytics pipeline.

First — Consent obtained for enrolment is not consent for analytics profiling. Section 6(1) requires consent to be limited to personal data that is necessary for the specified purpose. The specified purpose at enrolment was participation in a loyalty programme for discount points. Predictive profiling, behavioural segmentation, demographic inference, and dynamic pricing optimisation are materially different purposes — each requiring its own consent basis.

Rule 3 of the DPDP Rules further tightens this standard. The notice must give an itemised description of personal data and a specific description of the goods, services, or uses that processing will enable. A loyalty programme enrolment form that says “your data will be used to improve your shopping experience” does not satisfy this requirement when the actual use involves inferring pregnancy likelihood or segmenting users by spending disposition.

Second — Inferred data is personal data under Section 2(t). The DPDP Act defines personal data as any data about an individual who is identifiable by or in relation to such data. Behavioural segments derived from Nalini’s purchasing patterns — “likely expecting,” “premium spender” — are data about an identifiable individual. They are personal data. Their creation and storage are processing activities subject to the Act’s full consent and purpose requirements. The retail chain’s argument that derived data cannot be erased does not hold under Section 12(3) — the erasure right applies to personal data in whatever form it exists, including derived outputs of analytics processing.

Third — Third-party data enrichment without disclosure under Section 5. The retail chain ingested demographic inference data from an external vendor to enrich Nalini’s profile. This enrichment vendor is a Data Processor under the Act. Its data categories were never disclosed to Nalini in the original notice. Section 5(1) and Rule 3 together require that the source and category of every piece of personal data used in processing be disclosed to the Data Principal at the time of notice. Using enrichment data that was never disclosed violates this requirement.

Fourth — In-store beacon tracking without consent under Section 6. Location signals collected from in-store beacons constitute processing of personal data. Nalini had enrolled in a loyalty programme — she had not consented to being tracked by physical location sensors during her store visits. This is a separate processing activity requiring a separate consent.

Fifth — The “seven-year retention” argument does not justify analytics profiling. The retail chain’s argument that transactional data must be retained for seven years under commercial law is legally accurate for transaction records — but retention for regulatory compliance does not authorise the retained data to be used for new purposes. Section 8(1) and the purpose limitation principle require that data retained for regulatory compliance be isolated from analytics processing pipelines. Retaining data is not the same as having a continuing licence to process it for all purposes.

The IS Audit and Puttaswamy Perspective

IS Audit 3.0 by ICAI describes data analytics as a series of processes designed to sanitise raw data and transform it into a form appropriate for analysis to facilitate decision-making. In Nalini’s case, the analytics pipeline transformed loyalty programme enrolment data into a comprehensive personal profile — a transformation that was never disclosed, never consented to, and that the DPDP Act does not permit on the basis of an original loyalty enrolment consent alone.

The Puttaswamy judgment (2018) is especially pertinent here. The court directly addressed the concept of the “quantified self” — individuals generating vast amounts of data through everyday activities, without conceiving of themselves as having volunteered it for profiling. Justice Kaul’s analysis of profiling under data protection law applies directly: using personal data to evaluate aspects of an individual’s life — her economic situation, personal preferences, behaviour, or movements — is a privacy-sensitive activity that requires explicit, specific authorisation. A discount programme sign-up is not that authorisation.

Furthermore, the court’s description of the “Big Data” era as one where data sets are linked, searchable, and permanently collected maps directly to Nalini’s situation: her 400 transactions, combined with location signals and enrichment data, created exactly the kind of linked, searchable, permanent dataset that the Puttaswamy judgment identified as requiring robust legal governance.

The Compliance Fix

Retail loyalty programmes and any organisation using analytics for customer profiling must:

→ Separate enrolment consent from analytics consent. Loyalty enrolment and behavioural profiling are different processing activities. They require separate, specific consent notices that clearly describe what profiling will occur, what data will be used, and what outputs will be generated.

 → Treat all derived data — segments, inferences, scores — as personal data under Section 2(t). Design erasure mechanisms that reach derived data, not just source data.

→ Disclose every external data enrichment vendor by name and category in the consent notice under Rule 3. “Third-party partners” is not adequate disclosure.

→ Obtain separate consent for in-store location tracking via beacons. Physical location processing is a distinct activity requiring a distinct lawful basis.

 → Apply a regulatory retention firewall: data retained for tax or commercial law compliance must be technically isolated from live analytics pipelines. Regulatory retention and analytics processing are separate activities requiring separate legal bases.

 → For organisations using analytics on large volumes of personal data for profiling, segmentation, or prediction, a DPIA under Rule 13 of the DPDP Rules is a mandatory exercise — not an optional governance measure.

Nalini’s retail chain ran excellent analytics. However, it ran them on a broken consent architecture. The quality of the insights does not determine the lawfulness of the processing.

The Central Compliance Question for Data Analytics Deployments

Data analytics is not inherently incompatible with the DPDP Act. Organisations can legitimately use data analytics for internal operations, fraud detection, product improvement, and customer service — provided the analytics is designed from the ground up around a compliant consent and purpose framework.

The compliance failure that runs through all three stories in this episode is the same: analytics capabilities grew beyond the consent architecture that was in place when data was originally collected. Devika’s lender ran predictive models on data never disclosed to her. Harini’s hospital activated a marketing module without patient consent. Nalini’s retail chain built a behavioural profile using data from sources never itemised in any notice.

Every organisation running data analytics on personal data must therefore answer six questions before each analytics deployment:

One. Does our Rule 3 notice itemise every data source feeding this analytics model — including external enrichment vendors, third-party data providers, and inferred or derived data categories?

Two. Is every analytical use case — profiling, segmentation, prediction, decision-making — identified as a specific stated purpose at the time of consent?

Three. Are all Data Processors in the analytics pipeline — vendors, cloud platforms, enrichment providers — covered by Section 8(2) contracts that limit them to the specified purpose?

Four. Does our analytics pipeline have a purpose limitation control that prevents data collected for one purpose from feeding into models built for a different purpose?

Five. Can we honour a Section 11 access request accurately enough to tell a Data Principal which data sources contributed to a decision about her?

Six. Can we honour a Section 12(3) erasure request that reaches derived data — segments, scores, and inferences — not just source records?

IS Audit 3.0 study material by ICAI identifies data privacy risk as the primary challenge for analytics deployments. Rule 13 of the DPDP Rules mandates annual DPIA and audit for Significant Data Fiduciaries using analytics at scale. Together, they establish that governance of data analytics is not optional — it is the compliance baseline.

What Responsible Data Analytics and DPDP Compliance Looks Like

The organisations that will manage this intersection well are not those with the most sophisticated models. They are the ones that invested in governance architecture before their analytics capabilities outgrew their consent frameworks.

Responsible data analytics and DPDP compliance means one thing above all else: every data point entering an analytics pipeline must be traceable to a consent record that specifically authorises its use for that specific analytical purpose. Where that traceability breaks down — at the point of data collection, at the point of third-party enrichment, at the point of model output — the compliance obligation is violated, regardless of how valuable or accurate the analytical output may be.

The value of an insight does not determine the lawfulness of how it was generated. The DPDP Act makes that unambiguous. The compliance deadline is 13 May 2027.

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors: This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.5.3: DPDP and Data Analytics – Retail Chain Loyalty Points

DPDP 3.5.2: DPDP and Data Analytics – Healthcare Analytics

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The Scenario

Harini is the CFO of a mid-sized private hospital group in Tamil Nadu. The group had invested significantly in a business intelligence platform, which pulled data from their hospital management system, OPD records, pharmacy transactions, and diagnostic reports. The platform generated dashboards that the management team used for operational decisions — bed utilisation, consumable planning, department profitability, and patient retention.

At the end of the financial year, the analytics vendor offered a value-added service: a patient re-engagement module that would use the same data to identify patients who had not returned for follow-up visits and send them targeted health alerts via SMS and email. The hospital management approved the feature.

What the hospital had not done was revisit the consent it had obtained from patients at registration. That consent — collected through a standard hospital intake form — covered the processing of personal data for the provision of medical care and for billing purposes. It did not cover the use of diagnostic and prescription data for automated marketing outreach.

The DPDP Act Position

This scenario involves a textbook purpose limitation failure — one that also carries significantly aggravated risk because the personal data in question is health data.

First — Processing beyond the specified purpose under Section 8(1). Section 8(1) of the DPDP Act makes the Data Fiduciary responsible for compliance with the Act in respect of processing carried out by it or by any Data Processor on its behalf. The analytics vendor, when it ran the re-engagement module, was a Data Processor acting on the hospital’s behalf. Consequently, the hospital bears liability for this processing. The specified purpose under which patient data was collected was medical care and billing. Re-engagement marketing is not a purpose covered by that consent — regardless of whether the hospital subjectively considered it beneficial to patients.

Second — Data Processor contract inadequacy under Section 8(2). Section 8(2) requires the Data Fiduciary to ensure that its Data Processor processes personal data only for the specified purpose under a valid contract. The analytics vendor’s contract, in this case, covered the provision of BI dashboards and reporting. The re-engagement module was a new capability added under an expanded commercial arrangement — but without a corresponding update to the Data Processor contract to reflect the new processing purpose, and without fresh consent from patients for that purpose.

Third — Retention and purpose drift under Rule 8. Rule 8 of the DPDP Rules, 2025 governs the time period for which personal data may be retained once its specified purpose is served. Health data collected for a specific episode of care — an OPD visit, a diagnostic test — remains subject to the purpose for which it was collected. Feeding that data into a marketing analytics module after the specified purpose has been served is a purpose drift violation, compounded by the retention obligation.

The IS Audit 3.0 Perspective

IS Audit 3.0 by ICAI identifies data privacy and confidentiality as the leading challenge for data analytics deployments, specifically calling out the risk of data being misused once it is accessible to an analytics platform. Hospital management systems are among the most data-rich environments in any organisation. However, that richness also makes them among the highest-risk environments for purpose drift — the gradual expansion of analytics use cases beyond the original data collection purpose.

Importantly, IS Audit 3.0 material by ICAI also flags the risk that insufficient evidence is retained on file about the procedures and inputs used in analytics processing. In the hospital’s case, there was no audit trail linking the re-engagement module’s data inputs to a consent record. Consequently, if the Data Protection Board of India ever investigated, the hospital would be unable to demonstrate that lawful basis existed for the marketing processing.

The Compliance Fix

Healthcare organisations — and any organisation running analytics on data collected for a primary service purpose — must:

→ Conduct a purpose mapping exercise before activating any new analytics module. Every new use case must be traced back to a consent record that explicitly covers it.

→ Review and update all Data Processor contracts each time the analytics vendor introduces a new processing module or capability. Section 8(2) compliance requires the contract to reflect the actual processing being performed.

→ Maintain consent records that are granular enough to support an audit trail. “Patient consented to data processing” is not sufficient. The consent record must specify what data was covered, for what purpose, and at what point in time.

→ Apply purpose limitation controls at the analytics platform level — configure data access so that each module can only access the data categories covered by the relevant consent record.

→ Obtain fresh, specific consent before using health data for any purpose beyond direct patient care and billing — including patient outreach, marketing, research, and quality benchmarking.

Harini’s hospital had good intentions. However, the DPDP Act governs outcomes, not intentions. Unlawful processing remains unlawful even when it is well-meaning.

The compliance deadline is 13 May 2027. Health data analytics is high-risk processing. Its governance must match that risk.

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors: This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.5.2: DPDP and Data Analytics – Healthcare Analytics

DPDP 3.5.1: DPDP and Data Analytics

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INTRODUCTION

Data analytics and DPDP compliance may appear to be an unlikely pairing. One is a business tool. The other is a legal framework. Yet they collide precisely because analytics, at its most powerful, is built on personal data — and the DPDP Act, 2023 governs every piece of it.

IS Audit 3.0 study material by ICAI defines data analytics as the science of examining raw and unprocessed data with the intention of drawing conclusions from the information thus derived. At its simplest level, descriptive analytics tells us what happened. Diagnostic analytics explains why it happened. Predictive analytics forecasts what may happen next. Prescriptive analytics recommends what should be done. Cognitive analytics — the highest tier — uses Big Data and artificial intelligence to recognise patterns and take proactive action.

Each tier of this evolution processes more personal data, draws more intimate inferences, and creates more consequential outputs for the individuals at the centre of that data. By the time an organisation is running predictive or cognitive analytics at scale, it is not merely analysing data. It is constructing detailed models of human behaviour — including patterns the individual herself may not be aware of. The Puttaswamy judgment (2018) confronted precisely this reality when it held that data mining and knowledge discovery processes can create new knowledge about individuals, including facts that even those individuals did not possess about themselves.

The DPDP Act does not prohibit analytics. However, it requires that every stage of the analytics pipeline — from data collection to model output to decision-making — be governed by a lawful basis, a specific purpose, and a respect for Data Principal rights. Three stories show exactly where that governance breaks down in practice, and what responsible Data analytics and DPDP compliance looks like.

Story 1 — Devika’s Loan Application and the Score That Came From Nowhere

The Scenario

Devika is a 33-year-old chartered accountant in practice who applied for a personal loan from a digital lending platform. Her credit profile was clean. Her income was verifiable and above the threshold. Nevertheless, the platform’s system rejected her application within seconds, flagging her as a high-risk borrower.

Devika asked for a reason. The lender’s customer service team explained that an internal risk model had assessed her application and that a detailed explanation could not be provided. She then invoked her right to know what personal data the platform held about her and how it was being processed. The platform sent her a generic response listing basic data categories — name, income, PAN — but made no mention of the behavioural and transactional data from third-party sources that the model had actually used.

Devika had been profiled based on data she had not provided and had never been informed about.

The DPDP Act Position

Three distinct compliance failures arise from Devika’s situation.

First — Absence of notice for third-party data under Section 5. Section 5(1) of the DPDP Act requires that every request for consent be accompanied or preceded by a notice informing the Data Principal of the personal data to be processed and the purpose for which it will be processed. Rule 3 of the DPDP Rules reinforces this: the notice must give an itemised description of the personal data, including its source. When the lender ingested Devika’s behavioural data from third-party platforms without disclosing this as a data source in its notice, it failed this obligation entirely.

Data analytics pipelines routinely pull data from multiple external sources — credit bureaux, social media signals, transaction aggregators, app usage patterns, telecom data partners. Every one of these data categories must be disclosed, itemised, and tied to a specific processing purpose in the notice. A generic privacy policy that refers to “data from third parties” does not satisfy this standard.

Second — Processing beyond the stated purpose under Section 6(1). Devika’s consent, to the extent it was obtained, covered the processing of her application data for a lending decision. Behavioural profiling using third-party data analytics is a materially different processing activity. Section 6(1) limits consent to the personal data that is necessary for the specified purpose. Conducting wide-scope predictive profiling on data not covered by the stated purpose, and not disclosed in the notice, has no lawful consent basis.

Third — Right to access information violated under Section 11(1). Section 11(1) of the DPDP Act gives every Data Principal the right to obtain from the Data Fiduciary a summary of personal data being processed, a description of processing activities, and the identities of all Data Fiduciaries and Data Processors with whom her personal data has been shared. The platform’s generic response — listing only basic data categories — is an inadequate discharge of this obligation. Devika had a statutory right to know that third-party data was feeding the model that rejected her. She was denied that right.

The IS Audit 3.0 and Puttaswamy Perspective

IS Audit 3.0 material by ICAI explicitly identifies data privacy and confidentiality as a primary risk of deploying data analytics, noting that the copying and storage of client data risks breach of confidentiality and data protection laws. In an analytics context, this risk is not limited to the data that is explicitly collected — it extends to every data source the analytics model ingests, however indirectly.

The Puttaswamy judgment (2018) is even more direct. Justice Kaul’s concurring opinion described profiling — the use of personal data to evaluate a person’s performance, economic situation, health, personal preferences, reliability, behaviour, or movements — as a fundamental privacy concern, and warned that proprietary algorithms used to make consequential decisions about individuals can, if biased or unaccountable, produce discrimination based on religion, ethnicity, caste, or gender. This concern is not hypothetical in India’s digital lending sector. It is a live compliance and human rights risk.

The Compliance Fix

Digital lenders and any organisation using predictive analytics for individual decisions must:

→ Issue a Rule 3-compliant notice that itemises every data source used in the analytics model — including third-party data providers, their categories, and the specific purpose for which each is used.

→ Limit data ingestion to what is necessary for the stated lending purpose. Section 6(1) applies to every data point in the analytics pipeline, not just the primary application form.

→ Maintain a Data Processor register of every analytics partner or data provider, with contracts under Section 8(2) that specify permitted data uses.

→ Build a meaningful Section 11 response mechanism — one that can accurately answer a Data Principal’s question about what data sources contributed to a decision about her.

→ Where predictive analytics is used for credit, insurance, or employment decisions, conduct a DPIA under Section 10 to assess algorithmic risk to Data Principal rights before deployment.

Devika’s loan was rejected by a model she could not question, fed by data she did not know existed. Under the DPDP Act, that outcome has a name. It is a compliance failure.

Four things digital lenders using predictive analytics must do now:

→ Issue Rule 3-compliant notices that itemise every third-party data source feeding the lending model.

→ Build a meaningful Section 11 response capability — one that can accurately identify which data sources contributed to a specific decision.

→ Limit data ingestion to what is necessary for the stated lending purpose under Section 6(1).

→ Conduct a DPIA under Rule 13 of the DPDP Rules before deploying or updating predictive models used for credit decisions.

The DPDP Act does not permit algorithmic opacity when personal data drives consequential decisions.

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors: This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.5.1: DPDP and Data Analytics

DPDP 3.4.3: DPDP and Internet of Things (IoT) – Case Study 3 – Lakshmi’s Smart Home and the Data That Left the House

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The Scenario

Lakshmi is a homemaker in Bengaluru. Her family uses a suite of smart home devices — a connected doorbell with a camera, a voice-activated home assistant, smart lighting controlled by an app, and a connected refrigerator that tracks grocery inventory. All devices were purchased from different manufacturers, each with its own app and privacy policy.

Over time, Lakshmi realised that advertisements on her phone were displaying products she had discussed verbally at home and items she had added to her refrigerator’s shopping list. She had never consciously consented to any of this data leaving her home and being used for targeted advertising. She also had no single point of contact to understand what data was being collected, by whom, or how to stop it.

When she attempted to delete her data from one manufacturer’s platform, she received a response that her data had been shared with “technology partners” and could not be fully recalled.

The DPDP Act Position

Lakshmi’s situation illustrates the most complex IoT-DPDP compliance scenario: the multi-vendor smart home, where data collected by one device travels through multiple Data Fiduciaries and Data Processors, none of whom have a co-ordinated compliance architecture.

First — Consent fragmentation failure. Each device manufacturer that collects personal data within Lakshmi’s home is a Data Fiduciary. Each must independently satisfy Section 5 (notice) and Section 6(1) (specific, informed, free consent). A bundled privacy policy buried in a device setup wizard does not constitute the clear, plain-language, itemised consent notice required by Rule 3 of the DPDP Rules. Moreover, oral conversations captured by a voice assistant constitute personal data under Section 2(t) of the Act — any data about an individual who is identifiable by or in relation to such data. Capturing and processing these without specific, affirmative consent is unlawful.

Second — Data sharing with technology partners as undisclosed processing. When a Data Fiduciary shares personal data with a third-party technology partner for advertising targeting, that third party is either a Data Processor or a separate Data Fiduciary. Under Section 8(2), if it is a Data Processor, the original Data Fiduciary must ensure it processes data only for the specified purpose. If the specified purpose was “device functionality” and the actual use is “advertising targeting,” the sharing is unlawful — regardless of what the fine print says.

Third — Structural inability to respond to erasure requests. Section 12(3) of the DPDP Act requires the Data Fiduciary to erase personal data on request. When the Data Fiduciary has already shared data with technology partners — who are now using it independently — the original Data Fiduciary cannot honour this obligation without binding downstream agreements that require cascading deletion. The absence of such agreements is a compliance failure at the architecture stage, not a technical impossibility.

Fourth — Children’s data risk. If Lakshmi’s home includes minor children who interact with voice assistants or other smart devices, Section 9 of the DPDP Act applies. Processing personal data of a child requires verifiable parental consent. A voice assistant that captures a child’s voice — identifiable personal data — without verifiable parental consent is in direct violation. The penalty for breach of Section 9 obligations extends to ₹150 crore under the Schedule to the Act.

The IS Audit Perspective

IS Audit 3.0 by ICAI is explicit: IoT privacy risk arises because objects within the IoT collect and aggregate fragments of data that relate to their service, and that individually innocuous data points combine to reveal sensitive attributes. Lakshmi’s scenario is the textbook illustration of this risk at the consumer level.

The Puttaswamy judgment (2018) directly anticipated this concern, observing that IoT and data mining processes together can create new knowledge about individuals — something even the individuals themselves did not possess. The court noted that this is an age of “ubiquitous dataveillance,” where users of wearable devices and smart home technology generate vast data about lifestyles, choices, and preferences without conceiving of themselves as having volunteered that data. Indian law now governs that data.

The Compliance Fix

Smart home device manufacturers and platform operators selling to Indian consumers must:

→ Provide a separate, device-specific consent notice in clear and plain language — not a buried privacy policy — before any data collection begins. Rule 3 of the DPDP Rules applies to every device sold in India that collects personal data. → Identify and disclose every “technology partner” as a named Data Processor or co-Data Fiduciary in the consent notice, along with a specific description of what data they receive and for what purpose. → Build cascading deletion mechanisms so that an erasure request to the primary Data Fiduciary propagates to all downstream Data Processors. → Implement verifiable parental consent mechanisms under Section 9 before any smart home device capable of collecting children’s data is activated in a household. → Apply transport-level encryption across all device-to-cloud data flows — IS Audit 3.0 (Section 6.5.6) identifies the lack of transport encryption as a primary IoT risk.

Lakshmi’s home was smart. Its manufacturers were not compliant.

The Central Compliance Question for IoT Deployments

The IoT-DPDP compliance challenge is, at its core, a consent architecture problem. IoT devices are designed to collect continuously, silently, and at scale. The DPDP Act requires consent to be specific, informed, and granular. These two imperatives pull in opposite directions — and organisations that do not resolve this tension before deployment will face it as an enforcement problem after 13 May 2027.

There are five questions every organisation must answer before deploying IoT systems that collect personal data:

One. Have we issued a Rule 3-compliant notice — itemised, plain-language, and device-specific — for every category of personal data our IoT system collects?

Two. Is each downstream use of that data — analytics, scoring, advertising, pricing, performance management — identified as a specified purpose at the time of initial consent?

Three. Does every vendor, cloud platform, and analytics partner in our IoT data chain have a written Data Processor contract under Section 8(2) limiting them to the specified purpose?

Four. Have we built cascading deletion mechanisms so that a Section 12(3) erasure request can be honoured across the full data chain?

Five. If children may interact with our IoT devices, do we have a verifiable parental consent mechanism that meets the Section 9 standard?

IS Audit 3.0 by ICAI sets the governance standard clearly: IoT governance must address the lifecycle of IoT devices, the data managed by the IoT solution, and IoT applications in the organisation’s IT landscape. The DPDP Act makes this governance framework legally mandatory, not merely a best practice.

What Responsible IoT and DPDP Compliance Looks Like

The DPDP Act does not prohibit IoT. It requires that every IoT deployment be designed so that Data Principal rights — consent, purpose limitation, erasure, and correction — can be meaningfully exercised.

Devices will continue to collect data. Networks will continue to transmit it. Platforms will continue to analyse it. None of that is prohibited. However, every step in that chain — from sensor to server to insight — must be governed by a consent framework that the person at the centre of it all understood and agreed to, in plain language, before collection began.

The Puttaswamy judgment (2018) described this era as one of ubiquitous dataveillance. IoT is the infrastructure of that dataveillance. The DPDP Act is the law that governs it. Together, they mean one thing for Indian organisations: design your IoT systems for privacy first, not compliance as an afterthought.

The compliance deadline is 13 May 2027. Every IoT device collecting personal data in India today is already in scope.

Sources: IS Audit 3.0, ICAI | DPDP Act 2023| DPDP Rules 2025 | Puttaswamy v Union of India (2018)

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors: This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.4.3: DPDP and Internet of Things (IoT) – Case Study 3 – Lakshmi’s Smart Home and the Data That Left the House

DPDP 3.4.1: DPDP and Internet of Things (IoT) – Case Study 1 –  When Every Device Becomes a Witness

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INTRODUCTION

IoT and DPDP compliance represent one of the most underestimated challenges in India’s data protection landscape today.

Your fitness band knows your resting heart rate. Your smart meter knows when you wake up. Your office access badge knows exactly where you were at 9:14 am. Individually, each of these data points seems harmless. Together, however, they compose a surveillance portrait of extraordinary detail — one that its subject never agreed to provide and may not even know exists.

This is precisely the privacy risk that IS Audit 3.0 (ICAI Module 6, Section 6.5.6) identifies when it notes that IoT devices collect and aggregate fragments of data that, in combination, can reveal religion, health information, lifestyle choices, and other sensitive personal attributes — even when no single data point appears sensitive in isolation.

The Internet of Things is defined in IS Audit 3.0 (Module 6, Section 6.5.1) as a system of interrelated computing devices, mechanical and digital machines, objects, animals, or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. In simple terms, devices collect, send, and act on data — largely without human involvement at the point of collection.

That last phrase is the compliance problem. The DPDP Act, 2023 is built on the premise of informed, specific, and free consent. IoT is built on the premise of seamless, continuous, and frictionless data collection. These two architectures sit in fundamental tension — and that tension has a deadline: 13 May 2027.

Three Case Study bring this tension to life.

Case Study 1 — Rohini’s Wearable and the Insurance Company That Knew Too Much

The Scenario

Rohini is a 41-year-old schoolteacher in Pune. She purchased a health insurance policy from a mid-sized insurer. As part of a wellness incentive programme, the insurer offered her a discount in exchange for syncing her fitness wearable to their app. Rohini agreed, expecting only her step count to be shared.

Over eighteen months, the wearable transmitted her heart rate variability, sleep patterns, stress indicators, menstrual cycle data, and location history to the insurer’s cloud platform. The insurer’s algorithm then recalculated her risk profile — and at renewal, her premium increased substantially. No one told Rohini that these data points were being collected, how they were being used, or that they would influence her premium.

The DPDP Act Position

Rohini’s situation discloses at least three distinct violations.

First — Consent specificity failure under Section 6(1). The DPDP Act requires consent to be free, specific, informed, unconditional, and unambiguous. It must be limited to personal data that is necessary for the specified purpose. Rohini consented to sharing her step count for a wellness discount. She did not consent to the sharing of menstrual cycle data, stress indicators, or sleep patterns. Any processing beyond the specified purpose is unlawful.

Rule 3 of the DPDP Rules, 2025 reinforces this directly. A Data Fiduciary must give notice in clear and plain language, with an itemised description of personal data to be collected and a specific description of the purpose. A general reference to “health data” does not satisfy this standard. Each category of IoT data being collected must be identified separately in the consent notice.

Second — Purpose limitation failure under Section 8(1). The purpose for which Rohini’s data was processed — premium recalculation — was never disclosed as a specified purpose at the time of consent. Consequently, processing her data for that purpose is unlawful under the Act, regardless of what the policy fine print may say.

Third — Sensitive personal data processing without adequate basis. Menstrual cycle data and health metrics constitute personal health information. While the DPDP Act does not yet have a separate “sensitive personal data” category in the manner of some foreign frameworks, Section 8(5) requires the Data Fiduciary to implement reasonable security safeguards. Moreover, when data is used to make a decision that affects the Data Principal, Section 8(3) requires the Data Fiduciary to ensure completeness, accuracy, and consistency of that data. An algorithmic premium revision based on wearable data must meet this standard.

The IS Audit 3.0 Perspective

IS Audit 3.0 (Module 6, Section 6.5.6) specifically identifies privacy concerns in healthcare IoT as a primary risk, observing that devices in this domain collect at least one piece of personal information and that the aggregation of IoT data fragments can reveal health information that was never explicitly disclosed. This aggregation risk is precisely what happened to Rohini.

Furthermore, IS Audit 3.0 (Section 6.5.7) identifies governance of IoT data as requiring a clear reference architecture, defined governance processes, and lifecycle management of the data managed by the IoT solution — none of which the insurer had implemented.

The Compliance Fix

Insurers and any organisation using wearable or health IoT data must:

→ Issue a separate, itemised consent notice for each data category collected by the device — not a single bundled consent covering “health data.” → Clearly specify every downstream use — including risk assessment, pricing, and underwriting decisions — as a stated purpose at the time of initial consent. → Ensure that data collected via wearable integration is not used for purposes beyond what was disclosed, unless fresh consent is obtained for the new purpose. → Implement data minimisation controls at the IoT gateway level, so that only the specific data categories covered by consent are transmitted to the cloud platform.

Rohini’s insurer collected far more than it disclosed. The DPDP Act calls that a violation — not a feature.

Rohini’s insurer had a wellness programme. It did not have a consent programme. Under the DPDP Act, that distinction matters enormously. The compliance deadline is 13 May 2027.

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors:
This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.4.1: DPDP and Internet of Things (IoT) – Case Study 1 –  When Every Device Becomes a Witness

DPDP 3.3.3: DPDP and Blockchain – Case Study 3 – When the Ledger Never Forgets – Kaveri’s KYC That Lived Forever

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The Scenario

Kaveri runs a small trading firm. As part of onboarding a fintech lending platform, she completed a KYC process in 2022. The platform had built its KYC infrastructure on a consortium blockchain shared across six lenders.

In 2024, Kaveri closed her account and formally requested that her KYC data be removed. The fintech replied that the KYC records were permanent, shared across the consortium, and could not be altered because that would “break the chain’s integrity.”

Meanwhile, one lender in the consortium — located in a foreign jurisdiction — faced a regulatory investigation. Kaveri’s personal data was accessed by foreign law enforcement as part of that investigation, without her knowledge.

The Two DPDP Act Violations

Violation one: Section 12(3) — erasure right denied. The fintech’s architecture made it structurally impossible to honour a statutory right. That is a compliance failure attributable to design, not a force majeure.

Violation two: Rule 15 of the DPDP Rules, 2025 — foreign state access restrictions. Rule 15 prohibits a Data Fiduciary from enabling foreign governments or their instrumentalities to access personal data of Indian Data Principals except through legal channels prescribed by Indian law. Kaveri’s data was accessed by a foreign authority through a consortium node without any such channel being invoked.

This is precisely the data sovereignty concern that the Puttaswamy judgment (2018) framed when it recognised informational self-determination as a fundamental right. The Supreme Court observed that humans forget, but the internet does not. Blockchain amplifies this observation: not only does the internet not forget — the chain mathematically cannot.

The IS Audit  Perspective

IS Audit course material of ICAI notes that credential security on blockchain is only as strong as the access point, and that not all participants in a public or consortium chain can be assumed to have equivalent governance standards. Furthermore, Section on Governance and Controls requires that organisations assessing blockchain solutions audit cross-border data flows as a primary governance item.

The Compliance Architecture Fix

Consortium blockchain participants must establish a data governance charter before deployment, specifying: which personal data categories may be written on-chain; cross-border node participation restrictions consistent with Rule 15; erasure and key-revocation protocols binding on all nodes; and a Data Processor contract between the consortium and each member lender covering DPDP Act obligations.

Kaveri’s case is not hypothetical. Indian KYC infrastructure today sits at exactly this intersection.

The Central Compliance Question for Blockchain Deployments

The immutability of blockchain is not, by itself, illegal under the DPDP Act. However, placing personal data directly on an immutable ledger without designing for erasure is a compliance failure. The DPDP Act does not prohibit blockchain. It requires that every technology deployment — including blockchain — be architected so that Data Principal rights can be exercised.

There are four questions every organisation must answer before writing personal data to a blockchain:

One. Can we honour a Section 12(3) erasure request without breaking the chain? If not, the architecture must change before deployment.

Two. Does every node in our network have a written Data Processor agreement under Section 8(2)?

Three. Do any nodes sit in foreign jurisdictions? If so, does Rule 15 governance apply, and is it documented?

Four. Have we used off-chain storage with on-chain hashes, or encryption-at-rest with key deletion on erasure, to build in a compliance exit?

IS Audit study material by ICAI frames this governance requirement clearly: organisations implementing or assessing blockchain solutions must evaluate legal and compliance risk as a first-order item, not an afterthought. The DPDP Act makes this mandatory. The deadline is 13 May 2027.

What Responsible Blockchain and DPDP Compliance Looks Like

The DPDP Act does not ask organisations to abandon blockchain. It asks them to build it responsibly. Consequently, the organisations that will navigate this intersection well are those that:

Design data architecture around erasure rights from day one. Keep personal data off the immutable ledger wherever possible. Use encryption and key management as a proxy for deletion where off-chain storage is not feasible. Govern every node as a Data Processor. Apply Rule 15 restrictions to every cross-border node participant. And audit the entire architecture annually under IS Audit 3.0 standards.

Blockchain is a powerful technology. The DPDP Act is a serious law. Used together — with care — they are compatible. Used carelessly, they create the hardest compliance problems in India’s data protection landscape. Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors:
This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.3.3: DPDP and Blockchain – Case Study 3 – When the Ledger Never Forgets – Kaveri’s KYC That Lived Forever

DPDP 3.3.1 : DPDP and Blockchain – Case Study 1

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“The blockchain won’t let me delete your data.” That is not a legal answer under Indian law.

Arjun resigned from his employer and asked them to erase his personal data. His HR team apologised — the data was on their blockchain-based verification system and, they said, could not be deleted.

Under Section 12(3) of the DPDP Act, 2023, every Data Principal has a statutory right to erasure. The Data Fiduciary must comply — unless retention is necessary for a specified purpose or legal obligation. The DPDP Act creates no exception for immutable ledgers.

Furthermore, Section 8(1) places non-derogable liability on the Data Fiduciary. An organisation cannot transfer that liability to its technology architecture.

IS Audit 3.0 by ICAI identifies legal and compliance uncertainty as a primary blockchain risk. Before the DPDP Act, that uncertainty was structural. Today, the law is clear.

The compliance design choices are not complicated — but they must be made at the architecture stage, not after deployment:

→ Store personal data off-chain. Record only a hash on the ledger. Delete the off-chain data on erasure request.
→ Alternatively, encrypt personal data before writing to the chain. On erasure request, delete the encryption key. The block remains — but it is unreadable.
→ For permissioned chains, build node-level governance with erasure-triggering protocols.

Arjun’s employer had an immutable ledger. However, they did not have an erasure plan. Those are two different problems — and only one of them was a technical constraint.

The DPDP Act compliance deadline is 13 May 2027. Blockchain architectures processing personal data today need an erasure design now.

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors:
This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.3.1 : DPDP and Blockchain – Case Study 1

DPDP 3.2.1 : DPDP and Cloud – Case Study 1 – The Cloud Vendor’s Breach. The Founder’s Problem.

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Vikram is 34 years old. He built a thriving edtech startup from his apartment in Bengaluru. Fifty thousand registered users. A small but dedicated team. And all of it running on a global cloud platform.

When he signed up, he ticked the terms of service. The data processing agreement was long. He didn’t read it. He assumed the cloud provider would handle security — that’s what the cloud is for, right?

One morning, Vikram receives an automated alert. His cloud provider has detected unusual access to a storage bucket containing the personal data of his users — names, email addresses, learning history, and payment records. The provider’s security team is investigating.

Vikram logs a support ticket with the cloud vendor and waits for them to resolve it.

He does not know that the DPDP Act has already placed the obligation firmly on him — and the 72-hour clock started the moment he became aware.

The accountability that does not transfer

Section 8(1) of the DPDP Act, 2023 is direct: a Data Fiduciary shall, irrespective of any agreement to the contrary, be responsible for complying with the provisions of this Act in respect of any processing undertaken by it or on its behalf by a Data Processor.

Vikram’s cloud provider is a Data Processor. The personal data in that storage bucket belongs to Vikram’s users — users who gave their consent to Vikram’s platform, not to a global cloud company. The fact that the breach occurred at the cloud provider’s infrastructure does not move the compliance obligation. It remains with Vikram.

Section 8(2) requires that a Data Fiduciary may engage a Data Processor only under a valid contract. That contract must include — under Rule 6(1)(f) — appropriate provisions for taking reasonable security safeguards. If Vikram’s data processing agreement did not specify encryption of personal data at rest, access controls, log retention, and breach notification timelines aligned with DPDP requirements — the contract itself is a compliance gap.

The 72-hour window and what it demands from Vikram

Under Rule 7(2) of the DPDP Rules, 2025, a Data Fiduciary must intimate the Data Protection Board within 72 hours of becoming aware of a personal data breach — with the nature, extent, timing, location, likely impact, root cause, remedial measures, and a report on notifications sent to affected Data Principals.

Vikram became aware when he received the alert. The 72-hour clock started at that moment. It does not restart when the cloud vendor completes its investigation. It does not pause while the support ticket is open. It started when Vikram knew.

IS Audit Module 6 of the ICAI IS Audit 3.0 Course identifies a cloud-specific privacy risk directly relevant here: cloud computing involves a greater dependency on third parties and increased transborder flow of personally identifiable information, creating greater magnitude of privacy risks. The breach Vikram is experiencing is exactly that risk manifesting — and DPDP requires him to have anticipated it contractually, technically, and procedurally.

What Vikram should have built before going live on cloud

A DPDP-compliant cloud deployment for a Data Fiduciary requires four things that Vikram did not have.

A valid data processing agreement that explicitly requires the cloud provider to implement encryption, access controls, logs, and breach notification to the Data Fiduciary within a timeframe that allows the Data Fiduciary to meet its own 72-hour Board reporting obligation.

Encryption of personal data at rest in every cloud storage bucket and database — so that even if access is unauthorised, the data retrieved is unreadable without the decryption key (Rule 6(1)(a)).

Access controls and logs on every cloud resource holding personal data — so that Vikram can determine, at the time of the breach, exactly which data was accessed, when, and by whom (Rule 6(1)(b) and (c)).

A documented incident response workflow that — from the moment of a breach alert — initiates Board notification preparation, Data Principal communication, and forensic log preservation simultaneously, not sequentially.

The cloud is not a compliance shortcut. It is a processing architecture that transfers operational convenience to a third party while retaining legal accountability with the Data Fiduciary.

The question every founder must answer before their next cloud migration

Does your data processing agreement with your cloud provider obligate them — in writing — to implement the security safeguards that Rule 6 of the DPDP Rules requires? Does it require them to notify you of a breach within a timeframe that lets you meet your 72-hour obligation? Does it permit you to audit their compliance?

If the answer is “our standard plan terms cover it” — read the terms again. Vikram had the same assumption.

Sources: DPDP Act 2023 (Sections 8(1), 8(2)) | DPDP Rules 2025 (Rules 6(1)(a)(b)(c)(f), 7(2)) | ICAI IS Audit 3.0 Course — Module 6 (Section 6.3.5 — Cloud Privacy and Security Risks)

Disclaimer

The contents of this post are intended for general awareness and informational purposes only. They do not constitute legal opinion, professional advice, consultancy, statutory interpretation, or a recommendation to act in any particular manner.

The Digital Personal Data Protection Act, 2023, related rules, notifications, regulatory guidance and judicial interpretations may evolve from time to time. The applicability of the law may also vary depending on the facts, sector, nature of data processing, organisational role, contractual terms and compliance framework.

Readers should not rely solely on this post for making legal, business, HR, technology, data-processing or compliance decisions. Specific advice from a qualified legal, privacy, cybersecurity, governance or compliance professional should be obtained before acting on any matter discussed.

The author / publisher shall not be responsible for any loss, liability, claim, penalty or consequence arising from reliance on the contents of this post without independent professional advice.

Authors:
This article has been co-authored by CA. Sunil Elayadath and CA. Karthik Narayanan S, Partners of Karthik & Sunil, together with Mr. Dhanesh P. K., Designated Partner, DSK Sustainability Tech.

DPDP 3.2.1 : DPDP and Cloud – Case Study 1 – The Cloud Vendor’s Breach. The Founder’s Problem.