Architecting a solution for achieving DPDPA Compliance
The Digital Personal Data Protection Act (DPDPA), shifts focus from a just having policies (intent) to how it is implemented. It moves from an ad-hoc spreadsheet and email-based implementation to how it can be systematically implanted. That presents a system design problem. Policies alone are not useful – they just “demonstrate intent”; privacy must now be hardwired into your applications, data flows and operational processes. It must be architected into the very code, data streams, and processes that drive the business and you should have the ability to “demonstrate execution”
Companies now need to build compliance directly into how their systems work, rather than just asking users to sign forms after the fact. Do it right the first time.
When we start looking at DPDPA as not a policy problem but system design problem, we must start with answering few basic questions.
For every piece of personal data that we collect we need to look at:
Purpose limitation – Why are we collecting it? The personal data should be collected and used only for a clear, specific, and lawful purpose that we have told the individual about, and nothing beyond that.
Data mapping and access control – Where is the data stored and who all have access to it. We need to identify and document what personal data are we collecting, where it comes from, why it is used, where it is stored, how it flows across systems, and who has access to it.
Retention – How long do we keep it? We should keep the data only for as long as it is necessary to fulfil the stated purpose for which it was collected or to meet legal obligations. Once the purpose is achieved and no lawful reason to retain the data exists, the data must be deleted.
Data principal rights – How do we maintain / delete / correct the personal data? The individuals should have control over their personal data, including the right to access information about how their data is processed, request correction or erasure, withdraw consent, and seek grievance redressal.
If the solution that we are building does not answer these questions, then we can safely assume that we are Not DPDPA Compliant, even if policies exist.
Having understood this part, lets look at the components/functionalities that you should put in place to build a DPDPA Compliant Solution
Data Discovery & Classification
This inventory and classification layer comprises of Data Catalogues or data stores. These data stores could be logical entities and not necessarily a specific table in a database or a tool or a report.
Metadata tags are to be built on these data stores to easily identify their characteristics and categorize them. Make sure the tags propagate downstream (DB, cache, logs, backups).
Classification of these data sets as per DPDPA needs to be done. I have talked about this in detail in my earlier blog. If data is not classified, it should not be stored.
A provision to define clear ownership for each dataset should be in place to ensure accountability under DPDPA. Every dataset containing personal data must have a designated owner responsible for its accuracy, lawful use, access control, retention, and response to data principal requests or incidents. Clear ownership helps prevent gaps in compliance, enables faster decision-making, and ensures effective governance across the data lifecycle.
Purpose Limitation by Design
DPDPA requires that the data is used only for the purpose consented to. A Single data set (or user master) used by marketing, ops, analytics, ML or Shared without purpose boundaries is not acceptable as per DPDPA
So, when designing the systems, you need to ensure you are not using the data anything other than what it was asked for. This needs to be enforced at code level and the APIs that you build, Service level authorizations that you put in place need to follow this.
Separate models are to be built around the same data set for operations, analytics and reporting, which ensures that the scope of access is not the same for all.
Net-net, you need to ensure the purpose is enforced by code, not policy PDFs.
Consent Management as a System Component
The consent management as per DPDPA goes way beyond just a checkbox. It should be seen a service. A service that is managed centrally across different parts/modules of the solution and should be auditable.
The Consent should be Explicit and Purpose-specific. You should build versioning capabilities and put an audit trail in place using which you should be able to establish the version of consent, a particular user has signed on.
The consent should be revocable and automatically be applicable to all the places for which the consent was asked for.
To achieve this, you need to provide for a Consent UI, a data store for Consent and more importantly – to access any data across system, an access gateway should be in place. This gateway should check if the consent is present or not, is the consent valid (not revoked) and only then the access to the data should be given.
Any of the services that does not go through this gateway will be a candidate for a potential non-compliance.
Data Principal Rights (Access, Correction, Deletion)
You need to ensure that individuals have control over their personal data. Which mean provisions should be in place for the Data principals to access information about their personal data – where is it being processed, request correction of inaccurate or outdated data, and seek deletion of data once the stated purpose is fulfilled or consent is withdrawn.
Audit Logs are to be maintained for each request; action taken and needs to be acknowledged.
If requested data updates or deletion requires manual DB queries, then it is safe to say you are not compliant, and this is where most systems fail.
Retention & Automated Deletion
DPDPA requires keeping the personal data only as long as necessary. All the retentions rules should be configurable as per data classification and the consent sought.
You need to implement time-based background jobs to delete the data when the specified period is over. Provision for event based (e.g., deleting account) deletions should be built.
The defined backup lifecycle policies are to be implement and audited from time to time.
Access Control & Zero Trust
The architecture should ensure that access to personal data is reasonably safeguarded by following Zero Trust principles, where no user or system is trusted by default.
Access to personal data must be enforced through role-based or attribute-based controls, ensuring users can access only what is necessary for their defined purpose.
Personal data should be encrypted both at rest and in transit.
Credentials must be unique and not shared, and all access to personal data must be fully logged and auditable. Audit logs should clearly record who accessed what data, when it was accessed, and for what purpose, enabling accountability and compliance verification.
Breach Readiness (Beyond Prevention)
DPDPA requires breach readiness and not just prevention.
To start with, the breach must be notified to all the concerned parties. The solution should cater to central logging of the breach, anomaly detection and access pattern alerts.
Incident Response Playbook for every Security Event and Impact Assessment methodology should be present, and a notification workflow should be put in place.
Vendor & Processor Architecture
Even when personal data is processed or stored by third parties such as cloud providers, SaaS tools, or analytics platforms, your organization remains fully responsible under DPDPA.
Appropriate safeguards must therefore be built into the architecture, including sharing only the minimum data required, enforcing purpose-bound contractual obligations, ensuring region-aware data storage where applicable, and applying techniques such as tokenization or masking before data is transferred.
These measures help maintain control, reduce risk, and ensure accountability across the vendor ecosystem.
To Summarise if the answer to following questions is Yes, then you are architecturally DPDPA-ready.
- Can we delete one user completely in <24 hours?
- Can we prove why we collected each field?
- Can we show consent history?
- Can we show who accessed personal data?
- Can we isolate data by purpose?
Organizations must embed privacy into application design by enforcing purpose limitation, data mapping, retention, consent, access control, and data principal rights directly in code and workflows.
Ultimately, compliance is proven not by policies, but by the ability to demonstrate control, accountability, and traceability across the entire data lifecycle.
