DPDP Act26 May 2026 7 min read

DPDP Act Data Accuracy: Keeping Personal Data Up to Date

Understanding the DPDP Act data accuracy obligation under Section 8 — how to keep personal data correct, handle correction requests, and build accuracy controls.

Key Takeaways

Section 8(3) requires Data Fiduciaries to ensure personal data is accurate and up to date where it is used to make decisions that affect the Data Principal.
The right to correction under Section 11 allows Data Principals to request correction of inaccurate or incomplete data.
Accuracy is a higher obligation for decision-making contexts — credit scoring, employment decisions, and healthcare all carry heightened accuracy risk.
Processes for handling correction requests should respond within the timeframe the DPDP Rules specify.
Periodic data quality checks reduce the risk of enforcement action and improve the quality of business decisions made using personal data.

In this guide

The Data Accuracy Obligation Under the DPDP Act
Section 8(3): What the Law Requires
Accuracy in Decision-Making Contexts
Handling Data Correction Requests
Building Data Quality Controls
Accuracy Obligations for Third-Party Data
Enforcement Risk and Accuracy Failures

The Data Accuracy Obligation Under the DPDP Act

Data accuracy is the obligation to ensure that personal data held by a Data Fiduciary is correct, complete, and kept up to date — particularly when it is used as the basis for decisions that affect the individual to whom it relates. Under the DPDP Act 2023, accuracy is not just a good data governance practice; it is a statutory requirement that can give rise to Data Principal rights and regulatory enforcement.

The accuracy obligation recognises a core privacy harm: inaccurate personal data used in decisions can cause real damage to individuals. An incorrect credit score can deny a loan. An outdated employment record can cost someone a job offer. A wrong medical record can lead to inappropriate treatment. The DPDP Act seeks to address these harms by requiring Data Fiduciaries to maintain data quality and to respond to correction requests.

For most Indian SaaS companies, the accuracy obligation applies most prominently in HR software, fintech applications, healthcare platforms, and any product that uses personal data to make automated decisions. But the obligation is universal — every Data Fiduciary must have a process for receiving and acting on correction requests, regardless of the sensitivity of the data involved.

Section 8(3): What the Law Requires

Section 8(3) of the DPDP Act states that a Data Fiduciary shall ensure that the personal data is complete, accurate, and consistent, to the extent that the fiduciary is able to do so, and shall update the personal data where necessary. The qualifier "where necessary" is important — accuracy is a higher obligation for data used in decisions that affect the Data Principal than for data used purely for statistical purposes where the impact on any individual is minimal.

Section 11(b) gives Data Principals the right to correction, completion, updating, and erasure of personal data as may be specified. The right to correction is actionable — a Data Principal can file a complaint with the Data Protection Board if their correction request is not processed appropriately, and non-compliance can attract penalties under Schedule 1.

The Act does not specify a response timeframe for correction requests in the statute itself — this will be addressed in the DPDP Rules. Based on comparable legislation and regulatory expectations, companies should aim to process correction requests within 30 days and to acknowledge receipt within 5 working days. Build these response SLAs into your internal processes before the Rules are finalised.

Accuracy in Decision-Making Contexts

The accuracy obligation is most critical in contexts where personal data drives consequential decisions. In fintech, credit decisions made on the basis of personal financial data (income, transaction history, repayment record) must be based on accurate data — an error in a borrower's record that leads to a wrongful rejection or a higher interest rate is both a business liability and a DPDP compliance failure.

In HR software (which is a large segment of Indian B2B SaaS), employment records, performance ratings, attendance data, and payroll information are all personal data subject to the accuracy obligation. An incorrect attendance record that triggers a salary deduction, or an erroneous performance rating that affects a promotion decision, are accuracy failures with tangible harm to the employee.

Healthcare and wellness platforms face the highest accuracy risk. An incorrect allergy record or a misattributed lab result can cause direct physical harm. For these platforms, accuracy controls must be more rigorous — including clinical data validation, version history, and mandatory review workflows for any changes to safety-critical records.

Handling Data Correction Requests

Establish a clear, accessible process for Data Principals to submit correction requests. The DPDP Act and Rules will require a grievance mechanism (Section 12), and correction requests will typically be channelled through this mechanism. At a minimum, you need: a designated email address or in-product form for data requests, a defined internal workflow for triaging and processing requests, and a way to notify the Data Principal of the outcome.

When you receive a correction request, verify the requester's identity before making changes — you should not alter someone's data based on an unverified request, as this itself could create inaccuracies. Once identity is confirmed, assess whether the correction is valid: is the current data actually inaccurate? Is the requested correction supported by appropriate evidence (e.g., a government-issued document for a name change)? Document your decision-making.

After making a correction, cascade it to all systems where the inaccurate data was held. In microservices architectures where personal data is replicated across multiple services, a correction in the primary data store must propagate to derived stores, caches, analytics systems, and any exports sent to third parties. Incomplete cascading means the inaccurate data continues to exist in secondary systems.

Building Data Quality Controls

Proactive data quality controls prevent accuracy issues from accumulating. Implement validation at the point of data entry: enforce format validation for fields like phone numbers, PAN, Aadhaar (where lawfully collected), email addresses, and postcodes. Reject obviously invalid entries at the form level rather than storing garbage data that must later be corrected.

Implement periodic data freshness reviews. For data categories that are likely to change over time (address, phone number, employment status), send users prompts to confirm or update their information at regular intervals — annually for most consumer applications, more frequently for high-stakes contexts like financial services. This can be incorporated into the normal product experience (e.g., a profile completeness prompt or an annual account review email).

Maintain change history for critical personal data fields. For any field whose historical value matters for compliance or for resolving disputes — name changes, address history, consent records — store a timestamped history of changes rather than overwriting the previous value. This supports accuracy investigations and provides an evidence trail.

Accuracy Obligations for Third-Party Data

Many Indian companies enrich their CRM data using third-party data providers — sourcing contact details, company information, or financial data from aggregators. This third-party data is still personal data under the DPDP Act, and the accuracy obligation applies to it in your systems regardless of where it originated.

The challenge is that you cannot verify the accuracy of third-party data at the point of ingestion. Disclose in your privacy notice that you may source data from third parties and give Data Principals the ability to correct inaccuracies. When you receive a correction request for data that originated from a third-party source, correct it in your own systems and, where possible, notify the originating source of the inaccuracy.

Review your contracts with data enrichment providers. Reputable providers will have data quality SLAs and mechanisms to receive accuracy notifications. Some providers refresh their data frequently and can be a useful source of data freshness. However, the ultimate accuracy obligation under the DPDP Act sits with you as the Data Fiduciary, not with the data provider.

Enforcement Risk and Accuracy Failures

Accuracy failures become enforcement risks when they combine with consequential decision-making and a failure to respond to correction requests. A Data Principal who is denied a service because of inaccurate data in your systems, submits a correction request that is ignored, and then files a complaint with the Data Protection Board is presenting a strong case for penalty imposition.

The penalty for non-fulfilment of Data Principal rights — including the right to correction — is up to ₹10,000 per instance under Schedule 1. While this seems modest, systemic failures affecting thousands of Data Principals (for example, a bug that corrupts a field across your user base) could result in penalties of significant aggregate scale. More importantly, a Board finding of systematic accuracy failures could trigger deeper scrutiny of your overall compliance programme.

Treat accuracy complaints as valuable compliance signals. When a Data Principal reports inaccurate data, investigate not just their individual case but whether the inaccuracy is systemic — caused by a data ingestion error, a migration bug, or a flawed validation rule that may have affected other records. Resolving the root cause prevents future enforcement exposure.

Frequently Asked Questions

Are we required to proactively verify data accuracy, or only respond to correction requests?

Section 8(3) requires proactive accuracy maintenance where data is used for consequential decisions. This means you should implement controls to keep data fresh and accurate, not just respond reactively to correction requests. For lower-stakes data with minimal decision-making impact, the obligation is lighter — you must still have a correction mechanism but are not required to conduct unsolicited data quality sweeps.

What if a Data Principal requests a correction we believe is incorrect?

You are not required to make a correction you believe to be inaccurate. You should investigate the request, document your reasoning, and inform the Data Principal of your decision. If the Principal disagrees, they may escalate to the Data Protection Board. Maintain records of your assessment in case the matter is escalated.

How does the accuracy obligation interact with our contract with enterprise clients whose end-users are the Data Principals?

Your enterprise client is the Data Fiduciary; you are the Data Processor. The accuracy obligation sits primarily with the Data Fiduciary (your client). However, your contract should include provisions requiring your client to pass correction requests to you and requiring you to implement corrections in your systems within an agreed timeframe. Build correction request routing into your product.

What if correcting a record would conflict with our audit log integrity?

Audit logs should record that a correction was made, not delete or alter the original entry. Maintain an immutable audit trail that shows the original value, the correction date, and the new value. This preserves audit integrity while fulfilling the correction obligation. For legal and compliance records that must not be altered, document the correction separately and annotate the original record.

Does the accuracy obligation apply to employee data held by HR SaaS platforms?

Yes. Employee personal data is subject to the DPDP Act, and accuracy is a key concern in HR contexts given that employment decisions are based on this data. HR SaaS platforms should provide self-service profile update mechanisms for employees, manager correction workflows, and HR administrator override capabilities, all with appropriate logging.