Nikhil Jhanji, Principal Product Manager, Privy by IDfy The DPDP rules have finally given enterprises a clear structure for how personal data enters and moves through their systems. What has not been discussed enough is that this same structure also reduces the space in which deepfakes and synthetic identities can slip through. For months the Act lived in broad conversation without detail. Now enterprises have to translate the rules into real action. As they do that work, a practical advantage becomes visible. The discipline required around consent, accuracy, and provenance creates an environment where false personas cannot blend in as easily. This was not the intention of the framework, but it is an important consequence.

DPDP Rules Bring Structure to Enterprise Data Intake

The first shift happens at data entry. The rules require clear consent, proof of lawful purpose, and timely correction of errors. This forces organisations to examine the origin of the data they collect and to maintain records that confirm why the data exists. Better visibility into the source and purpose of data makes it harder for synthetic identities to enter the system through weak or careless intake flows. This matters because the word synthetic now carries two very different meanings. One meaning refers to responsible synthetic data used in privacy enhancing technologies. This type is created intentionally, documented carefully, and used to train models or test systems without revealing personal information. It supports the goals of privacy regulation and does not imitate real individuals.

Synthetic Data vs Synthetic Identity: A Critical Difference

The other meaning refers to deceptive synthetic identities, false personas deliberately created to exploit weak verification processes. These may include deepfake facial images, manipulated voice samples, and fabricated documents or profiles that appear legitimate enough to pass routine checks.

This form of synthetic identity thrives in environments with poor data discipline and is designed specifically to mislead systems and people.

The DPDP rules help enterprises tell the difference with more clarity. Responsible synthetic data has provenance and purposeful creation. Deceptive synthetic identity has neither. Once intake and governance become more structured, the distinction becomes easier to detect through both human review and automated systems.

Cleaner Data Improves Fraud and Risk Detection

As organisations rewrite consent journeys and strengthen provenance under the DPDP rules, the second advantage becomes clear. Cleaner input improves downstream behaviour. Fraud engines perform better with consistent signals. Risk decisions become clearer. Customer support teams gain more dependable records. When data is scattered and unchecked, synthetic personas move more freely. When data is organised and verified, they become more visible. This is where the influence of DPDP rules becomes subtle. Deepfake content succeeds by matching familiar patterns. It blends into weak systems that cannot challenge continuity. Structured data environments limit these opportunities. They reduce ambiguity and shrink the number of places where a false identity can hide. This gives enterprises a stronger base for every detection capability they depend on. There is also a behavioural shift introduced by the DPDP rules. Once teams begin managing data with more discipline, their instinct around authenticity improves. Consent is checked properly. Accuracy is taken seriously. Records are maintained rather than ignored. This change in everyday behaviour strengthens identity awareness across the organisation. Deepfake risk is not only technical. It is also operational, and disciplined teams recognise anomalies faster.

DPDP Rules Do Not Stop Deepfakes—but They Shrink the Attack Surface

None of this means that DPDP rules stop deepfakes. They do not. Deepfake quality is rising and will continue to challenge even mature systems. What the rules offer is a necessary foundation. They push organisations to adopt habits of verification, documentation, and controlled intake. Those habits shrink the attack surface for synthetic identities and improve the effectiveness of whatever detection tools a company chooses to use. As enterprises interpret the rules, many will see the work as procedural. New notices. Updated consent. Retention plans. But the real strength will emerge in the functions that depend on reliable identity and reliable records. Credit decisions. Access management. Customer onboarding. Dispute resolution. Identity verification. These areas become more stable when the data that supports them is consistent and traceable. The rise of deepfakes makes this stability essential. False personas are cheap to create and increasingly convincing. They will exploit gaps wherever they exist. Strong tools matter, but so does the quality of the data that flows into those tools. Without clean and verified data, even advanced detection systems struggle. The DPDP rules arrive at a moment when enterprises need stronger foundations. By demanding better intake discipline and clearer data pathways, they reduce the natural openings that deceptive synthetic content relies on. In a world where authentic and synthetic individuals now compete for space inside enterprise systems, this shift may become one of the most practical outcomes of the entire compliance effort. (This article reflects the author’s analysis and personal viewpoints and is intended for informational purposes only. It should not be construed as legal or regulatory advice.)

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