Nikhil Jhanji, Principal Product Manager, Privy by IDfy
The recently implemented Data Protection and Digital Privacy (DPDP) rules provide organizations with a well-defined framework for handling personal data. An often-overlooked benefit of these regulations is their potential to curtail the proliferation of deepfakes and synthetic identities, which can infiltrate systems under the radar.
DPDP Rules: Transforming Data Management
The initial change we see with the DPDP regulations occurs during data entry. These rules enforce precise consent, lawful purpose of data collection, and timely error correction. This means businesses must scrutinize where their data comes from, bolstering the accountability of their data management practices. Improved transparency regarding data sources can significantly hinder the entry of synthetic identities, which often exploit lax data intake procedures.
The term “synthetic” can now refer to two contrasting concepts. On one hand, there is responsible synthetic data—developed intentionally and documented meticulously for purposes like testing systems or training models while safeguarding personal identities. This type of synthetic data aligns with privacy regulations and does not mimic real individuals.
Understanding the Divide: Synthetic Data vs. Synthetic Identity
Conversely, deceptive synthetic identities represent a serious threat. These are fake personas created to deceive systems, typically employing deepfake imaging, altered audio samples, and forged documents that appear genuine enough to pass standard verification procedures.
This dangerous form of synthetic identity flourishes in environments lacking robust data governance, specifically designed to mislead both people and technology. The good news is that the DPDP rules offer businesses the means to discern these two definitions more clearly. Responsible synthetic data is characterized by its documented creation process and clear purpose; deceptive synthetic identities lack such provenance, making it easier to identify them when data governance practices are structured.
Enhanced Data Quality for Improved Fraud and Risk Management
As organizations begin to revise their consent processes and strengthen data provenance in line with DPDP, a second benefit becomes apparent: enhanced data quality leads to better performance in risk and fraud detection systems. Cleaner, more consistent data is easier for fraud detection engines to interpret, resulting in clearer risk assessments.
When data is disorganized and unchecked, synthetic personas can slip through unnoticed. In contrast, when data is well-managed and verified, these deceptive identities become more conspicuous. The influence of DPDP regulations, therefore, allows organizations to exclude ambiguity, reducing the chances that false identities can bypass their defenses. This foundational change bolsters the effectiveness of any detection technology in use.
Moreover, implementing DPDP principles encourages a cultural shift. As teams adopt more disciplined data management habits, they become increasingly attuned to issues of authenticity. Proper consent checks and earnest attention to accuracy become the norm, ultimately fostering a stronger awareness surrounding identity verification within the organization. Deepfake risks are not solely technical; they require operational vigilance, which well-trained teams can provide.
Not a Cure-All—But a Step in the Right Direction
It’s crucial to note that while DPDP regulations do not completely eliminate the risk posed by deepfakes, they do significantly reduce the opportunities for these threats to manifest. The quality of deepfake technology continues to advance, presenting ongoing challenges. However, the rules lay a critical groundwork for organizations to build habits that prioritize verification, documentation, and controlled data intake, which collectively lessen the susceptibility to synthetic identities.
For many enterprises, the implementation of DPDP regulations might be perceived as merely procedural—updating consent forms and retention policies, for instance. Nevertheless, the real benefits will become clear in areas that demand reliable identities and accurate records: credit approvals, access management, customer onboarding, dispute resolution, and identity validation. Stability in these functions emerges when supported by well-organized and traceable data.
The increasing threat from deepfakes underscores the need for this stability. Creating false personas is not only simple but also convincing enough to exploit any weaknesses in data systems. While advanced tools for detection are vital, the quality of incoming data is equally important. Without clean and reliable data, even the most cutting-edge detection systems will struggle.
The DPDP rules arrive at a critical juncture for enterprises, urging them to enhance their data intake practices and clarify data flow paths. By doing so, they decrease the vulnerabilities that misleading synthetic content thrives on. In a landscape where authentic and artificial identities vie for relevance within enterprise systems, designing stronger data governance practices may represent one of the most practical outcomes of this compliance effort.
(This article reflects the author’s analysis and is intended solely for informational purposes. It should not be considered as legal or regulatory advice.)
