Algorithms and statistical models are no longer just technical tools, they are decision-making engines. From personalizing offers to predicting credit risk or churn, businesses increasingly rely on automated systems that process personal data at scale.
But with this power comes regulatory responsibility, especially under India’s Digital Personal Data Protection (DPDP) Act, 2023, and the DPDP Rules, 2025.
Even though DPDP doesn’t explicitly define AI or statistical models, it directly holds organizations accountable for how these systems handle personal data.
The Key Principle: Accountability Over Technology
The DPDP Rules introduce obligations for Significant Data Fiduciaries (SDFs) to ensure that any algorithmic software processing personal data does not jeopardize the rights of data principals.
What this means in practice:
- Your recommendation engine, risk scoring model, or predictive churn algorithm falls under compliance obligations if it uses personal data.
- The law does not regulate the algorithm itself, but it regulates your use of it.
In short: the organization, not the algorithm, is accountable.
Real-World Implications
Consider a few examples:
| Sector | Use Case | Data Volume | DPDP Compliance Action |
| BFSI | Credit risk scoring | 500,000 applications/month | Include model in DPIA; verify data inputs; check for misclassification risks |
| E-commerce | Personalized offers | 10 million users | Map data flow; confirm consent; maintain documentation |
| Healthcare | Predictive risk scoring | 2 million patient records | Ensure purpose limitation; integrate model in audits; validate outcomes |
Even if the algorithms are outsourced or purchased from vendors, the fiduciary is responsible for due diligence and regulatory compliance.
Practical Steps for Data Fiduciaries
- Map Data Flows: Understand which models use personal data, where it comes from, and how it is processed.
- Integrate Models in DPIAs & Audits: Assess risks, including bias, misclassification, or privacy violations.
- Document Rigorously: Maintain records of model purpose, inputs, outputs, validation, and risk mitigation.
- Vendor Oversight: Ensure third-party models comply with DPDP; maintain contractual obligations and evidence.
- Monitor & Review: Establish periodic reviews of model performance, risks, and compliance alignment.
What DPDPA Doesn’t Require
- Explainable AI for every model
- Public disclosure of model logic
- Prescribed bias thresholds
Yet, fiduciaries are accountable for model outcomes, making governance and documentation non-negotiable.
Key Takeaways
- DPDP compliance is fiduciary responsibility, not algorithm policing.
- Models that process personal data must be included in DPIAs, audits, and risk assessments.
- Vendor and third-party models are also within your compliance scope.
- Proactive governance mitigates regulatory and operational risks while demonstrating responsible data practices.
Conclusion
The DPDP Act has shifted data governance from a technical concern to a strategic accountability function. Data Fiduciaries must ensure that every algorithmic or statistical model processing personal data is transparent, auditable, and aligned with fiduciary obligations.
In DPDP’s framework, algorithmic opacity equals fiduciary risk — and preparedness today prevents compliance challenges tomorrow.
How Seqrite can Help
As India’s data protection landscape evolves, ensuring transparency, accountability, and security across algorithmic models is no longer optional. Seqrite’s Data Privacy and Security solutions empower organizations to identify, monitor, and protect personal data across systems, support DPIA readiness, and enforce strong governance controls aligned with DPDPA requirements.
With deep visibility into data flows and robust risk management capabilities, Seqrite helps data fiduciaries stay compliant, reduce regulatory exposure, and build digital trust. Discover how Seqrite can help you operationalize DPDPA compliance with confidence.
