Operationalising the DPDP Act and Rules 2025: Engineering Verifiable Consent and Erasure

Paper at a Glance

The Digital Personal Data Protection Act, 2023, established under Section 1, and the anticipated DPDP Rules, 2025, force Indian enterprises to transform privacy from a passive legal policy into an active engineering requirement. Recent academic and policy research, including 'Balancing Innovation and Privacy (2026)' and 'Machine Unlearning in Collaborative Filtering (2026),' explores this exact operational shift. The core thesis across the literature is that manual compliance frameworks are no longer sufficient to meet statutory obligations. Instead, organisations must adopt advanced privacy-enhancing technologies (PETs), algorithmic unlearning for verifiable deletion, and automated incident response architectures. By leveraging Consent Managers under the Data Empowerment and Protection Architecture (DEPA) and embedding Privacy by Design (PbD) directly into product lifecycles, enterprises can enforce data minimization and purpose limitation.

Methodology and Limits

The synthesised studies rely on a rigorous mix of empirical surveys, comparative legal analysis, and simulated technological prototypes. For instance, researchers surveyed over 380 corporate stakeholders across the legal, banking, and corporate sectors to assess compliance preparedness, revealing significant sectoral variations. Furthermore, engineers prototyped architectures like the DPDPA-Cloud Security Integration Model (DCSIM) - aligned with ISO/IEC 27017 and 27701 - on major cloud providers, alongside Federated and Privacy-Preserving AI (FPPAI) models simulated on AWS, Azure, and GCP. Tests of Right to Erasure implementations utilised datasets like MovieLens-1M and Amazon-Book. However, these papers present notable limitations for immediate enterprise application. The effectiveness of technical solutions like Shard-Cascade Unlearning, decentralized Electronic Health Records using Ethereum smart contracts, and agentic software frameworks is largely based on simulated environments or specific datasets rather than live, high-volume transactional systems. Additionally, predictions regarding exact Data Protection Board of India (DPBI) enforcement penalties remain speculative until substantial case law develops.

Findings Relevant to India

The DPDP Act covers digital personal data processed within India and data processed outside India if connected to offering goods or services to Data Principals in India, as defined in Section 3. To process this data, Section 4 establishes that consent is the primary basis for processing, except where Section 7 legitimate uses apply. Research on the DEPA framework highlights that consent managers will require enterprises to deploy standardised consent tokens and APIs. This ensures Data Principals can revoke permission seamlessly, moving away from bundled, click-based notices that fail regulatory scrutiny. Current e-commerce interfaces often rely on dark patterns, which undermine the Act's requirement for unambiguous agreement.

Fulfilling the Right to Erasure under Section 12 presents a distinct engineering hurdle, particularly for AI-driven platforms. The literature argues that traditional database deletion is insufficient when user data is encoded into machine learning algorithms. Solutions like Shard-Cascade Unlearning are proposed to provide verifiable, cryptographic proof of deletion across interconnected enterprise systems by removing user preferences encoded in collaborative-filtering models. Additionally, privacy engineering frameworks like FPPAI have demonstrated the ability to minimize data movement by 94.3% while maintaining model accuracy. Decentralized health record models have also successfully achieved compliance using AES-256 encryption and Shamir’s Secret Sharing to ensure secure, verifiable deletion.

The literature also notes an intersection between the DPDP Act and artificial intelligence, revealing complexities around algorithmic accountability. Hybrid systems like RegAI, which combine natural language processing and knowledge graphs, are actively being developed to dynamically map evolving legal clauses to technical controls. Furthermore, the Rules mandate strict incident response protocols, requiring enterprises to implement automated forensics to avoid severe multi-crore financial penalties upon intervention by the DPBI.

Implications for Compliance Teams

For compliance and legal heads, the transition to the 2025 Rules means cross-team accountability is now mandatory. Relying on static spreadsheets for the Record of Processing Activities will not withstand a DPBI audit or support the rapid turnaround required for breach reporting. Evaluating a privacy solution now requires assessing its ability to handle automated evidence trails, verifiable consent records, and API-driven vendor oversight. Tooling must integrate directly with product development lifecycles to enforce privacy by design from the outset.

Research indicates a sharp disparity in compliance readiness between large corporations with sophisticated technology systems and small-to-medium enterprises (SMEs). SMEs face disproportionate financial and operational burdens, making the adoption of automated compliance checkers - which studies show can achieve up to 86% accuracy in auditing adherence - essential for proactive vulnerability identification. Agentic software frameworks utilizing Know-Your-User (KYU) and Compliance agents can further provide scalable governance through masking and pseudonymization.

Cross-border data transfers demand careful technical tracking under this new regime. Under the Act, transfers are generally permitted unless the Central Government restricts transfer to notified countries or territories. This negative list approach requires compliance systems to dynamically map data flows and restrict routing to prohibited jurisdictions without relying on manual intervention. While tools can automate API integrations and anomaly detection, assigning control owners and defining risk thresholds will remain a critical manual governance task for the enterprise.

Questions to Ask Your Own Team

1. If a Data Principal requests erasure under Section 12 today, can our current data architecture cryptographically prove that their information is removed from our machine learning models? Mere deletion of a database row will not satisfy compliance audits if the user data remains encoded in active algorithms.

2. Are our customer-facing portals integrated with DEPA-compliant consent managers using standardised APIs? Relying on legacy, bundled consent checkboxes creates an immediate vulnerability under the new notice mechanisms.

3. Do our incident response workflows automatically generate a DPBI-ready breach report to meet statutory timelines? We must also ensure this system simultaneously triggers intimations to affected Data Principals without delay.

4. Have we assessed the disparity in compliance readiness across our supply chain, particularly regarding the SMEs processing data on our behalf?

Gaps and Open Questions

While the research provides strong architectural blueprints, it leaves several operational questions unanswered for Indian fiduciaries. The literature lacks quantified financial models detailing the exact budget allocations required to overhaul legacy data lakes for algorithmic unlearning. There is also a gap regarding the exact technical specifications and API standards the government will mandate for registering and integrating with Consent Managers. Finally, empirical data on early DPBI enforcement actions and penalty structures remains unavailable.

Closing these operational gaps requires continuous assessment of your internal workflows. To evaluate how your organisation's current audit trails and consent mechanisms measure against the DPDP Act and Rules 2025, try our evaluation tool at freescan.complydp.com.