DPDP IMPLEMENTATION FRAMEWORK FOR RBI-REGULATED BANKS (Part 4)
- VENUGOPAL PARAMESWARA
- 1 day ago
- 9 min read
Updated: 16 hours ago
In Part 3 of this blog series, I introduced the DPDP implementation framework, which includes a nine-layer implementation model.
This model incorporates privacy into governance, architecture, operations, and culture, enabling banks to move beyond a reactive phase referred to as Program Governance and Enterprise Alignment. In the prior blog post, we discussed methods for achieving enterprise alignment within banks.
From a program governance perspective, implementing DPDP begins with forming a DPDP steering committee. DPDP execution requires collaboration beyond a single team, as it involves various business and IT functions such as consent, rights management, retention, breach response, vendor oversight, and system redesign. Without a central governing body, DPDP can become fragmented, inconsistent, and unclear. Therefore, the DPDP steering team should comprise cross-functional leadership.
Core Actions to Implement
Appoint essential leaders: CISO, DPO/Privacy Officer, CIO/CTO, CRO, Compliance, Legal, Operations, Digital Banking, and business heads.
Authorize a DPDP implementation roadmap.
Examine all data processing activities and categorize them under fiduciary/processor responsibilities.
Delegate ownership of each DPDP responsibility to the appropriate department.
Hold regular meetings to assess progress, risks, and audit findings
DPDP Act 2023 Section 7(1) — Person responsible for business or activity of the Data Fiduciary , states that "The Data Fiduciary shall be responsible for complying with the provisions of this Act and the rules made thereunder, and the person responsible for carrying on the business or activity of the Data Fiduciary shall be accountable for such compliance". Banks and enterprises typically designate a Privacy Officer / Chief Privacy Officer to fulfil this responsibility.
Incase of Significant Data Fiduciary (SDF), DPDP Act 2023 Section 10(3), states that "The Data Protection Officer (DPO) shall be the point of contact for the Data Principal on behalf of the Significant Data Fiduciary".
The third crucial step for organizations is to establish policies, standards, guidelines, standard operating procedures, operating models, and toolkits such as:
DPDP Policy
Data Classification Standard
Minimisation SOP
Rights Handling SOP
Consent Framework
Retention & Deletion SOP
Vendor Assessment Toolkit
Breach Notification SOP
Privacy-by-Design Guidelines
API Privacy Standards
Integrate DPDP into all departments to ensure privacy responsibilities are part of daily business operations. Without a defined operating model, each team improvises, causing gaps. Consider the following actions:
Create workflow blueprints for consent, rights, retention, breaches, and vendor onboarding.
Automate processes wherever possible: consent logs, retention triggers, and access governance.
Develop standard templates for:
Consent text
Notices
DPIA
Vendor assessments
Incorporate DPDP into the SDLC, change management, access reviews, and risk assessments.
RBI frameworks already require committees like ISSC, RMC, IT Strategy, and Outsourcing Committee. Integrating DPDP ensures privacy risks are tracked alongside cybersecurity, operational risk, and strategic initiatives. The below provide DPDP aligned responsibilities for each of these committees.
DPDP needs measurable indicators. Boards and regulators increasingly want evidence.
Providing a sample DPDP specific KRI
% Systems with classified data
% Sensitive data encrypted
Consent capture rate by channel
Rights request SLA compliance
Vendor DPDP compliance scores
Retention compliance %
Data minimisation progress
Breach detection & reporting timelines
The DPDP Steering Committee closely monitors these metrics to assess the bank’s maturity in data protection and privacy compliance. They evaluate encryption coverage, consent capture effectiveness across channels, SLA adherence for rights requests, and vendor compliance scores to identify operational gaps and regulatory risks. By analyzing breach detection timelines, retention discipline, and data minimisation progress, the committee advises the bank on prioritizing remediation efforts, enhancing governance, and aligning with DPDP mandates. Their guidance ensures that privacy is embedded in business processes, vendor relationships, and customer interactions, fostering trust and regulatory resilience.
Data Discovery and Classification
Banks need to comprehend the existence of personal data, its flow, who has access to it, and its retention duration. This understanding is crucial for compliance with data privacy regulations and for building trust with customers. The steps outlined below are essential to support the overall context of DPDP, ensuring that banks can effectively manage personal data while mitigating risks associated with data breaches and misuse.
Key Steps
2.1 Identify all Personal & Sensitive Personal Data
It is essential to identify all types of personal and sensitive personal data that the bank holds. Examples include:
KYC (PAN, Aadhaar, Voter ID)
Biometric identifiers (fingerprint/IRIS for KUA)
Contact details
Income proofs, statements
Loan documents
Transaction history
Device identifiers (IMEI, IP, geolocation)
2.2 Classify Data (C1–C4 Model)
Data classification helps in managing data according to its sensitivity. The typical BFSI model includes:
C1: Public data
C2: Internal operational data
C3: Personal data
C4: Sensitive personal/financial data
2.3 Map Data Flows (End-To-End)
Mapping data flows across various systems is critical for understanding how personal data moves within the organization. Key systems include:
Core Banking System (CBS)
LOS/LMS
CRM
Mobile & Internet Banking
ATM Switch / UPI
Collections and recovery systems
DWH and analytics
Email, messaging systems
Cloud workloads
Third-party ecosystems & fintech APIs
HR, payroll, ERP systems
2.4 Identify Shadow/Unstructured Data
Identifying unstructured data is crucial as it often contains sensitive information that may not be adequately protected. This includes:
Shared folders
Email attachments
Personal drives
Exports from LOS/CRM
WhatsApp-based processes (common in branch ops)
2.5 Discovery & Classification Tools Recommended
To effectively manage personal data, the following tools are recommended:
DSPM (Data Security Posture Management)
Enterprise DLP (Data Loss Prevention)
Data discovery agents
API/microservice flow mapping tools
Cloud-native discovery tools
Drive data minimisation
Banks conduct data audits to map usage against DPDP "purpose limitation," then purge non-essential fields like old addresses or duplicate transaction logs no longer needed for servicing or RBI reporting.
Practical measures include:
Business unit-led "data diets": Each department (loans, deposits, collections) justifies every PII field in forms and reports, removing optional demographics or historical notes from active systems.
Dynamic field suppression: CRM and LOS auto-hide fields post-purpose (e.g., income proof vanishes after loan disbursement), enforced via database views or API gateways.
Enforce masking/tokenisation in non-production
Development, testing, QA, and analytics environments must never hold production PII; banks deploy format-preserving encryption or token vaults to swap real Aadhaar/PAN with surrogates while preserving app logic.
Mandate tokenisation gateways: Route all non-prod data through pii vault services (e.g., Thales CipherTrust) so developers query fake PANs that resolve to real data only in prod.
DevSecOps pipelines: CI/CD scans block unmasked data commits, auto-redact datasets, and enforce "no PII in GitHub/Slack" via DLP agents.
Quarterly scans: DSPM tools verify zero live KYC in dev/test, flagging violations for immediate sanitisation.
Remove redundant KYC copies
Multiple KYC copies proliferate across LOS, CRM, branch folders, vendor portals, and archives; banks centralise into a single KYC vault with immutable storage and point-to-point references.
CKYC/CIC integration: Pull live KYC from Central KYC Registry or CIBIL APIs instead of local storage, retaining only digest hashes and expiry dates in CBS.
De-duplication sweeps: Run annual entity resolution to merge customer profiles, delete 80%+ redundant docs, and redirect apps to canonical records.
Access revocation: Expire read/write permissions on legacy copies post-migration, using IAM to enforce single-source-of-truth.
Implement retention automation
Manual retention leads to indefinite storage; banks embed DPDP/RBI timelines (e.g., 7 years for loans, 10 for grievances) into metadata-driven policies that auto-delete at expiry.
Policy engines: ILM tools (e.g., IBM Spectrum, Veritas) tag records with retention dates, auto-archive to cold storage, then purge with audit trails.
Customer-triggered expiry: Post-account closure, consent withdrawal, or purpose end, trigger 30-day erasure workflows across CBS/CRM/DWH with reconfirmation logs.
Exception handling: Legal hold flags pause deletion for disputes/RBI audits, auto-released post-resolution.
Reduce shadow and unstructured datasets: Shadow data in emails, shares, WhatsApp, and exports evades controls; banks scan, classify, then quarantine or delete via DSPM/DLP with user education.
Agentless crawling: Weekly DSPM scans of OneDrive, email, file servers detect PII patterns (Aadhaar regex, PAN formats), auto-quarantine high-risk files.
Endpoint DLP: Block USB/email exports of C3/C4 data, force uploads to governed repositories with expiry timers.
Branch process redesign: Replace WhatsApp KYC sharing with secure portals/apps that auto-delete after verification, plus training on "no screenshots".
Outcome
The ultimate goal of these steps is to create a comprehensive and authoritative data inventory that provides insights into vulnerabilities and risks associated with personal data. This inventory is foundational for implementing effective data protection strategies and ensuring compliance with data privacy regulations.
Banks struggle with personal data primarily because legacy technology, fragmented data ownership, and weak vendor governance make visibility and control extremely hard to achieve at DPDP standards.
Legacy Data: Legacy core banking and surround systems often lack fine-grained access controls, data lineage, audit trails, and modern encryption, making it difficult to demonstrate compliance with evolving privacy regulations.
Patching privacy controls on top of such systems leads to brittle workarounds, inconsistent masking/retention, and high migration risk, especially when decades of customer records must be cleaned, mapped, and minimised without breaking critical business processes.
Data Spanning Across Organizations: Customer data typically sits across CBS, LOS/LMS, CRM, mobile banking, DWH, marketing stacks, HR and finance systems, with each function maintaining its own schema, reports and extracts.
This fragmentation creates inconsistent “sources of truth”, makes it hard to build a single DPDP-compliant inventory, and complicates fulfilment of rights (access, correction, erasure) because no one view reliably shows all locations where a given customer’s data resides.
Vendor Management: Under DPDP, banks remain accountable for personal data processed by third parties, but vendor ecosystems now span fintechs, collection agencies, analytics firms, cloud providers, BPOs, and RegTech/SaaS platforms.
Many banks lack centralised vendor registers, risk tiering, and continuous assessment of processor security, leading to weak contracts, opaque sub-processor chains, and limited monitoring of how and where vendors store, copy, or further share customer data.
Remedial Measures
Banks can overcome these challenges by combining structural data governance changes with targeted technology and vendor reforms, then measuring outcomes through clear KPIs and independent assurance.
Fixing legacy and scattered data
- Modernise in phases: Wrap legacy systems with APIs, segregate “hot” operational data from “cold” historical data, and progressively migrate or archive old records into modern, searchable, access-controlled platforms instead of big-bang replacements.
- Establish a bank-wide data governance model: Define data owners/stewards, common data dictionaries, and a unified data inventory so that all systems (CBS, LOS, CRM, DWH, channels) map back to the same customer and product data definitions.
- Use discovery and catalog tools: Deploy DSPM, data catalogs, and scanning tools to automatically find and classify personal data across databases, file shares, cloud, and analytics platforms, feeding into DPDP-required records of processing.
Strengthening vendor and processor controls
- Formal vendor risk management: Maintain a central vendor register, classify vendors by data and service criticality, and perform structured due diligence before granting access to personal data.
- Contractual and technical safeguards: Embed DPDP clauses, breach notification timelines, audit rights, and data localisation/retention rules into contracts, and couple this with least-privilege access, network segmentation, and strong authentication for vendor users and APIs.
- Continuous monitoring: Review SOC/ISO reports, run periodic security assessments, track SLA/KPI breaches, and require remediation plans for identified gaps instead of treating assessments as one-time events.
Making the steps effective (assurance)
- Define KPIs and KRIs: Track metrics such as percentage of systems covered by data discovery, number of orphan data stores eliminated, reduction in high-risk vendors, time to fulfil access/erasure requests, and DPDP audit findings.
- Embed controls into BAU: Integrate data checks into change management, onboarding of new products/fintechs, and regular IT operations so controls are not “project-only”.
- Independent validation: Use internal audit or external assessors to test data flows, verify that inventory and classification are accurate, simulate breaches or rights requests, and confirm that responses meet regulatory expectations over time.
Conclusion
DPDP implementation is far more than a compliance exercise — it requires banks to rethink how data is governed, processed, and protected across the enterprise. Effective execution begins with strong program governance: a cross-functional DPDP Steering Committee, clearly assigned fiduciary and processor responsibilities, and a unified set of policies, standards, operating models, and toolkits that embed privacy into day-to-day operations.
As banks operationalise DPDP, measurable indicators such as consent capture rates, data classification coverage, retention compliance, and rights-request SLAs become essential for tracking maturity and guiding remediation. Data discovery and classification serve as the foundation, enabling banks to understand what personal data they hold, where it resides, how it flows, and who accesses it — insights that are often obscured by legacy systems, fragmented data ownership, and expansive vendor ecosystems.
Overcoming these challenges requires a combination of structural data governance reforms, modernised technology patterns, and stronger vendor oversight. Phased legacy modernisation, enterprise-wide data ownership, automated discovery tools, and contractually enforced DPDP controls help restore visibility and discipline. Continuous monitoring, KPIs/KRIs, and independent assurance ensure that DPDP does not remain a project but evolves into a sustainable BAU practice.
Ultimately, DPDP strengthens trust — between banks, customers, regulators, and partners. By embedding privacy into governance, architecture, operations, and culture, banks build resilience, reduce regulatory exposure, and position themselves for a future where data protection is a defining pillar of digital financial services.
📚 Further Reading – DPDP Blog Series (In Case You Missed Them)
If you’re following the DPDP Implementation series, here are the earlier parts to help you build a complete understanding of how privacy, governance, and data protection come together in Indian BFSI:
🔹Part 1 — DPDP Rules: What You Must Know
Your essential primer on the DPDP Act, how the Rules shape real obligations for Data Fiduciaries, and what banks must prepare for in the coming years.
🔹Part 2 — DPDP for Banks: Why It Matters More Than Ever
A deep dive into why DPDP is transformative for Indian banks, how it elevates privacy into a strategic capability, and how it aligns with RBI’s cybersecurity and governance expectations.
🔹Part 3 — DPDP Implementation Framework for RBI-Regulated Banks
Introduces a 9-layer implementation model that blends governance, architecture, operations, and culture — the foundation for moving beyond checkbox compliance.
Together, these earlier parts create the complete context for Part 4, helping you understand why DPDP requires a framework-led approach and how banks can progress toward true operational privacy maturity.
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