DPDPA Compliance Platform

Your Engineering Stack Has a DPDPA Problem. Qverlabs Fixes It Automatically.

Personal data is scattered across your databases, S3 buckets, microservices, and code repositories. Your developers have unknowingly logged PII. Your consent infrastructure may not meet the DPDPA standard. And your team does not have the bandwidth to audit all of it manually before enforcement begins in May 2027.

Updated

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Qverlabs deploys a coordinated system of AI agents that scan every layer of your data infrastructure, map compliance gaps to specific DPDPA sections, and generate audit-ready evidence - without a six-month consulting engagement.

The Compliance Gap Most CTOs Don't See Until It's Too Late

Vendor Data Breach

If a vendor breaches data you gave them, you are still liable under DPDPA.

Hardcoded PII in Test Data

A real Aadhaar number hardcoded in a test fixture three years ago -that is your exposure.

API Without Consent Check

An API endpoint returning user profiles without consent verification is a Section 6 violation in production right now.

Manual audits catch some of this. Spreadsheet-based data mapping catches less. Neither scales as your systems grow, your team ships new features, and regulations evolve. What organisations processing Indian personal data actually need is a system that runs continuously, knows where every piece of PII lives, and can prove compliance on demand.

Multi-Agent Architecture: How Qverlabs Works Under the Hood

Qverlabs does not run a single scan and produce a report. It deploys a set of specialised agents, each responsible for a distinct layer of your infrastructure. They run in parallel, produce structured findings with DPDPA section references, and feed into a Lead Aggregator Agent that correlates everything into a unified compliance picture.

Database Agent

Connects to SQL and NoSQL databases. Scans schemas, samples data rows, detects PII columns using a four-layer detection engine - regex pattern matching for India-specific identifiers (Aadhaar, PAN, UPI IDs, passport numbers), column name heuristics, transformer-based Named Entity Recognition, and LLM-based contextual reasoning for ambiguous cases.

  • Checks encryption status & access controls
  • Verifies consent records exist for stored data
  • India-specific identifier detection

Data Lake & S3 Agent

Indexes your object stores and data lakes. Analyzes bucket policies, samples CSV, Parquet, and JSON files, verifies encryption configuration, and flags missing or inconsistent retention policies.

  • Bucket policy analysis
  • Unstructured data sampling
  • Retention policy validation

Document Agent

Scans PDFs, Excel files, Word documents, and images using OCR with Hindi and English language support. HR shared drives, onboarding documents, scanned ID cards - this agent surfaces PII that lives outside your databases entirely.

  • OCR with Hindi + English support
  • HR documents & onboarding files
  • Scanned ID card detection
Blind Spot

Code Repository Agent

This is where most enterprise compliance programmes have a genuine blind spot. Clones your GitHub or GitLab repositories and audits source code, config files, test fixtures, log statements, API routes, database migrations, and CI/CD pipelines.

What it finds in practice:

Section 8(5)

PII logged in application logs without encryption
logger.info(f"User {user.email} logged in from {user.ip}")

Critical

Real Aadhaar and PAN numbers hardcoded in test data files - no consent

Section 6

API endpoints that return personal data without consent verification

Lead Aggregator Agent

Receives structured findings from every agent, deduplicates across sources, maps each issue to the relevant DPDPA section, calculates penalty exposure, and produces a unified compliance report.

  • Detailed PDF/DOCX audit report (50-80 pages)
  • Live web dashboard with drill-down
  • Severity, system, and DPDPA section filters

Four-Layer PII Detection Engine

The accuracy of any compliance system depends entirely on how well it detects personal data. Qverlabs uses four detection layers in sequence.

1

Regex Pattern Matching

Fast, high-recall detection of India-specific identifiers - Aadhaar numbers, PAN cards, mobile numbers, email addresses, UPI IDs, passport numbers, and more.

High Recall
2

Column Name Heuristics

Fuzzy matching on database column names and file headers catches PII even when the data itself is masked or tokenised. A column named aadhaar_no is flagged regardless of what values it currently holds.

Schema Aware
3

NER Model

A transformer-based Named Entity Recognition model fine-tuned on Indian PII categories classifies entities in unstructured text across documents, logs, and code.

AI-Powered
4

LLM Contextual Analysis

For cases where the first three layers are inconclusive, an LLM evaluates whether the data constitutes personal data under DPDPA's definition based on surrounding context.

Context-Aware
97.3%Precision
94.8%Recall

Combined accuracy across Indian PII benchmark datasets

What the Platform Covers Across the Full DPDPA Obligation Stack

Beyond discovery and auditing, Qverlabs handles the operational compliance obligations that your team would otherwise need to build or manage manually.

Data Principal Rights

A centralised intake system for all Data Subject Requests. Agents classify each request, identify which systems and vendors hold the relevant data, assign resolution tasks, track statutory deadlines, and draft compliant responses. The full handling chain is logged and auditable.

Breach Detection & Notification

Real-time monitoring for anomalies and security incidents. When a potential breach is detected, agents classify severity, identify affected data principals, determine notification obligations, and generate pre-populated notification drafts aligned with DPDPA requirements - including mandatory reporting to the Data Protection Board. Every action and decision in the response chain is timestamped and logged.

Cross-Border Transfer Monitoring

Tracks data location and vendor processing geography in real time. Enforces country allow-lists, flags restricted jurisdiction transfers as they occur, and maintains contractual safeguard documentation for permitted transfers under DPDPA Section 16.

Vendor & Processor Accountability

Maintains a live vendor registry with data access mapping, contract compliance status, and risk scoring. Monitors sub-processing arrangements and flags changes in vendor risk posture. Because under DPDPA, your liability does not end at your own systems.

Continuous Compliance Monitoring

Section-by-section compliance scoring, automated gap analysis, remediation tracking with priority ranking, and board-ready dashboards. For Significant Data Fiduciaries, the platform supports DPIA workflows, DPO reporting, and annual audit preparation. Regulatory change tracking ensures your compliance posture updates as government notifications evolve.

Penalty Risk Assessment

Every compliance gap is linked to its DPDPA section and a live penalty exposure score. Your legal and engineering teams can prioritise remediation based on actual financial risk - not guesswork. The penalty calculator updates in real time as issues are resolved.

Employee Training & Awareness

Role-based training modules covering data handling, consent obligations, breach identification, and rights request procedures. Tracks completion, supports certification, and feeds into your overall compliance dashboard.

Deployment Roadmap: From First Scan to Continuous Compliance

Most organisations reach a defensible compliance baseline within 8 weeks of deployment.

Phase 1Weeks 1-6

Foundation & First Scan

Connect data source integrations, activate the PII Detection Engine, run the first full scan, generate a baseline compliance report with penalty exposure by system.

Phase 2Weeks 7-12

Expanded Coverage

Expand coverage to data lakes, document stores, and code repositories. Activate the Children's Data Detection module. Launch the live compliance dashboard.

Phase 3Weeks 13-24

Automation & Intelligence

Enable AI-driven retention and consent decision engine, cross-source correlation, automated remediation ticket generation, and penalty calculator.

Phase 4Ongoing

Continuous Compliance

Continuous compliance with automated scans, CI/CD pipeline integration for code repo monitoring, real-time alerts, and regulatory update tracking.

Built for the Verticals With the Most at Stake

DPDPA obligations are uniform, but data handling patterns are not. The specific risks - and the specific systems where PII lives - differ significantly by industry.

BFSI & Fintech

Payment data and KYC records create dense PII concentrations requiring stringent controls.

Healthcare

Sensitive personal data and records of minors require the strictest handling under DPDPA.

E-Commerce & D2C

Consent at scale and retention sprawl are the dominant risks for consumer-facing platforms.

SaaS & IT Services

Data processed on behalf of clients creates layered fiduciary obligations and shared liability.

EdTech

Children's data protection under Section 9 carries the highest penalty tier at ₹200 crore.

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Frequently Asked Questions

The platform deploys agent connectors for standard database types, cloud storage providers (AWS S3, GCP, Azure Blob), and source code platforms (GitHub, GitLab). Integration is read-only for scanning purposes and does not require changes to your existing architecture. Specific connector documentation is available during onboarding.

A consulting engagement produces documentation - gap reports, policy drafts, assessment findings. Qverlabs produces a live compliance system. The agents run continuously, your compliance posture updates in real time, and evidence is generated automatically. You are not paying for a report that is outdated six months later.

Each report includes a DPDPA compliance matrix mapping every finding to the relevant section, an encryption audit with colour-coded status by system, an access control heat map, penalty exposure by issue, and prioritised remediation actions. Reports are available as PDF, DOCX, and via the live dashboard.

The platform is built to work across company sizes. Early-stage companies benefit most from the gap assessment and consent infrastructure modules, which establish a compliant foundation before data volumes scale. The agent architecture is the same regardless of size - coverage simply expands as more systems are connected.

Every gap generates a structured finding with the DPDPA section, severity level, penalty exposure, and a specific remediation action. High-severity findings trigger alerts. The remediation tracker lets your team assign, track, and close issues - with each resolution logged as compliance evidence.

Disclaimer: The information on this page is for general informational purposes only and does not constitute legal advice. For specific guidance on DPDPA compliance, consult a qualified legal professional. Regulatory requirements may change — verify current obligations with official government sources.

Get DPDPA-Ready with Qverlabs

Your engineering stack has a compliance problem. Our AI agents find it, map it, and generate audit-ready evidence - automatically.

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