Is this a consulting service?

No, this is a free, interactive software tool that calculates your potential liability under the DPDP Act 2023 in 30 seconds. It provides an instant risk score and automated action plan.

What are common DPDP violations in Lead Scoring Data Audit?

Common violations include: Using third-party enriched data (e.g., from ZoomInfo, Lusha) for scoring without verifying the original source's DPDP-compliant consent., Automated profiling of individuals (e.g., assigning a 'hot' score) based on personal data without explicit, purpose-specific consent., Integrating CRM/marketing automation tools (like Salesforce, HubSpot) that process personal data for scoring without robust DPDP-compliant Data Processing Agreements..

The DPDP Audit Tool

Compliance for Lead Scoring Data Audit

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Lead Scoring Data Audit
Liability Check

Your sophisticated lead scoring models are processing personal data to automate sales and marketing workflows. Under DPDP, any automated processing for profiling or decision-making without a lawful basis (like explicit consent) is a direct penalty risk up to ₹250 Crore.

Why Lead Scoring Data Audit is at Risk

B2B growth and revenue ops teams often rely on vast amounts of **personal data** — from public profiles to purchased lists and internal engagement signals — to score and prioritise leads. Under DPDP, using this data for **automated profiling and decision-making** (like assigning a 'hot' score or triggering an outreach sequence) requires a clear legal basis. Imagine a SaaS company in Gurgaon's Cyber Hub leveraging enriched data from tools like ZoomInfo or Lusha to automatically qualify leads without ensuring the original consent for such processing. If your lead scoring inputs, profiling logic, or enrichment processors handle personal data without **DPDP-compliant consent or a defined legitimate use**, your entire revenue engine is a compliance ticking bomb. The Data Protection Board will scrutinise how personal data influences automated decisions.

Common Violations

1.Using third-party enriched data (e.g., from ZoomInfo, Lusha) for scoring without verifying the original source's DPDP-compliant consent.
2.Automated profiling of individuals (e.g., assigning a 'hot' score) based on personal data without explicit, purpose-specific consent.
3.Integrating CRM/marketing automation tools (like Salesforce, HubSpot) that process personal data for scoring without robust DPDP-compliant Data Processing Agreements.

The Immediate Fix

Immediately conduct an audit of all data sources feeding your lead scoring models, especially third-party enrichment providers. Map out the consent trail for every personal data point used in scoring and automated profiling logic to identify immediate gaps.