Data Classification RFI/RFP questionnaire — 0-Doubt
Data Classification evaluation questionnaire
Start from a neutral baseline and add what matters to you. Criteria are labeled by source — the platform baseline is architecture-neutral; buyer-contributed criteria are shown separately.
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1. Weight what matters
20 criteria
baselineWhat classification methods does the platform use — regex/pattern matching, machine learning/NLP-based content analysis, or user-driven manual tagging — and how are these combined to reduce both false positives and missed sensitive data?
baselineDescribe measured classification accuracy (false-positive and false-negative rates) on a realistic, mixed dataset, and provide a customer reference for accuracy at production scale versus a vendor-controlled demo dataset.
baselineHow does classification scale across both structured (databases, data warehouses) and unstructured (documents, email, chat) data sources, and is the same taxonomy and confidence scoring applied consistently across both?
baselineWhat happens after classification — does the platform only label data, or does it also drive downstream enforcement (DLP policy, access restriction, encryption) automatically based on the assigned classification?
baselineDetail how the platform handles classification drift over time — re-classification as documents are edited/shared, and detection of sensitive data introduced into previously-classified-as-safe repositories.
baselineCan classification labels and confidence scores be reviewed and corrected by data owners, and does the platform learn from those corrections to improve future accuracy for similar content?
baselineExplain regulatory-taxonomy coverage out of the box (PII, PHI, PCI, GDPR special categories, industry-specific data types) versus what requires custom rule-building by the customer, with typical time-to-value for a new custom data type.
baselineHow does the platform avoid becoming a second, conflicting source of truth when the customer already has classification labels applied by Microsoft Purview, Google, or a DLP tool — does it read/reconcile existing labels or overwrite them?
baselineWhat is the pricing model — per data source, per document/record volume, or a flat enterprise tier — and how does cost scale as the volume of classified data grows significantly across a larger data estate?
baselineDuring an active security investigation, can the platform quickly answer 'what sensitive data was in this specific compromised system' using existing classification data, with a concrete turnaround-time example from a customer reference?
baselineDetail historical trend reporting on classification coverage across the data estate (percentage of data sources classified, coverage-gap trend) over time, suitable for demonstrating program maturity to leadership.
baselineHow does the platform integrate with (versus duplicate) the customer's existing DSPM/DLP tooling — does classification feed into the same unified risk view, or are they two disconnected systems each performing similar classification work?
baselineWho within the customer organization gets access to classification results and labels, and is there role-based access control given that a complete data-sensitivity map is itself valuable reconnaissance information?
baselineDoes the platform address AI/GenAI-specific classification needs — classifying data as it flows into or out of AI tools/prompts, distinct from static at-rest classification — and is this a native capability or entirely out of scope?
baselineHow consistent is classification depth across multi-cloud/hybrid data sources — is coverage equally deep across on-prem file shares, multiple cloud storage providers, and SaaS applications, or meaningfully shallower for one environment?
baselineWhat is a customer-referenced onboarding timeline from contract signature to the platform providing genuinely useful classification coverage across an existing, large, previously-unclassified data estate?
baselineDocument chain of custody: how password data is accessed, transferred, stored, and protected end-to-end.
baselineStore all engagement data in encrypted form, not transmit municipal data to any third party, not use municipal data for marketing/case studies/AI-ML training, and securely destroy all engagement data no later than 30 days after final report acceptance except where retention is required by law.
baselineThe bidder should configure the proposed DLP solution to collect O365 Email DLP alerts for a centralized dashboard, and integrate the classification solution with the financial institution's existing Azure Information Protection (AIP) — reclassifying existing AIP-tagged files seamlessly, taking ownership of AIP data classification/labelling policy shortcomings if any.
baselineDoes this product or service capture and/or retain names, addresses, date of birth, Social Security Number, health information, Department of Defense information, banking information, credit/debit card information, or grades? (Indicate all that apply)
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Platform baseline
neutral · staff-reviewed
RFIWhat classification methods does the platform use — regex/pattern matching, machine learning/NLP-based content analysis, or user-driven manual tagging — and how are these combined to reduce both false positives and missed sensitive data?Answer key — what a strong answer shows
Strong answers combine multiple methods (pattern matching for structured data like SSNs, ML/NLP for unstructured context) rather than relying on one technique alone.
RFPDescribe measured classification accuracy (false-positive and false-negative rates) on a realistic, mixed dataset, and provide a customer reference for accuracy at production scale versus a vendor-controlled demo dataset.Answer key — what a strong answer shows
Demo-dataset accuracy figures are frequently much higher than real-world production accuracy; insist on a customer-referenced figure at real scale.
RFIHow does classification scale across both structured (databases, data warehouses) and unstructured (documents, email, chat) data sources, and is the same taxonomy and confidence scoring applied consistently across both?Answer key — what a strong answer shows
A consistent taxonomy and confidence model across structured and unstructured sources is stronger than two disconnected classification engines with incompatible labels.
RFIWhat happens after classification — does the platform only label data, or does it also drive downstream enforcement (DLP policy, access restriction, encryption) automatically based on the assigned classification?Answer key — what a strong answer shows
Classification without downstream enforcement is only half the value; look for native integration that turns a classification label into an actual protective control.
RFPDetail how the platform handles classification drift over time — re-classification as documents are edited/shared, and detection of sensitive data introduced into previously-classified-as-safe repositories.
From other buyers
crowdsourced · anonymized
💬
No buyer-contributed criteria yet
Verified buyers can suggest criteria (anonymized before pooling).
Answer key — what a strong answer shows
Classification is not a one-time event; look for continuous re-scanning, not a single point-in-time labeling pass that goes stale.
RFICan classification labels and confidence scores be reviewed and corrected by data owners, and does the platform learn from those corrections to improve future accuracy for similar content?Answer key — what a strong answer shows
A feedback loop where owner corrections improve the model over time is materially stronger than a static classifier that repeats the same mistakes indefinitely.
RFPExplain regulatory-taxonomy coverage out of the box (PII, PHI, PCI, GDPR special categories, industry-specific data types) versus what requires custom rule-building by the customer, with typical time-to-value for a new custom data type.Answer key — what a strong answer shows
Strong answers separate built-in coverage from custom-rule-required coverage explicitly, and give a real time estimate for standing up a new custom classifier.
RFIHow does the platform avoid becoming a second, conflicting source of truth when the customer already has classification labels applied by Microsoft Purview, Google, or a DLP tool — does it read/reconcile existing labels or overwrite them?Answer key — what a strong answer shows
Look for explicit reconciliation with existing labeling ecosystems (especially Microsoft Purview, given its ubiquity) rather than a competing label set that creates confusion about which label is authoritative.
RFIWhat is the pricing model — per data source, per document/record volume, or a flat enterprise tier — and how does cost scale as the volume of classified data grows significantly across a larger data estate?Answer key — what a strong answer shows
Look for transparent, predictable scaling economics; a vendor unable to project cost at meaningfully larger data volume creates real budget risk for a growing data estate.
RFPDuring an active security investigation, can the platform quickly answer 'what sensitive data was in this specific compromised system' using existing classification data, with a concrete turnaround-time example from a customer reference?Answer key — what a strong answer shows
Fast classification-based lookup during an active incident is a high-value, distinct use case from steady-state classification — ask for a real turnaround-time figure, not just confirmation that classification data exists.
RFIDetail historical trend reporting on classification coverage across the data estate (percentage of data sources classified, coverage-gap trend) over time, suitable for demonstrating program maturity to leadership.Answer key — what a strong answer shows
Trend-over-time coverage reporting is a distinct capability from a per-source classification configuration — confirm this exists as a maintained, exportable report.
RFPHow does the platform integrate with (versus duplicate) the customer's existing DSPM/DLP tooling — does classification feed into the same unified risk view, or are they two disconnected systems each performing similar classification work?Answer key — what a strong answer shows
Look for genuine integration avoiding redundant classification work; two overlapping tools each independently classifying the same data creates real reconciliation burden and potentially conflicting labels.
RFIWho within the customer organization gets access to classification results and labels, and is there role-based access control given that a complete data-sensitivity map is itself valuable reconnaissance information?Answer key — what a strong answer shows
A complete data-classification map is a meaningful target in its own right — role-based access control over the tool's own findings is an often-overlooked consideration.
RFIDoes the platform address AI/GenAI-specific classification needs — classifying data as it flows into or out of AI tools/prompts, distinct from static at-rest classification — and is this a native capability or entirely out of scope?Answer key — what a strong answer shows
AI-flow-specific classification (data entering a prompt, or being used to train a model) is a distinct and increasingly important use case from traditional static-repository classification — a vendor should give a specific answer rather than assuming static classification extends automatically.
RFPHow consistent is classification depth across multi-cloud/hybrid data sources — is coverage equally deep across on-prem file shares, multiple cloud storage providers, and SaaS applications, or meaningfully shallower for one environment?Answer key — what a strong answer shows
Ask for an honest per-environment coverage breakdown; uneven classification coverage across environments is a common real gap a vendor should disclose rather than obscure.
RFIWhat is a customer-referenced onboarding timeline from contract signature to the platform providing genuinely useful classification coverage across an existing, large, previously-unclassified data estate?Answer key — what a strong answer shows
Strong answers give a concrete, customer-validated timeline for a realistic large-scale retrofit scenario (not a small greenfield deployment), and are honest about the customer-side effort required.
RFPDocument chain of custody: how password data is accessed, transferred, stored, and protected end-to-end.
RFPStore all engagement data in encrypted form, not transmit municipal data to any third party, not use municipal data for marketing/case studies/AI-ML training, and securely destroy all engagement data no later than 30 days after final report acceptance except where retention is required by law.
RFPThe bidder should configure the proposed DLP solution to collect O365 Email DLP alerts for a centralized dashboard, and integrate the classification solution with the financial institution's existing Azure Information Protection (AIP) — reclassifying existing AIP-tagged files seamlessly, taking ownership of AIP data classification/labelling policy shortcomings if any.
RFPDoes this product or service capture and/or retain names, addresses, date of birth, Social Security Number, health information, Department of Defense information, banking information, credit/debit card information, or grades? (Indicate all that apply)
