What data channels does the platform monitor and enforce policy on — endpoint (USB, clipboard, print), network/email, cloud/SaaS (CASB-integrated), and generative AI tools — and which require separate modules or licenses?

Look for a coverage matrix and clarity on which channels are native versus requiring an additional module.

Describe the content-detection methodology beyond simple regex/keyword matching (e.g., exact data matching/fingerprinting, machine-learning classifiers, contextual analysis) and provide a measured false-positive rate from a customer reference.

Strong answers name specific detection techniques beyond regex and give a real false-positive figure; regex-only DLP is notoriously noisy in production.

How does the platform handle unstructured/sensitive documents that don't match a known pattern (e.g., a proprietary design document, source code) — is there a training/fingerprinting workflow for customer-specific sensitive content?

Look for a described fingerprinting/exact-data-match training workflow for arbitrary customer content, not just built-in patterns for common data types.

Detail the incident response workflow when a policy violation is detected — automated blocking versus alert-only — and what is the typical time from policy tuning to a production-ready ruleset with a customer reference.

Strong answers distinguish block vs. alert-only modes and state a real time-to-production-tuning figure, since over-aggressive blocking on day one is a common deployment failure.

How does the product handle encrypted or password-protected files and channels (e.g., encrypted email attachments, HTTPS-inspected traffic) — can policy be enforced without breaking encryption entirely?

Look for a specific technical answer (e.g., endpoint-side inspection before encryption, or documented TLS inspection architecture), not a vague 'we handle encrypted data.'

What integration exists with data classification/labeling tools (e.g., Microsoft Purview labels) so DLP policy can act on classification metadata rather than re-detecting content from scratch?

Native classification-label integration avoids duplicate detection logic and is stronger than a DLP product that ignores existing labels.

Explain how the platform monitors and controls data movement into generative AI tools (ChatGPT, Copilot, etc.) specifically, including whether this requires a separate browser extension or agent.

This is a fast-growing exfiltration vector — look for a specific, current answer rather than a dated capability list that predates gen-AI tools.

What reporting exists for compliance evidence (e.g., proving DLP controls were active and enforced for a specific period, for an auditor) — is this a built-in exportable report?

Look for a native, exportable compliance report rather than requiring the customer to build one from raw logs.

What is the pricing model — per endpoint, per user, or a flat enterprise tier — and does cost differ meaningfully between endpoint-only DLP and full-channel coverage (network, cloud, GenAI), given that partial coverage is a common way buyers get surprised by upsell costs later?

Look for transparent, tier-differentiated pricing that's explicit about which channels are included at the base price versus a paid add-on.

Describe detection specific to insider-threat exfiltration patterns (a departing employee bulk-downloading files, unusual after-hours access to sensitive repositories) distinct from generic content-based policy matching, and provide a customer reference showing a real insider incident caught.

Behavioral/insider-pattern detection is a materially different capability from pure content-matching DLP — ask for a real caught-incident reference, not just a feature checkbox.

What mobile-device DLP coverage exists — can policy be enforced on data accessed or copied via managed mobile devices, or is coverage effectively limited to desktop/laptop endpoints?

Mobile is a common and growing DLP blind spot — a vendor should give a specific, current answer on mobile coverage depth rather than an unqualified 'all endpoints' claim.

Does the platform address physical exfiltration channels (screen capture, photographing a screen with a phone) via any technical control, or is this explicitly out of scope and left to policy/training?

This is a genuine, hard-to-solve technical limitation for most DLP products — a vendor should be honest that this is largely out of scope rather than overselling a marginal watermarking feature as a real control.

Detail multi-tenant/subsidiary policy management for a larger organization — can different business units or subsidiaries have independently managed DLP policies from one console, with appropriate data isolation between them?

Look for genuine per-entity policy isolation; a single flat policy set forcing one-size-fits-all rules across a diverse organization creates real friction and either over- or under-blocks for different business units.

What is the vendor's honest disclosure of false-negative risk (sensitive data that slips through undetected), and what independent or customer-validated testing has been done to measure it, distinct from the more commonly-cited false-positive rate?

False-negatives (missed real leaks) are arguably more dangerous than false-positives (blocked legitimate traffic) but far less commonly disclosed — a vendor willing to discuss this rate is more credible than one only ever citing accuracy in terms of false positives.

How does the platform integrate with UEBA/user-risk-scoring so that DLP policy enforcement can be risk-adaptive (stricter for a flagged high-risk user, standard for everyone else) rather than uniformly applied regardless of context?

Risk-adaptive policy is a more sophisticated and lower-friction approach than uniform enforcement, which tends to generate excess false positives for normal user behavior.

Explain incident-forensics depth for a confirmed data-loss event — can an investigator quickly reconstruct exactly what data left, via what channel, to where, and by whom, with a concrete turnaround-time example from a customer reference?

Strong answers describe a fast, complete forensic reconstruction capability with a real turnaround-time figure — this is DLP's highest-stakes use case (an actual confirmed leak), not just preventive blocking.

Data Loss Prevention evaluation questionnaire

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1. Weight what matters

36 criteria

baselineWhat data channels does the platform monitor and enforce policy on — endpoint (USB, clipboard, print), network/email, cloud/SaaS (CASB-integrated), and generative AI tools — and which require separate modules or licenses?weight
baselineDescribe the content-detection methodology beyond simple regex/keyword matching (e.g., exact data matching/fingerprinting, machine-learning classifiers, contextual analysis) and provide a measured false-positive rate from a customer reference.weight
baselineHow does the platform handle unstructured/sensitive documents that don't match a known pattern (e.g., a proprietary design document, source code) — is there a training/fingerprinting workflow for customer-specific sensitive content?weight
baselineDetail the incident response workflow when a policy violation is detected — automated blocking versus alert-only — and what is the typical time from policy tuning to a production-ready ruleset with a customer reference.weight
baselineHow does the product handle encrypted or password-protected files and channels (e.g., encrypted email attachments, HTTPS-inspected traffic) — can policy be enforced without breaking encryption entirely?weight
baselineWhat integration exists with data classification/labeling tools (e.g., Microsoft Purview labels) so DLP policy can act on classification metadata rather than re-detecting content from scratch?weight
baselineExplain how the platform monitors and controls data movement into generative AI tools (ChatGPT, Copilot, etc.) specifically, including whether this requires a separate browser extension or agent.weight
baselineWhat reporting exists for compliance evidence (e.g., proving DLP controls were active and enforced for a specific period, for an auditor) — is this a built-in exportable report?weight
baselineWhat is the pricing model — per endpoint, per user, or a flat enterprise tier — and does cost differ meaningfully between endpoint-only DLP and full-channel coverage (network, cloud, GenAI), given that partial coverage is a common way buyers get surprised by upsell costs later?weight
baselineDescribe detection specific to insider-threat exfiltration patterns (a departing employee bulk-downloading files, unusual after-hours access to sensitive repositories) distinct from generic content-based policy matching, and provide a customer reference showing a real insider incident caught.weight
baselineWhat mobile-device DLP coverage exists — can policy be enforced on data accessed or copied via managed mobile devices, or is coverage effectively limited to desktop/laptop endpoints?weight
baselineDoes the platform address physical exfiltration channels (screen capture, photographing a screen with a phone) via any technical control, or is this explicitly out of scope and left to policy/training?weight
baselineDetail multi-tenant/subsidiary policy management for a larger organization — can different business units or subsidiaries have independently managed DLP policies from one console, with appropriate data isolation between them?weight
baselineWhat is the vendor's honest disclosure of false-negative risk (sensitive data that slips through undetected), and what independent or customer-validated testing has been done to measure it, distinct from the more commonly-cited false-positive rate?weight
baselineHow does the platform integrate with UEBA/user-risk-scoring so that DLP policy enforcement can be risk-adaptive (stricter for a flagged high-risk user, standard for everyone else) rather than uniformly applied regardless of context?weight
baselineExplain incident-forensics depth for a confirmed data-loss event — can an investigator quickly reconstruct exactly what data left, via what channel, to where, and by whom, with a concrete turnaround-time example from a customer reference?weight
baselineThe bidder shall implement the DLP software in High Availability across the primary data center and disaster recovery site, configure a UAT instance for testing (with DR/UAT licenses factored into the commercial bid), and ensure the configuration is vetted by the OEM with written confirmation to the financial institution.weight
baselineSolution will be productionised only after closure of all security findings by the financial institution's Information Security Department; if vulnerabilities remain unresolved beyond 30 days, the financial institution reserves the right to cancel the purchase order and require reimbursement of payments made for licenses/installation.weight
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.weight
baselineThe bidder shall take back-to-back OEM support so the financial institution can raise direct tickets with the DLP OEM; configure scheduled incident reports by criticality within the DLP solution or integrate with the financial institution's ManageEngine ticketing tool; and ensure the DLP solution can integrate with the financial institution's HRMS/Active Directory to route data-infringement reports to the appropriate reporting/reviewing authority.weight
baselineThe bidder shall provide one onsite support engineer (minimum 2 years DLP experience, specified educational qualifications) for an initial 1 year, with L2/L3 support from the OEM directly during that period; the financial institution reserves the right to discontinue onsite support after 6 months, after which offshore support (L1/L2/L3 via OEM) continues per the RFP scope.weight
baselineSelected bidder is responsible for product updates/upgrades (including version upgrades) throughout the contract at no additional cost; in the event of a Data Leakage incident, the onsite engineer must prepare and submit a Root Cause Analysis to the financial institution.weight
baselineThe on-site resource shall reconcile the count of endpoints with DLP agents at regular intervals and report; perform periodical backup restoration and participate in DR drill activities; and be responsible for DLP solution reconfiguration in the event of migration to cloud or hardware/software crash during the contract.weight
baselineThe bidder shall provide periodic security awareness training at least 4 times per calendar year to the financial institution's staff before and after production deployment; provide at least 10 end-user training sessions in the first year plus handholding to designated staff; and arrange admin training for at least 3 of the financial institution's admins from the OEM.weight
baselineOnsite Engineer shall be responsible for DLP agent deployment on end-users' machines, policy configuration/customization per the financial institution's Data Governance Policy, and monitoring DLP events/alerts with regular reporting; the financial institution reserves the right to interview and reject any deployed engineer at any stage.weight
baselineThe bidder is responsible for maintaining Secure Configuration Documents (SCD) before production and throughout the contract; the standard service window is 10:00am-6:45pm Monday-Friday, but the vendor must support Public Holidays and Sundays in emergency situations.weight
baselineSecondary Red Team objectives: establish persistent access and maintain stealth, lateral movement across network segments/geographic boundaries, privilege escalation to critical administrative roles, exfiltration of sensitive data without detection, and bypassing of security controls (EDR, SIEM, DLP, MFA).weight
baselineWhat are your data loss prevention capabilities?weight
baselineSolution should support centralized incident management and triaging of alerts from multiple security products (SIEM, DLP, IPS, WAF, Anti-APT, ETDR); investigation module should integrate with log sources (SIEM, ETDR, EPP, Data Lake) on demand to pull data related to the investigated alert with charting and graphing to analyse data.weight
baselineZero Trust Phase 1: Contractor shall complete rollout/operationalization of the Microsoft 365 security suite including Microsoft Intune (device health/UEM), Defender for Endpoint (EDR/XDR), Microsoft Purview (DLP and sensitive data risk assessments), Mobile Access (ZTNA + Intune MAM), and Privileged Access Management (PAM) implementation using platforms including CyberArk, Microsoft Entra PIM, or Okta.weight
baselineThe bidder should be a company registered in India as per Companies Act 1956 and registered on mca.gov.in, with yearly sales turnover of minimum Rs. 25 crores each year and profitable during the last three financial years (2022-23, 2023-24, 2024-25), evidenced by a statutory auditor certificate.weight
baselineBidder must have supplied and implemented a DLP Solution suite, preferably to BFSI or PSU/Enterprise customers with at least 500 users, during the past 5 years in India, for at least 2 clients, evidenced by Purchase Order and Satisfactory Project Completion Certificate/email.weight
baselineBidder should have partnership with the OEM of the proposed solution and all its components (Manufacturer Authorization Form required); the Bidder's account should not have been declared an NPA in any bank/financial institution's books as of 31 March 2025; the proposed OEM should have a support centre and registered office in India.weight
baselineSupply, implement, commission and maintain a DLP solution at the financial institution's primary data center and disaster recovery site; the bidder in consultation with OEM should do sizing of processor cores, memory, and disk-based storage capacity.weight
baselineOffered products/software/hardware should be of the latest version, not End-of-Life/End-of-Support during the subscription term; the selected bidder must supply replacement products if the supplied products reach EOL/EOS during the contract period.weight
baselinePhase 2 roadmap: solution should support Role Mining, Fine-Grained Access Control, SIEM/DLP integration, integration with the Cherwell service management tool, Multi-Factor Authentication, advanced Access Review and Certification, and Privileged User Management.weight

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Platform baseline

neutral · staff-reviewed

RFIWhat data channels does the platform monitor and enforce policy on — endpoint (USB, clipboard, print), network/email, cloud/SaaS (CASB-integrated), and generative AI tools — and which require separate modules or licenses?
Answer key — what a strong answer shows
Look for a coverage matrix and clarity on which channels are native versus requiring an additional module.
RFPDescribe the content-detection methodology beyond simple regex/keyword matching (e.g., exact data matching/fingerprinting, machine-learning classifiers, contextual analysis) and provide a measured false-positive rate from a customer reference.
Answer key — what a strong answer shows
Strong answers name specific detection techniques beyond regex and give a real false-positive figure; regex-only DLP is notoriously noisy in production.
RFIHow does the platform handle unstructured/sensitive documents that don't match a known pattern (e.g., a proprietary design document, source code) — is there a training/fingerprinting workflow for customer-specific sensitive content?
Answer key — what a strong answer shows
Look for a described fingerprinting/exact-data-match training workflow for arbitrary customer content, not just built-in patterns for common data types.
RFPDetail the incident response workflow when a policy violation is detected — automated blocking versus alert-only — and what is the typical time from policy tuning to a production-ready ruleset with a customer reference.
Answer key — what a strong answer shows
Strong answers distinguish block vs. alert-only modes and state a real time-to-production-tuning figure, since over-aggressive blocking on day one is a common deployment failure.
RFIHow does the product handle encrypted or password-protected files and channels (e.g., encrypted email attachments, HTTPS-inspected traffic) — can policy be enforced without breaking encryption entirely?
Answer key — what a strong answer shows

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