You've deployed a DLP solution. Now you need policies that actually work.
Most organizations start with vendor defaults or copy-paste rules that trigger thousands of false positives. Within weeks, your team starts ignoring alerts, users find ways around restrictions, and your DLP investment becomes security theater.
This template provides a structured starting point for writing DLP policies that balance protection with operational reality. It's built around the three core DLP functions: discovery, protection, and investigation. Customize it for your environment, then iterate based on what you learn.
Purpose of This Template
This policy template helps you define how your DLP solution handles sensitive data across your organization. You'll specify:
- Which sensitive information types (SITs) to monitor
- Where discovery should scan (repositories, endpoints, cloud storage)
- What protective actions to take when policies match
- How to route alerts for investigation
The template focuses on personal data covered by GDPR, but you can adapt the structure for financial records, intellectual property, or compliance-specific data types.
Prerequisites
Before customizing this template, ensure you have:
- Classified data inventory: You've mapped where sensitive data resides (databases, file shares, SaaS apps, endpoints).
- Defined sensitive information types: Your DLP solution has SITs configured for the data you're protecting (social security numbers, credit card patterns, passport numbers, or custom patterns for your proprietary formats).
- Clear ownership: You know which teams own different data types and who approves exceptions.
- Integration points identified: You've connected your DLP to SIEM, ticketing, or other security tools for coordinated response.
If you're still discovering what data you have, run discovery in audit mode first. Don't enforce restrictions until you understand your baseline.
The Policy Template
policy_name: "GDPR Personal Data Protection - Production"
policy_id: "DLP-GDPR-001"
version: "1.2"
effective_date: "2024-01-15"
owner: "[email protected]"
# DISCOVERY SCOPE
discovery:
locations:
- cloud_storage: ["SharePoint", "OneDrive", "Google Drive"]
- endpoints: ["Windows", "macOS"]
- repositories: ["GitHub", "GitLab"]
- email: ["Exchange Online", "Gmail"]
sensitive_information_types:
- type: "EU_Passport_Number"
confidence: "High"
- type: "EU_National_Identification_Number"
confidence: "Medium"
- type: "Credit_Card_Number"
confidence: "High"
context_required: true # Require proximity to keywords like "CVV" or "expiration"
- type: "Custom_Employee_ID"
pattern: "EMP-[0-9]{6}"
confidence: "Medium"
# PROTECTION RULES
protection:
conditions:
- name: "Personal data shared externally"
trigger:
- action: ["email_send", "file_share", "upload"]
- destination: "external_domain"
- content_matches: "sensitive_information_types"
response:
primary: "block"
user_notification: true
notification_text: "This file contains personal data and cannot be shared outside the organization without approval. Contact [email protected] for data sharing requests."
allow_override: false
- name: "Personal data in public repository"
trigger:
- location: "public_repository"
- content_matches: "sensitive_information_types"
response:
primary: "alert"
severity: "critical"
auto_quarantine: true
notify: ["[email protected]", "repo-owner"]
- name: "Bulk personal data download"
trigger:
- action: "download"
- content_matches: "sensitive_information_types"
- volume: ">100 records"
response:
primary: "require_justification"
justification_required: true
approval_required: true
approvers: ["[email protected]"]
timeout: "4 hours"
# INVESTIGATION AND RESPONSE
investigation:
alert_routing:
- severity: "critical"
destination: "SIEM"
create_ticket: true
ticket_system: "Jira"
project: "SEC"
- severity: "high"
destination: "security_team_queue"
sla: "24 hours"
logging:
retention: "90 days"
include_content_snippets: false # Log metadata only, not actual content
log_destinations: ["CloudWatch", "Splunk"]
metrics:
- "policy_matches_per_day"
- "false_positive_rate"
- "user_override_requests"
- "time_to_resolution"
# EXCEPTIONS
exceptions:
- exception_id: "EXC-001"
description: "Finance team quarterly reporting"
scope: "user_group:finance_analysts"
allowed_action: "download"
expiration: "2024-12-31"
approved_by: "CFO"
- exception_id: "EXC-002"
description: "HR onboarding automation"
scope: "service_account:hr_automation"
allowed_action: "bulk_upload"
conditions: "destination:internal_hr_system"
permanent: true
review_date: "2024-06-30"
How to Customize It
Start with your highest-risk scenarios: Don't try to cover everything at once. Pick one sensitive information type and one high-risk action (like external sharing or public repository commits). Get that working before expanding.
Set confidence thresholds based on your false positive tolerance: High confidence means fewer false positives but potentially missed matches. Medium confidence casts a wider net. Test both in audit mode and compare results against a known sample of sensitive files.
Match responses to actual risk: Blocking external email with credit card numbers makes sense. Blocking an engineer who commits a test UUID that pattern-matches your employee ID format creates friction without security value. Use require_justification for medium-risk scenarios where context matters.
Define clear exception criteria: You'll need exceptions. Document why each exists, who approved it, and when it expires. Review quarterly. If an exception becomes permanent, your policy is probably wrong.
Integrate with your existing workflows: Route critical alerts to your SIEM. Create tickets automatically for high-severity matches. Don't build a separate investigation queue that nobody checks.
Customize notification language for your culture: Generic "policy violation" messages train users to ignore DLP. Explain why the restriction exists and what the user should do instead. Include a contact for legitimate data sharing requests.
Validation Steps
Run discovery in audit mode for two weeks: Collect baseline data on where your sensitive information types appear and how often they're accessed. Review the match quality. If you're getting matches on test data or false positives, refine your SIT patterns or add context requirements.
Test each protection rule with known scenarios: Create test files containing your sensitive information types. Attempt the actions your policy restricts (external email, public upload, bulk download). Verify that responses match what you defined. Check that user notifications are clear.
Verify integration points: Confirm alerts reach your SIEM. Check that tickets are created with the right severity and assigned to the correct queue. Validate that logs include the metadata you need for investigation without capturing actual sensitive content.
Measure false positive rate: For the first month, track what percentage of policy matches are legitimate issues versus false positives. If you're above 20% false positives, users will start ignoring alerts. Tune your confidence thresholds or add context requirements.
Review exception requests weekly: Track how many users request overrides and why. If you're seeing patterns (like the same team requesting exceptions repeatedly), your policy might be too restrictive for their legitimate workflow.
Monitor policy match trends: Set up dashboards for daily policy matches by type, location, and user. Sudden spikes indicate either an incident or a policy misconfiguration. Gradual increases might mean your data footprint is growing or users are finding workarounds.
Update this policy quarterly or whenever your data environment changes significantly. DLP effectiveness depends on keeping policies aligned with how your organization actually uses data.
