external/anthropic-cybersecurity-skills/skills/implementing-endpoint-dlp-controls/SKILL.md
Implements endpoint Data Loss Prevention (DLP) controls to detect and prevent sensitive data exfiltration through email, USB, cloud storage, and printing. Use when deploying DLP agents, creating content inspection policies, or preventing unauthorized data movement from endpoints. Activates for requests involving DLP, data exfiltration prevention, content inspection, or sensitive data protection on endpoints.
npx skillsauth add seikaikyo/dash-skills implementing-endpoint-dlp-controlsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when:
Do not use for network DLP (inline proxy-based) or cloud-only DLP (CASB).
Microsoft Purview → Data Classification → Sensitive info types
Built-in SITs for common data:
- Credit card number (PCI)
- Social Security Number (PII)
- Health records (HIPAA)
- Passport number
- Bank account number
Custom SIT example (Employee ID):
Pattern: EMP-[0-9]{6}
Confidence: High
Keywords: "employee id", "emp id", "staff number"
Microsoft Purview → Data loss prevention → Policies → Create policy
Policy Configuration:
1. Template: Financial / Medical / PII (or custom)
2. Locations: Devices (endpoint DLP)
3. Conditions:
- Content contains: Credit card numbers (min 5 instances)
- OR Content contains: SSN (min 1 instance)
4. Actions:
- Block: Prevent copy to USB, cloud, email
- Audit: Log but allow (for initial deployment)
- Notify: Show user notification with policy tip
5. User notifications:
- "This file contains sensitive data and cannot be copied to this location"
- Allow override with business justification (optional)
Monitored endpoint activities:
- Upload to cloud service (OneDrive, Dropbox, Google Drive)
- Copy to removable media (USB drives)
- Copy to network share
- Print document
- Copy to clipboard
- Access by unallowed browser (non-managed browser)
- Access by unallowed app
- Copy to Remote Desktop session
For each activity, configure:
- Audit only (log the action)
- Block with override (user can justify and proceed)
- Block (prevent action entirely)
Deploy DLP policy in "Test mode with notifications" first:
1. Policy runs in audit mode for 2-4 weeks
2. Review DLP alerts in Activity Explorer
3. Identify false positives
4. Tune SIT patterns and conditions
5. Add exclusions for legitimate workflows
6. Switch to "Turn on the policy" (enforcement)
Purview → Data loss prevention → Activity explorer
Key metrics:
- DLP policy matches per day/week
- Top matched sensitive info types
- Top users triggering DLP
- Top activities blocked (USB, cloud, email)
- Override rate (percentage of blocks overridden)
DLP incident response:
1. Review DLP alert with matched content
2. Verify sensitivity of detected data
3. Assess intent (accidental vs. intentional)
4. If intentional exfiltration → escalate to security incident
5. If accidental → educate user, refine policy
| Term | Definition | |------|-----------| | DLP | Data Loss Prevention; technology that detects and prevents unauthorized transmission of sensitive data | | SIT | Sensitive Information Type; pattern matching rules for identifying sensitive data (regex, keywords, ML classifiers) | | Policy Tip | User-facing notification explaining why an action was blocked and how to request an override | | Content Inspection | Deep inspection of file contents to identify sensitive data patterns | | Exact Data Match (EDM) | DLP matching against a specific database of known sensitive values (exact SSNs, employee records) |
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.