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AWatch-rus/docs/dlp-production-plan-windows-10-19.md
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# Production DLP Plan for Windows 10-19
## Goal
Build a reliable, easy-to-deploy, and maintainable production DLP system on top of the existing `AWatch-rus` platform, with Windows hosts `10-19` as the primary target scope.
This plan assumes the current baseline already exists:
- Windows collectors for endpoint/browser/email/file telemetry
- ActivityWatch-based ingestion and Web UI overlays
- Ansible deployment for AW server and Windows endpoints
- InnoSetup-based Windows install kit
## Scope
In scope:
- Centralized DLP policy lifecycle
- Advanced practical content analysis for Russian personal data
- SIEM/SOAR exports and notifications
- Case management for investigations
- Compliance reporting
- Administrative tooling and health checks
Out of scope for this phase:
- Full enterprise RBAC/SoD model
- Multi-tenant administration
- Heavy ML/UEBA
- Approval workflows more complex than basic policy rollback/case review
## Delivery Principles
- Keep deployment simple: Python + SQLite + systemd on server, PowerShell on endpoints.
- Default to last-known-good behavior on every critical component.
- Do not break current Phase-1/2 DLP behavior while adding server-side controls.
- Prefer additive rollout behind feature flags and config toggles.
- Every new service must have Ansible deployment, health checks, logs, and rollback notes.
## Current Baseline and Gap
Current AWatch-rus DLP already provides:
- Rule-based endpoint detection
- Incident buckets and review UI
- Basic enforcement for clipboard/USB/print
- Email/file/browser collectors
- Initial reliability hardening work
Main gap to production DLP:
- Policies are still too endpoint-local
- Content analysis is not centralized or rich enough
- Incident export/investigation/reporting chain is incomplete
- Health/operations model is not yet unified
## Stage 1: Policy Engine
### Objective
Centralize policy management and distribution without breaking endpoint autonomy.
### Files
- `aw-server/dlp-policy-engine/policy_service.py`
- `aw-server/dlp-policy-engine/policy_schema.py`
- `aw-server/dlp-policy-engine/policy_storage.py`
- `aw-server/dlp-policy-engine/policy_distributor.py`
- `aw-server/dlp-policy-engine/requirements.txt`
- `aw-server/dlp-policy-engine/dlp-policy-engine.service`
- `ansible/roles/dlp-policy-engine/tasks/main.yml`
- `docs/dlp-policy-engine.md`
- `windows/dlp-policy-client.ps1`
### Server responsibilities
- REST API:
- `GET /api/0/dlp/policies`
- `POST /api/0/dlp/policies`
- `PUT /api/0/dlp/policies/{id}`
- `DELETE /api/0/dlp/policies/{id}`
- `GET /api/0/dlp/policies/active`
- SQLite-backed storage with version history in `policy_versions`
- Validation through Pydantic and JSON schema before activation
- Backup and rollback of active policy versions
- Heartbeat-aware policy distribution model
### Endpoint changes
Update `windows/dlp-endpoint-signals-collector.ps1`:
- add `-PolicyMode` with values `local` and `server`
- in `server` mode pull active policy every 5 minutes
- cache last valid server policy locally
- fallback to local cached policy if server is unavailable
### Acceptance criteria
- Policy can be created, versioned, activated, and rolled back through API.
- Endpoints continue working during policy engine outage.
- Invalid policy cannot become active.
- Endpoint logs clearly show source of active policy: `local`, `server`, or `cached`.
### Main risks
- Breaking current local-policy-only flow
- Partial rollout where server mode is enabled before engine is healthy
- Policy drift between server and endpoints
### Risk controls
- Feature flag: `aw_dlp_policy_engine_enabled`
- Default endpoint mode remains `local` until validation is complete
- Store active policy checksum/version on both server and endpoint
## Stage 2: Advanced Content Analysis
### Objective
Add practical, legally relevant detection quality without overengineering.
### 2.1 Dictionary packs for 152-FZ personal data
#### Files
- `aw-server/dlp-content-analysis/dictionaries/152-fz-pdn.json`
- `aw-server/dlp-content-analysis/checksum_validator.py`
- `aw-server/dlp-content-analysis/dictionary_matcher.py`
#### Required capabilities
- Detect:
- INN
- SNILS
- Russian passport patterns
- Validate checksums where applicable to reduce false positives
### 2.2 Regex packs
#### Files
- `aw-server/dlp-content-analysis/regex-packs/financial.json`
- `aw-server/dlp-content-analysis/regex-packs/contacts.json`
- `aw-server/dlp-content-analysis/regex-packs/secrets.json`
#### Required capabilities
- Reusable grouped pattern packs
- Server-defined matching rules distributed via policy
- Match metadata attached to incidents
### 2.3 OCR for screenshots
#### Files
- `aw-server/dlp-content-analysis/ocr_processor.py`
- `aw-server/dlp-content-analysis/requirements.txt`
- `ansible/roles/dlp-content-analysis/tasks/main.yml`
#### Required capabilities
- Tesseract wrapper for screenshots in `incident_artifacts`
- OCR text sent through regex and dictionary pipeline
- OCR enrichment attached to the original incident
### 2.4 Endpoint integration
Update:
- `windows/dlp-policy.example.json`
- `windows/dlp-endpoint-signals-collector.ps1`
Add policy fields:
- `dictionaryPack`
- `regexPack`
- `ocrEnabled`
Endpoint behavior:
- load server-delivered dictionary/regex references
- perform checksum-aware validation for supported PII
- upload screenshots for OCR when enabled by policy
### Acceptance criteria
- Server can enrich incidents with dictionary, regex, and OCR findings.
- False positives are reduced through checksum validation.
- OCR can be disabled per policy without code changes.
- Screenshot upload path is explicit and logged.
### Main risks
- OCR cost and latency
- Privacy overreach from over-collecting screenshots
- Regex pack sprawl and poor maintainability
### Risk controls
- OCR disabled by default
- Artifact retention policy documented
- Pack ownership and naming convention enforced
## Stage 3: SIEM and SOAR Integrations
### Objective
Make DLP incidents operational outside the AW UI.
### 3.1 CEF exporter
#### Files
- `aw-server/dlp-integrations/cef_exporter.py`
- `aw-server/dlp-integrations/cef-config.yaml`
- `aw-server/dlp-integrations/cef-exporter.service`
- `aw-server/dlp-integrations/cef-exporter.timer`
#### Required capabilities
- Read normalized incidents from SQLite/PostgreSQL
- Convert incidents to CEF
- Send via syslog
- Map DLP severities to CEF severities
### 3.2 Webhook notifications
#### Files
- `aw-server/dlp-integrations/webhook_sender.py`
- `aw-server/dlp-integrations/webhook-config.yaml`
#### Required capabilities
- Notify on `severity=high`
- Retry with backoff
- Include incident details, source host, user, rule, and evidence link
### 3.3 Ansible integration
Update `ansible/deploy_aw_server.yml`:
- install Python dependencies
- deploy configs/services/timers
- manage enable/start state
### Acceptance criteria
- High-severity incidents can be exported to SIEM and webhook endpoints.
- Export failures are visible and retry safely.
- Timers/services are idempotently managed by Ansible.
### Main risks
- Duplicate exports
- Alert fatigue
- Silent delivery failure to external systems
### Risk controls
- Event ID based dedupe
- Severity thresholding
- Delivery logs and health checks
## Stage 4: Case Management
### Objective
Provide a practical investigation workflow without introducing a heavy IR platform.
### Files
- `aw-server/dlp-case-management/case_service.py`
- `aw-server/dlp-case-management/case_schema.py`
- `aw-server/dlp-case-management/case_storage.py`
- `aw-server/dlp-case-management/case-service.service`
- `install-kit-awindows-20260427-211240/aw-server/aw-case-management-ui.js`
### Required capabilities
- Create a case from an incident
- Attach evidence links
- Support statuses:
- `open`
- `investigating`
- `resolved`
- `closed`
- Support comments
- Maintain immutable audit records in `case_audit`
### UI integration
Update `install-kit-awindows-20260427-211240/aw-server/aw-ru-patch.js`:
- add `Create case` action in DLP review table
- add `Case Management` section in UI
- show linked cases on incident views
### Acceptance criteria
- An operator can create and track a case directly from a DLP incident.
- Evidence remains linked after status transitions.
- Case audit trail is append-only.
### Main risks
- UI debt in current patch overlay
- Weak evidence chain semantics
- Mixing incident review and case workflow logic
### Risk controls
- Keep case service isolated from core AW server
- Use immutable audit table
- Treat evidence as links/references first, not copied blobs
## Stage 5: Compliance Reporting
### Objective
Generate regular compliance-grade reporting for Russian personal data handling.
### Files
- `aw-server/dlp-compliance/report_generator.py`
- `aw-server/dlp-compliance/templates/152-fz-report.html`
- `aw-server/dlp-compliance/report-scheduler.service`
- `aw-server/dlp-compliance/report-scheduler.timer`
### Required capabilities
- Period incident report
- Leak-channel statistics
- User statistics
- PDF export via `weasyprint`
- Scheduled email delivery
### Acceptance criteria
- Monthly report can be generated unattended.
- Report includes traceable source metrics.
- Output is usable by operations/compliance without manual cleanup.
### Main risks
- Weak data quality in upstream incidents
- PDF rendering dependency issues
- Email delivery failures
### Risk controls
- Validate report inputs before generation
- Keep HTML template under version control
- Add health check for scheduler and last successful report
## Stage 6: Administrative Tooling
### Objective
Make the whole stack operable without manual database edits or ad hoc scripts.
### Files
- `scripts/dlp-admin-cli.py`
- `scripts/dlp-health-check.py`
### Required CLI functions
- `python3 dlp-admin-cli.py policies list`
- `python3 dlp-admin-cli.py policies push --host HOSTNAME`
- `python3 dlp-admin-cli.py incidents list --severity high`
- `python3 dlp-admin-cli.py cases create --incident-id ID`
- `python3 dlp-admin-cli.py health check`
### Health checks
- API endpoint availability
- endpoint reachability and policy sync state
- queue health if implemented
- disk space
- systemd service state
### Acceptance criteria
- Operator can inspect policy, incident, case, and service health from CLI.
- Health check has machine-readable exit status.
- All critical services are covered by a single operational runbook.
## Cross-Cutting Ansible Work
Update:
- `ansible/deploy_aw_server.yml`
- `ansible/group_vars/all.example.yml`
- `ansible/roles/dlp-policy-engine/tasks/main.yml`
- `ansible/roles/dlp-content-analysis/tasks/main.yml`
Required variables:
- `aw_dlp_policy_engine_enabled: true`
- `aw_dlp_policy_engine_port: 5601`
Ansible quality bar:
- idempotent
- rollback-aware
- systemd-managed
- config templated, not hand-edited in prod
## Execution Order
1. Stage 1: Policy Engine
2. Stage 2: Advanced Content Analysis
3. Stage 6: Administrative Tooling
4. Stage 3: SIEM and SOAR Integrations
5. Stage 4: Case Management
6. Stage 5: Compliance Reporting
Rationale:
- centralized policies are the control plane
- content analysis increases signal quality
- admin tooling is needed before broadening operations
- integrations, cases, and reports depend on stable normalized incidents
## Release Strategy
### Wave 1
- Deploy policy engine on AW server
- Keep endpoints in `local` mode
- Validate API, versioning, rollback
### Wave 2
- Enable `server` policy mode for a pilot subset of Windows `10-19`
- Validate cache/fallback behavior
- Measure heartbeat and policy freshness
### Wave 3
- Roll out dictionary/regex/OCR selectively
- Enable SIEM/webhook export
- Stabilize case management and reporting
## Definition of Done
The plan is considered implemented only when:
- all new services are deployed by Ansible
- endpoint fallback works under server outage
- policy activation/rollback is proven
- content analysis is documented and testable
- SIEM/webhook integrations are observable
- case workflow is usable from UI
- monthly compliance report is generated automatically
- admin CLI and health checks replace ad hoc operational steps
## Deliverables Checklist
- [ ] Policy engine service and API
- [ ] Endpoint policy client and cache/fallback
- [ ] SQLite policy versioning and rollback
- [ ] Dictionary packs for 152-FZ
- [ ] Regex packs for financial/contact/secret data
- [ ] OCR processing pipeline
- [ ] CEF exporter
- [ ] Webhook sender
- [ ] Case management service and UI integration
- [ ] 152-FZ report generator and scheduler
- [ ] Administrative CLI
- [ ] Unified health check
- [ ] Ansible deployment coverage
- [ ] Operational documentation