247 lines
12 KiB
Markdown
247 lines
12 KiB
Markdown
# AGENTS.md
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Operational rules for OpenCode/Codex agents in AWatch-rus.
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## Defaults
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- Rust is the primary runtime: use `adk-rust/`, build with `cargo build --release -p <crate>`, test with `cargo test -p <crate>`.
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- Root scripts (`check-aw-data.sh`, `check-aw-full.sh`, `scripts/prod_rollout.sh`, install-kit helpers) are Rust-first wrappers with legacy fallback.
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- Python is allowed only in `aw-server/dlp-content-analysis/`, `clickhouse-1c/ai/`, `clickhouse-1c/etl/`, `detmir-mcp/main.py`, `grafana-1c/`, `pfsense/`, `proxmox/tsj_guardian_bot.py`.
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- Never add real secrets from `secrets/`, private `.env`, or host credentials.
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- When auditing private/ignored files, report only path, secret type, and remediation. Never copy secret values into docs, logs, markdown, terminal summaries, commits, or handoff reports.
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## Required Checks
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- General: `scripts/quality-gate.sh`.
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- Rust: targeted `cargo test -p <crate>`.
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- Windows: parse PowerShell; CI also runs PSScriptAnalyzer on `windows/*.ps1`, `.psm1`, `.psd1`.
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- Ansible: affected `ansible-playbook --syntax-check ...`.
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## Map
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- `adk-rust/`: operational crates.
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- `aw-server/`: server install, env examples, RU WebUI patch, systemd.
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- `windows/`: RDP deployment, collectors, recovery, validation.
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- `ansible/`: deployment playbooks.
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- `proxmox/`: CT/gateway/bot automation.
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- `clickhouse-1c/`, `grafana-1c/`, `pfsense/`: integration stacks.
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- `grafana/`: flat version-controlled dashboard JSON; use Ansible to import/check it.
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## Entrypoints
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Use `proxmox/create-ct.sh`, `proxmox/push-aw-artifacts.sh`, `aw-server/install_aw_server.sh`, `aw-server/apply_webui_ru_patch.sh`, `windows/deploy-ensemble.ps1`, and docs in `docs/preparation.md`, `docs/deployment.md`, `docs/runbook.md`, `docs/operations.md`.
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## Incident Handling
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OpenCode must handle AWatch-rus incidents as evidence-based operational triage,
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not as guesswork from one red dashboard card.
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### Assessment Basis
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Assess every incident from these signals, in this order:
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- **User impact:** portal/report/dashboard unavailable, stale, slow, or wrong;
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which role is affected: executive, manager, security, forensics, admin.
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- **Data freshness:** ActivityWatch bucket `metadata.end`, collector heartbeats,
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Windows scheduled task recency, queue depth, and upload/send failure counters.
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- **Service health:** systemd failed units, active timers, bounded HTTP checks,
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`/health` or `/api/health` responses, container health where relevant.
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- **Pipeline layer:** identify the first broken layer in the chain
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`Windows/RDP collectors -> ActivityWatch buckets -> Rust services -> exporters
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-> Grafana/Portal -> ClickHouse/1C where configured`.
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- **Risk/evidence:** DLP endpoint signals, incident candidates, evidence
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artifacts, UEBA/risk narrative inputs, coverage gaps, and security
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correlation indicators.
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- **Blast radius:** one user/session/collector, one host, one service, one
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dashboard, or the full contour.
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- **Recoverability:** known rollback, stale-cache availability, safe restart
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boundary, and whether a human approval is required.
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Risk Narrative is only decision support. It can raise priority and explain
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why a manual check is needed, but it does not prove a policy violation, DLP
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incident, or SIEM finding by itself.
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### Severity
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Use this practical severity model:
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- `P0`: data loss risk, auth/security boundary broken, raw private service
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exposed, production report chain unavailable with no stale fallback, or
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repeated collector process storms/memory pressure.
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- `P1`: executive/security workflows degraded, fresh data missing for a critical
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host, DLP evidence sync broken, ClickHouse/1C ingest stopped, or portal health
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degraded with user-visible effect.
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- `P2`: one collector stale, one dashboard/panel wrong, delayed timer, bad label
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normalization, missing noncritical evidence, or recoverable stale report.
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- `P3`: documentation drift, cosmetic UI issue, non-production demo fixture,
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or a warning with fresh data still confirmed.
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Escalate severity when the same symptom repeats after recovery, when coverage
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is unknown, or when evidence contradicts dashboard status.
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### Mechanisms To Use
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Start with the repo wrappers before ad hoc probing:
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```bash
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cd /mnt/usb_hdd2/Projects/ActivityWatch-Russian
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./check-aw-data.sh
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./check-aw-full.sh
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```
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Then narrow by layer:
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- ActivityWatch API: `/api/0/info`, `/api/0/buckets`, bucket metadata and recent
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events with explicit `no_proxy` and short `curl --max-time`.
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- Worktime: `aw-worktime-api` `/health`, `/reports/worktime/today`,
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`/reports/worktime/management?allow_stale=1`, prewarm logs, stale-cache
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fields, `AW_WORKTIME_EVENTS_LIMIT`, and `aw_query_timeout_count`.
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- Windows/RDP: `validate-deployment.ps1`, exact `ActivityWatch Launch [...]`
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scheduled tasks, `ActivityWatch Recovery`, collector guard state, session
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collectors, local queue depth, and send failure counters.
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- DLP: `aw-dlp-policy-engine`, `aw-dlp-case-management`, `dlp-health-check`,
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`aw-dlp-endpoint-signals_<HOST>`, evidence artifact sync, policy audit, and
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case/compliance services.
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- Portal/Gateway/Grafana: `/portal/api/health`, `/api/reports`, gateway
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`/healthz`, protected `/d/...` Grafana routes, role gates, and browser smoke
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scripts.
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- ClickHouse/1C: only for file-1C/analytics incidents. Do not blame ClickHouse
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for worktime report failures unless the affected path explicitly uses it.
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Use existing guards and bounded mechanisms before broad restarts:
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- stale-cache and fail-closed worktime behavior;
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- `aw-worktime-autoheal`, `aw-worktime-prewarm`, `aw-worktime-ui-bridge`,
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`aw-rus-healthd` timers;
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- Windows collector guard and exact localized scheduled tasks;
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- DLP evidence sync and health timers;
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- targeted service restart only after evidence identifies the layer.
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### DLP Rule Update System
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Do not describe AWatch-rus DLP rules as manual local JSON entry, and do not
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collapse all DLP updates into one mechanism. There are two related but separate
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contours:
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1. policy lifecycle and endpoint synchronization through the DLP Policy Engine;
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2. automatic IOC/signature replenishment from the open-source Hayabusa/Sigma
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ruleset.
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The centralized policy update contour is:
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- Server service: `aw-dlp-policy-engine.service`, Rust binary
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`/usr/local/bin/aw-dlp-policy-engine-rust`, default API port `5601`.
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- Storage: SQLite DB from `AW_DLP_POLICY_ENGINE_DB_PATH`, with policy records,
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policy versions, active policy pointer, rollback versions, and `policy_audit`.
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- API contract:
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- `GET /healthz`;
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- CRUD: `/api/0/dlp/policies`;
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- active bundle: `GET /api/0/dlp/policies/active`;
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- active version/checksum: `GET /api/0/dlp/policies/active/version`;
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- approval lifecycle:
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`draft -> pending_approval -> approved -> deployed`;
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- workflow calls:
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`POST /submit`, `POST /approve`, `POST /draft`, `POST /activate`;
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- rollback: `POST /api/0/dlp/policies/rollback`;
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- audit:
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`GET /api/0/dlp/policies/audit?limit=N` and
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`GET /api/0/dlp/policies/{id}/audit?limit=N`;
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- endpoint sync:
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`POST /api/0/dlp/policies/agents/{agent_id}/heartbeat` and
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`GET /api/0/dlp/policies/agents/{agent_id}/desired`.
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- Windows side is configured for server-driven policy mode:
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`aw_windows_policy_mode: "server"`,
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`aw_windows_policy_engine_enabled: true`,
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`aw_windows_policy_refresh_seconds: 300`, and policy engine host/port from
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Ansible group vars.
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- Agents report their current policy version/checksum by heartbeat. The server
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compares it with the active deployed policy and returns `desired` with
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`refreshNow=true` when the endpoint must update.
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- `dlp-admin-cli` is the operator CLI for read-side checks such as
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`policies list`, `policies active`, incident/case listing, and combined DLP
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health checks. It is not a replacement for the lifecycle API when changing
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policy state.
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Automatic IOC/signature replenishment:
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- Name it precisely as `DLP IOC Enrichment from Hayabusa/Sigma` or
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`Hayabusa Sigma IOC refresh pipeline`.
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- Source rules come from the open-source GitHub ruleset
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`Yamato-Security/hayabusa-rules`, configured by
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`aw_dlp_ioc_rules_zip_url`.
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- Deployment is controlled by `ansible/deploy_aw_server.yml` when
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`aw_dlp_ioc_enabled=true`.
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- The refresh wrapper `/usr/local/bin/aw-dlp-ioc-refresh.sh` downloads the
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latest `hayabusa-rules` ZIP, unpacks Sigma YAML rules, and runs the Rust
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extractor `/usr/local/bin/aw-extract-ioc-from-sigma`.
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- The Rust extractor is built from
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`adk-rust/crates/extract-ioc-from-sigma`; local/manual builds use
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`scripts/build_dlp_ioc_from_hayabusa.sh`.
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- Extracted IOC-like values include process image suffixes, command-line
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substrings, original filenames, and SHA256 hashes. They are de-duplicated and
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emitted as `ioc_blacklist.json`, `ioc_blacklist.csv`, and
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`ioc_blacklist.sql`.
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- Production artifacts live under `/opt/activitywatch/dlp-ioc/output` and are
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served by `aw-worktime-api` on `/dlp-ioc/ioc_blacklist.json`,
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`/dlp-ioc/ioc_blacklist.csv`, and `/dlp-ioc/ioc_blacklist.sql`.
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- Windows DLP policy can consume this feed through the `ioc.source` field with
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format `hayabusa_sigma_v1`; endpoint health/heartbeat should expose loaded
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IOC state such as `iocRulesLoaded`.
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- Runtime automation is `aw-dlp-ioc-refresh.service` plus
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`aw-dlp-ioc-refresh.timer` with interval `aw_dlp_ioc_refresh_interval`
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(default `6h`). Health/diagnostics should check this timer before assuming
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signatures are static or manually maintained.
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- This Hayabusa/Sigma IOC pipeline enriches the DLP rule base automatically; it
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is not the same thing as hand-editing endpoint JSON and is also distinct from
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the server-side Hayabusa EVTX forensics runner.
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Operational meaning:
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1. To update rules, create or update a policy draft through the policy engine.
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2. Submit it for approval, approve it, then activate/deploy it. Activation is
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allowed only from `approved`.
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3. For policy changes, verify `active/version`, audit entries, Windows agent
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heartbeat/desired, and downstream DLP signals after endpoints refresh.
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4. For automatic signature replenishment, verify
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`aw-dlp-ioc-refresh.timer`, the last `aw-dlp-ioc-refresh.service` run,
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non-empty `ioc_blacklist.json/csv/sql`, Worktime API `/dlp-ioc/...`
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exports, and Windows IOC load counters.
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5. If a policy causes noise or misses, use policy rollback through the API; do
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not hand-edit endpoint policy files as the normal rollback path.
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Manual edits of `C:\Program Files\AWatch-rus\windows\dlp-policy.example.json`
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or `C:\ProgramData\AWatch-rus\dlp-policy.json` are diagnostic or emergency
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fallback only. If such an edit is unavoidable, document it as configuration
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drift and bring the rule back into the central policy engine.
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### Response Workflow
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1. Capture current state first: command, timestamp, host, service, and exact
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failing endpoint. Do not restart before collecting evidence unless the
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system is in active resource exhaustion.
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2. Find the first broken layer. If buckets are stale, fix collectors before
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Grafana. If `aw-worktime-api` is degraded, fix/report that before portal.
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3. Separate real outage from presentation drift: dashboards can be stale or
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mislabeled while buckets and services are healthy.
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4. Apply the narrowest safe recovery: restart a collector/task/service, reduce
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unsafe limits, clear process storms, or restore a known-good binary/config.
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Back up config/binaries before replacement.
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5. Verify with the same failing check plus one upstream and one downstream
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check. For collector incidents, require bucket freshness and guard/healthd
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consistency, not just one green command.
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6. Record closure evidence: root cause, affected layer, action taken, commands
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run, post-check results, remaining risk, and rollback path.
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### Safety Rules
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- Old snapshots, memory, dashboards, and handoff notes are hints; live runtime
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evidence wins.
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- Never expose passwords, tokens, private host credentials, private URLs, raw
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security events, or customer identifiers in incident writeups.
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- Do not run broad deploys, full restarts, or `cargo build --workspace` during
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incident triage unless the scope demands it and rollback is clear.
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- Do not treat `status=ok` as sufficient when freshness, queue depth, or
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coverage evidence says otherwise.
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- For owner-facing reports, publish only protected gateway/Grafana routes, not
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raw `:5600`, `:5610`, `:8720`, or ClickHouse endpoints.
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