admina.yaml.example

# Admina configuration
# Copy to admina.yaml and customize for your deployment.
# If admina.yaml is absent, Admina falls back to .env variables.
#
# schema_version is an incremental integer bumped only on breaking
# config-format changes; unrelated to the Admina product version.

schema_version: 1

domains:
  data_sovereignty:
    enabled: true
    pii:
      enabled: true
      categories: [email, phone, credit_card, ssn, iban, ip, person, org]
      ner_model: en_core_web_sm
    residency:
      enabled: true
      allowed_zones: [local, eu]
      block_outbound: true
    classification:
      enabled: true

  ai_infra:
    enabled: false              # opt-in
    llm:
      backend: ollama
      model: llama3.1:8b
      gpu_autodetect: true
    rag:
      backend: chromadb
      chunk_size: 512
      chunk_overlap: 50
    webui:
      enabled: true
      port: 3080

  agent_security:
    enabled: true
    proxy:
      port: 8080
      upstream: "http://localhost:9000"
    firewall:
      enabled: true
      heuristic_threshold: 0.7
      # Governance reaction mode (see ADMINA_GOVERNANCE_MODE):
      #   enforce  → block flagged requests (default, restrictive)
      #   observe  → never block, log "would have blocked" (use for the
      #              first 1-2 weeks of a new deployment)
      #   dry-run  → like observe + tag the response so downstream tools
      #              know the request was analysed
      mode: enforce
      # Categories you want SILENCED in this deployment (e.g. because
      # they trigger too many FPs in your domain). Builtin set:
      # instruction_override, role_hijack, prompt_extraction, jailbreak,
      # delimiter_injection, data_exfiltration, tool_abuse, obfuscation,
      # multilang_evasion. Stays empty = all categories active.
      disabled_categories: []
      # Add domain-specific rules here without forking. Each entry:
      #   - regex: a Python regex (case-insensitive)
      #     category: short label shown in dashboards/logs
      #     risk_level: low | medium | high | critical
      custom_patterns: []
      # Example:
      # custom_patterns:
      #   - regex: "delete\\s+user\\s+\\d+"
      #     category: "destructive_user_op"
      #     risk_level: high
      #   - regex: "(production|prod)\\s+(database|db)\\s+drop"
      #     category: "prod_db_destructive"
      #     risk_level: critical
    loop_breaker:
      enabled: true
      window_size: 10
      similarity_threshold: 0.85
      max_consecutive: 3
    # GuardrailsAI content validation (optional plugin)
    domains:
      guardrailsai:
        enabled: false            # opt-in: pip install admina[guardrailsai]
        inference_mode: local     # local | remote
        validators:
          - name: toxic_language
            threshold: 0.5
          - name: detect_pii
            entities: ["EMAIL_ADDRESS", "PHONE_NUMBER", "IBAN"]
          - name: detect_jailbreak
            threshold: 0.8

  compliance:
    enabled: true
    forensic:
      # backend: memory | filesystem | s3 | minio
      # Default is "memory" — events hashed and chained but LOST on
      # restart. Set explicitly to opt in to persistence.
      backend: memory
      bucket: forensic-blackbox
      # Required when backend=filesystem (no default — explicit opt-in)
      # base_dir: /var/lib/admina/forensic
      # Required when backend=s3 (boto3-based, works with any
      # S3-compatible store: AWS S3, R2, B2, SeaweedFS, Garage, …)
      # s3_endpoint: ""
      # s3_object_lock: false           # WORM (COMPLIANCE mode, 7y default)
    eu_ai_act:
      enabled: true
    nis2:
      # Self-assessment of the 10 Art. 21(2) measure areas.
      # Endpoint: GET /api/compliance/nis2/areas, POST /api/compliance/nis2/assess
      enabled: true
    gdpr:
      # Art. 30 RoPA registry + Art. 35 DPIA template scaffold.
      # By default the registry is in-memory only — set ropa_path
      # explicitly (or set ADMINA_GDPR_ROPA_PATH env var) to persist.
      enabled: true
      # ropa_path: /var/lib/admina/gdpr/ropa.json
    cross_regulation:
      # Constant matrix mapping operational controls across AI Act,
      # NIS2, GDPR. No configuration — exposed at GET /api/compliance/matrix.
      enabled: true
    otel:
      endpoint: "http://localhost:4317"

dashboard:
  enabled: true
  port: 3000

forensic_store: minio
auth_provider: apikey
pii_engine: spacy-regex
alert_channels:
  - type: log
  - type: webhook
    url: "https://hooks.example.com/admina"
    events: [HIGH, CRITICAL]

plugins: []

# ── Integrations ────────────────────────────────────────────
# External framework integrations. Each integration connects
# Admina's governance pipeline to a third-party ecosystem.

integrations:
  # OpenClaw agent governance skill
  openclaw:
    enabled: false              # opt-in
    proxy_port: 18790           # sidecar proxy port for OpenClaw agents
    # Install: cd integrations/openclaw/admina-governance && ./setup.sh

  # n8n workflow automation nodes
  n8n:
    enabled: false              # opt-in
    # Install: npm install n8n-nodes-admina in your n8n instance
    # Nodes: Admina Govern (inline), Admina Audit (passive), Admina Dashboard (trigger)

  # Cheshire Cat AI governance plugin
  cheshirecat:
    enabled: false              # opt-in
    proxy_port: 18790           # sidecar proxy port
    # Install: cd integrations/cheshirecat/admina-plugin && ./setup.sh
    # Hooks: agent_fast_reply, before_cat_sends_message, before_cat_recalls_memories

  # LangChain callback handler (in-process, no sidecar)
  langchain:
    enabled: false              # opt-in
    # Usage: ChatOpenAI(callbacks=[AdminaCallbackHandler()])
    # Governs: on_llm_start, on_llm_end, on_tool_start, on_tool_end

  # CrewAI step/task callbacks (in-process, no sidecar)
  crewai:
    enabled: false              # opt-in
    # Usage: Agent(step_callback=admina_step_callback)
    # Governs: each agent step (LLM reasoning, tool use, task output)