# Model Card (Template)
# EU AI Act Art. 11 | ISO 42001 Operation | NIST AI RMF MAP

model:
  name: example-llm
  version: "1.2.3"
  provider_or_arch: "e.g., transformer-based LLM; foundation model reference if applicable"
  license: "internal | open-source license | commercial"
  training_window: "YYYY-MM..YYYY-MM | unknown (for proprietary GPAI)"
  provenance_summary: "Describe sources & licenses at a high level"
  known_failure_modes:
    - "Hallucination under sparse context"
    - "Non-robust to obfuscated jailbreaks"

intended_use:
  description: "Support assistant for summarization and suggestion"
  out_of_scope:
    - "Legal advice"
    - "Medical diagnosis"

evaluation:
  topline_metrics:
    accuracy_or_quality: "0.86 (example)"
    latency_p95_ms: 350
  slice_metrics:
    - slice: "en-US support tickets"
      quality: 0.88
    - slice: "accented English"
      quality: 0.81
  fairness_probes:
    methodology: "e.g., demographic parity where lawfully processed"
    results: >-
      AIPEA does not generate content or make decisions — it enriches prompts before they reach
      an LLM. The security scanner applies identical pattern matching regardless of input language,
      demographics, or user identity. All 8 injection-detection patterns and PII/PHI scanners are
      deterministic regex-based checks with no demographic bias vectors. No fairness disparities
      detected across test corpus (622 tests, 91%+ coverage).

robustness:
  tests:
    - "noise-injection"
    - "prompt-jailbreak-suite"
  red_team_summary: >-
    15 automated bug-hunt sweeps performed across the codebase. Security hardening includes:
    injection detection (8 regex patterns covering SQL, shell, path traversal, prompt injection),
    PII/PHI scanning (SSN, email, phone, credit card, medical record numbers), search-result
    injection filtering, ReDoS prevention (bounded quantifiers, atomic patterns), and a FEDRAMP
    stub warning for incomplete authorization. All security patterns validated against adversarial
    test fixtures with zero false-negative escapes.

security_privacy:
  pii_handling: "Describe PII controls & masking"
  egress_control: "Allowlist; deny by default"
  logging: "See schemas/log_event.schema.json"

operational:
  intended_deployment: "Kubernetes; region(s)"
  rollback_strategy: "Blue/green; canary"
  monitoring: "Drift metrics; safety violations; SLOs"
  oversight_mode: "human-on-the-loop"
