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      "title": "|official docs|Harm categories and severity levels|Microsoft Azure AI Content Safety|access 2026-10-05|",
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      "href": "https://docs.cloud.google.com/model-armor/overview|PRIMARY|Prompt/response",
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      "href": "https://docs.cloud.google.com/model-armor/release-notes|COUNTEREVIDENCE|SKIP_DETECTION",
      "supports": [],
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      "scope": "Evidence table",
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    {
      "id": "EVD-EA-239-URL-BBDE8911AF93",
      "evidence_class": "RESEARCH",
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      "supports": [],
      "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。",
      "origin_class": "SOURCE_PACKET_REFERENCE",
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      "supporting_locators": [
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        "|official docs|Amazon Bedrock Guardrails|AWS|access 2026-10-05|https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html|PRIMARY|Configurable"
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      "scope": "Evidence table",
      "provenance_status": "SOURCE_PACKET_REFERENCE_PROJECTED"
    },
    {
      "id": "EVD-EA-239-URL-95D4EDB9E82E",
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      "title": "|official docs|Guardrail components|AWS|access 2026-10-05|",
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      "supports": [],
      "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。",
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      "supports": [],
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      "id": "EVD-EA-239-URL-A43B1AEBF967",
      "evidence_class": "RESEARCH",
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      "href": "https://github.com/meta-llama/PurpleLlama/blob/main/Llama-Guard3/8B/MODEL_CARD.md|PRIMARY/COUNTEREVIDENCE|Input/output",
      "supports": [],
      "limit": "元の監査済み資料に記載された参照先です。URLの復元は、資料の最新性や全主張の支持を新たに検証したことを意味しません。",
      "origin_class": "SOURCE_PACKET_REFERENCE",
      "source_or_experiment_ids": [
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      "scope": "Evidence table",
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  ],
  "research_sections": [
    {
      "heading": "要点と前提",
      "text": "Content Safety／Moderation APIは、入力や出力を「安全／危険」と最終決定する認可機構ではありません。特定のモデル、taxonomy、threshold、対応modalitiesと言語の下で、ポリシー判断に使う分類信号を返す部品です。組織側では、この信号をどの条件でblock、allow、transform、human reviewへ接続するかを別に定義します。"
    },
    {
      "heading": "運用で固定する境界",
      "text": "- **taxonomy**: hate、violence、self-harm等のカテゴリ定義は製品・モデル間で同一ではありません。カテゴリ名だけを横断的な同義語として扱いません。\n- **modality**: text、image、audio、video、file、streamingのどれが実際に評価対象かを記録します。たとえばOpenAI `omni-moderation-latest` はtext/imageを扱いますがaudioは分類しません。\n- **language/domain**: 「多言語対応」を全言語同等精度と解釈しません。tested languageと対象業務domainでprecision/recall/FPR/FNRを分けて評価します。\n- **threshold**: vendor defaultを組織policyの閾値と同一視しません。false negativeだけでなく、医療・教育・歴史・法務・セキュリティ等のbenign hard negativeによるover-blockも測ります。\n- **version**: model/API revision、policy/taxonomy revision、threshold変更を監査記録へ残し、変更時に回帰評価します。"
    },
    {
      "heading": "`safe` と `not evaluated` を分離する",
      "text": "最重要の実装境界は、明示的な安全判定と「評価できなかった」を混同しないことです。timeout、rate limit、provider outage、oversized input、truncation、unsupported modality、SKIP_DETECTION等を独立状態として保持し、risk classごとにfail-open、fail-closed、manual reviewのどれへ送るかを決めます。\n\nOpenAIではmoderation model固有の障害実績があります。Google Cloud Model Armorでは入力条件により`SKIP_DETECTION`が返り得ます。したがってmoderation endpointを同期依存に置くだけでは安全設計になりません。"
    },
    {
      "heading": "分類とenforcementを分離する",
      "text": "moderatorの出力を受け取ったアプリケーションが実際のenforcement pointです。以下を別レイヤとして扱います。\n\n1. content safety分類\n2. prompt injection / jailbreak検出\n3. PII・機密情報検出\n4. grounding / factuality checks\n5. identity / authorization / tool permission\n6. business policy\n7. human review / appeal\n\nAmazon BedrockのAutomated Reasoning checksのように、あるguardrail機能がprompt injectionやstreamingを対象にしない例があります。「guardrail導入済み」を全リスクの包含証明にはできません。"
    },
    {
      "heading": "評価設計",
      "text": "本番投入前に、組織policyでラベル付けしたcorpusをcategory × language × modality × domainで分割します。少なくともprecision、recall、FPR、FNR、skip/error/timeout率をcategory別に測定し、benign hard negatives、境界事例、adversarial variantsを含めます。単一accuracy/F1やvendor benchmarkをtenant保証へ外挿しません。\n\nMeta Llama Guardのmodel cardは非ゼロのfalse-positive rateとmultilingual/tool-useでの性能差を示しています。独立研究でもmoderatorの順位・性能はdataset、language、category、borderline caseで変化します。したがって「最良moderator」を固定的に選ぶのではなく、対象policyに対する評価結果を変更管理の入力にします。"
    },
    {
      "heading": "ログとprivacy",
      "text": "moderation対象そのものが機密・個人情報・有害コンテンツである場合があります。AWSは、設定によってblocked contentがModel Invocation Logsへplain textで残り得ると明記しています。moderationログは安全監査の証拠である一方、二次漏えい面にもなります。payload保存の必要性、redaction、retention、RBAC、residency、削除、SIEMへの二次コピーを個別に設計します。"
    },
    {
      "heading": "失敗時の最低要件",
      "text": "- `SAFE / FLAGGED / NOT_EVALUATED / ERROR`を潰してboolean一値にしない。\n- unsupported modalityや未評価channelを「safe」に倒さない。\n- timeout・rate limit・outage時のfallbackをrisk class別に明示する。\n- threshold/model/taxonomy変更時に回帰評価する。\n- human reviewへ送る条件とqueue overload時の挙動を決める。\n- moderationログを無期限のraw prompt/response保管庫にしない。"
    },
    {
      "heading": "安全な公開表現",
      "text": "Content Safety／Moderation APIは、特定のモデル・taxonomy・threshold・入力modalitiesの下で得られる分類信号であり、組織ポリシーへの適合や安全性を単独で保証しません。対応言語・カテゴリ・入力形式、false positive/false negative、error/skip/timeout時の処理、ログへの残存を検証し、アプリケーション側のenforcementとhuman reviewを別途設計します。"
    },
    {
      "heading": "未確定事項",
      "text": "対象provider/model/version、組織taxonomy、日本語・業務domain別のprecision/recall、画像・音声・動画要件、threshold、streaming、長文、fail-open/fail-closed、rate limit/SLA、human review/appeal、logging/retention/RBAC/residency、法務・child-safety要件、tenant固有設定は未確認です。"
    },
    {
      "heading": "Evidence anchors",
      "text": "OpenAI Moderation guide / OpenAI 2024 multimodal moderation announcement and 2025 status incident / Azure AI Content Safety harm categories and severity / Google Cloud Model Armor overview and release notes / Amazon Bedrock Guardrails and Automated Reasoning limitations / Meta PurpleLlama Llama Guard 3 model card and issue #74 / GuardEval (arXiv:2601.03273) / PolyGuard (arXiv:2504.04377) / No One Model Catches Every Harm (arXiv:2608.21775)."
    }
  ],
  "human_url": "/tools/ea-239-content-safety-moderation-model-api",
  "markdown_url": "/agent/article.md?path=%2Ftools%2Fea-239-content-safety-moderation-model-api",
  "json_url": "/agent/article.json?path=%2Ftools%2Fea-239-content-safety-moderation-model-api"
}