[{"data":1,"prerenderedAt":1093},["ShallowReactive",2],{"/ja-jp/blog/sast-vs-llm-security-scanner":3,"navigation-ja-jp":328,"banner-ja-jp":760,"footer-ja-jp":768,"blog-post-authors-ja-jp-Meir Benayoun|Chris Widstrom":1022,"blog-related-posts-ja-jp-sast-vs-llm-security-scanner":1049,"blog-share-wrapper-ja-jp":1074,"next-steps-ja-jp":1084},{"id":4,"title":5,"authors":6,"body":9,"category":308,"date":309,"description":310,"extension":311,"externalUrl":312,"faq":312,"featured":313,"heroImage":314,"meta":315,"navigation":313,"path":316,"seo":317,"slug":320,"stem":321,"tags":322,"template":325,"updatedDate":326,"__hash__":327},"blogPosts/ja-jp/blog/sast-vs-llm-security-scanner.md","SASTとLLMセキュリティスキャナーはどう使い分けるか",[7,8],"Meir Benayoun","Chris Widstrom",{"type":10,"value":11,"toc":297},"minimark",[12,16,19,22,137,141,144,153,162,165,167,170,173,175,178,181,184,186,189,192,195,198,201,206,214,219,228,231,236,239,244,247,252,255,260,263,266],[13,14,15],"p",{},"すでに、似たような実験を試している方も多いのではないでしょうか。最先端のモデルにマージリクエストを読ませ、脆弱性スキャナーの役割を兼ねさせてみる。1件のマージリクエストであれば、これはかなりうまく機能します。モデルはコードを読み、そのコードが何を意図しているかを推論し、実在する問題を検出する。パターンベースのスキャナーが完全に見逃す問題を見つけることさえあります。",[13,17,18],{},"ここで次のように考えるのは、自然な流れです。1件のマージリクエストをこれだけ的確にレビューできるのなら、パイプライン全体でスキャナーを置き換えてもよいのではないか。しかし、そこで話が崩れます。エンタープライズ規模のコードベースで、すべてのコミットに対して最先端のモデルを主要なスキャナーとして動かす。この使い方は、1件のマージリクエストをレビューさせる場合と比べてコストが高く、挙動も予測しにくくなります。",[13,20,21],{},"プラットフォームチームやプロダクトチームでアプリケーションセキュリティを担当しているなら、そもそもこれは二者択一の問題ではありません。静的アプリケーションセキュリティテスト（SAST）かLLMスキャナーか、ではなく、どちらをどこで動かすか。この切り分けさえ正しく設計できれば、すべてのコミットで決定的なカバレッジを維持したまま、推論が本当に価値を発揮する場所にだけAIを追加できます。予測できない推論コストも、監査証跡の欠落も避けられる。両者を比較すると、次のようになります。",[23,24,25,41],"table",{},[26,27,28],"thead",{},[29,30,31,35,38],"tr",{},[32,33],"th",{"align":34},"left",[32,36,37],{"align":34},"決定的なスキャナー（SAST）",[32,39,40],{"align":34},"AIによるレビュー（LLM）",[42,43,44,66,83,100,120],"tbody",{},[29,45,46,50,61],{},[47,48,49],"td",{"align":34},"コスト",[47,51,52,56,57,60],{"align":34},[53,54,55],"strong",{},"優位"," ",[58,59],"br",{}," 1回のスキャンあたり1セント未満。固定的で予測しやすい。",[47,62,63,65],{"align":34},[58,64],{}," スキャンのたびに有料の推論呼び出しが発生。コストはコード量に応じて増え、予測が難しい。",[29,67,68,71,78],{},[47,69,70],{"align":34},"一貫性",[47,72,73,56,75,77],{"align":34},[53,74,55],{},[58,76],{}," 同じ脆弱性を、毎回同じように検出する。",[47,79,80,82],{"align":34},[58,81],{}," 同じコードでも、実行ごとに検出する脆弱性が変わったり、見逃したりする。",[29,84,85,88,95],{},[47,86,87],{"align":34},"監査証跡",[47,89,90,56,92,94],{"align":34},[53,91,55],{},[58,93],{}," 再現可能。毎回同じ固定の手法で実行され、CWEにマッピングされる。",[47,96,97,99],{"align":34},[58,98],{}," 実行ごとに出力が変わる可能性があり、単独では監査証跡として使いにくい。",[29,101,102,105,113],{},[47,103,104],{"align":34},"脆弱性の種類",[47,106,107,56,110,112],{"align":34},[53,108,109],{},"脆弱性の種類による",[58,111],{}," パターンベースで追跡可能な欠陥（インジェクションなど）",[47,114,115,56,117,119],{"align":34},[53,116,109],{},[58,118],{}," 意図に起因する欠陥や新種の欠陥。認可チェックの欠落、所有権ロジックの破綻、CVEやCWEにまだマッピングされていない問題など。イシューやエピック、ドキュメントの文脈を使って、ビジネスロジックを推論できる可能性がある。",[29,121,122,125,130],{},[47,123,124],{"align":34},"検出結果の検証",[47,126,127,129],{"align":34},[58,128],{}," 脆弱性の候補を検出するが、実際に悪用可能かどうかは確認できない。",[47,131,132,56,134,136],{"align":34},[53,133,55],{},[58,135],{}," 動作するエクスプロイトを生成して検出結果を検証するため、キューに届く誤検出が減る。",[138,139,140],"h2",{"id":140},"すべてのコミットをスキャンするコスト",[13,142,143],{},"優位：SAST",[13,145,146,152],{},[147,148,151],"a",{"href":149,"rel":150},"https://about.gitlab.com/blog/what-is-a-large-language-model-llm/",[],"LLM","に1件のマージリクエストをレビューさせるコストは、ごくわずかに見えるかもしれません。しかし同じモデルを、エンタープライズ規模のコードベースのすべてのコミットに向けた瞬間、計算が合わなくなります。スキャンのたびに、予測できない有料の推論呼び出しが発生する。そのコストは、コミットごと、1日ごとに積み上がっていきます。",[13,154,155,156,161],{},"一方",[147,157,160],{"href":158,"rel":159},"https://docs.gitlab.com/ja-jp/user/application_security/sast/",[],"SAST","は、同じ量のコードを1回あたり1セント未満で、毎回同じように、予測可能な形で処理します。コミットごとのスキャンを安価かつ予測可能に保てるため、セキュリティのカバレッジが予算と競合しません。この役割をLLMに担わせると、コストは使用量に応じて膨らみ、見積もりも難しくなります。",[138,163,164],{"id":164},"一貫性と監査証跡の作成",[13,166,143],{},[13,168,169],{},"同じコードをLLMに2回通すと、検出される脆弱性が異なる場合があります。これはモデルが間違えたからではなく、サンプリングに基づく推論の仕組みそのものによるもの。この不安定さは、スキャンが実施された事実を証明し、監査に耐える信頼性を示す必要がある場面で課題になります。",[13,171,172],{},"監査担当者は、スキャナーを再実行して結果が一致することを確認し、その網羅性と正確性を独立して検証します。決定的なスキャナーはルールベースであるため、一貫性が設計に組み込まれている。再実行すれば、常に同じ結果が得られます。",[138,174,104],{"id":104},[13,176,177],{},"優位：両方",[13,179,180],{},"静的スキャナーとLLMスキャナーでは、得意とする脆弱性のクラスが異なります。決定的なスキャナーが想定しているのは、インジェクションの脆弱性、ハードコードされたシークレット、安全でない暗号処理の呼び出しといった、パターンベースの脆弱性。固定されたルールに従うため、実行するたびに同じ問題を検出します。",[13,182,183],{},"LLMによるスキャンは、仕組みが異なります。読み取れるのは、コードの周辺にある文脈、つまりシステムが何をすべきかを説明したエピックやイシュー、ドキュメント。スキャナー側には、これを処理する手段がありません。この文脈があるからこそ、認可チェックの欠落や所有権の前提の破綻といった、ビジネスロジックの問題を検出できます。文脈が利用できる状況であれば、LLMは、パターンベースのスキャナーが構造上どうしても捉えられない問題まで拾い上げられます。",[138,185,124],{"id":124},[13,187,188],{},"優位：LLMスキャナー",[13,190,191],{},"決定的なスキャナーは候補を検出しますが、その検出結果が実際に悪用可能かどうかまでは判断できません。LLMは、もう一歩踏み込めます。欠陥を実際に誘発できるかどうかを試す簡易なテストを書くため、検出結果が本物かどうかが、キューに届く前に分かる。チームが誤検出の調査に時間を費やさずに済み、検出結果の品質に対するデベロッパーの信頼も高まります。",[138,193,194],{"id":194},"セキュリティプログラムは両方の併用へ向かう",[13,196,197],{},"セキュリティチームは、スキャナーの種類を一つに絞るのではなく、両方を動かすプログラムの構築へと向かっています。決定的なスキャンは、どんな場合でもすべてのコミットで実行されるベースライン。プログラムとして妥協できないカバレッジだからです。",[13,199,200],{},"その上に重ねる形で、LLMスキャナーが推論を要するチェックを担います。適用箇所は通常、すべてのコミットではなくマージリクエストの単位。決定的なスキャナーが構造上検出できない脆弱性を捉えられるのは、まさにこの領域です。両者を組み合わせれば、予測可能なコストでパターンを検出する決定的なスキャンと、ロジックの欠陥を推論で見つけ出すLLMスキャナーがそろい、網羅的なカバレッジが実現します。",[13,202,203],{},[53,204,205],{},"GitLabをまだお使いでない方へ",[13,207,208,209],{},"高度なSASTがすべてのコミットをスキャンし、GitLabのセキュリティ向けLLMスキャナーであるセキュリティレビューフローが、推論に基づくレビューを追加します。いずれも同じパイプラインの中で完結します。",[147,210,213],{"href":211,"rel":212},"https://about.gitlab.com/ja-jp/free-trial/?utm_medium=native&utm_source=integrate-market&utm_campaign=eg_global_cmp_content-syndication_security_en_",[],"GitLab Ultimateの無料トライアルを開始する",[13,215,216],{},[53,217,218],{},"すでにGitLab Ultimateをご利用の方へ",[13,220,221,222,227],{},"すでに実行しているSASTスキャンと並行して動かすには、",[147,223,226],{"href":224,"rel":225},"https://docs.gitlab.com/ja-jp/user/duo_agent_platform/flows/foundational_flows/security_review/",[],"セキュリティレビューフローを有効化","してください。",[138,229,230],{"id":230},"よくある質問",[13,232,233],{},[53,234,235],{},"AIはSASTを置き換えられるのか",[13,237,238],{},"唯一のスキャナーとしては、置き換えられません。AIによるレビューは実在する問題を検出できますが、同じコードでも実行ごとに検出する脆弱性が変わったり、見逃したりします。また、エンタープライズ規模ですべてのコミットにLLMを走らせると、継続的かつ決定的に動作するよう作られたスキャナーよりもコストが高く、見積もりも難しくなります。",[13,240,241],{},[53,242,243],{},"再現可能な監査証跡が、なぜコンプライアンスで重要なのか",[13,245,246],{},"SOC 2、PCI DSS、欧州連合のサイバーレジリエンス法といったセキュアコーディングのフレームワークは、再現可能で監査可能な証跡を求めています。スキャナーの結果とは、監査担当者が再実行して内容の一致を確認することで、独立して検証される情報。決定的なスキャナーは、この独立した確認を設計上サポートします。一方、確率的なモデルには、スキャンのたびに同じ結果になる保証がありません。",[13,248,249],{},[53,250,251],{},"AIによるレビューはSASTに検出できない何を見つけられるのか",[13,253,254],{},"AIによるレビューが捉えられるのは、ビジネスロジックの欠陥です。認可チェックの欠落、所有権の検証の破綻、そしてコードがどう書かれているかではなく、何をすべきかを理解していなければ分からないバグ。AIによるレビューは意図についての推論を可能にしますが、これはパターンベースのスキャンが想定していない領域です。",[13,256,257],{},[53,258,259],{},"セキュリティチームは両方を使うべきか",[13,261,262],{},"使うべきです。SASTとAIによるレビューは、検出を得意とする脆弱性の種類が異なります。SASTはすべてのコミットで継続的に実行し、AIによるレビューは、スキャナーが構造上検出できないビジネスロジックの脆弱性など、推論を要するチェックに充てるとよいでしょう。",[138,264,265],{"id":265},"関連リソース",[267,268,269,277,284,290],"ul",{},[270,271,272],"li",{},[147,273,276],{"href":274,"rel":275},"https://about.gitlab.com/ja-jp/blog/delivering-faster-and-smarter-scans-with-advanced-sast/",[],"高度なSASTでより高速かつスマートなスキャンを実現",[270,278,279],{},[147,280,283],{"href":281,"rel":282},"https://docs.gitlab.com/ja-jp/user/application_security/sast/gitlab_advanced_sast/",[],"GitLab高度なSASTのドキュメント",[270,285,286],{},[147,287,289],{"href":224,"rel":288},[],"GitLab Duoセキュリティレビューフローのドキュメント",[270,291,292],{},[147,293,296],{"href":294,"rel":295},"https://about.gitlab.com/ja-jp/blog/gitlab-duo-security-review-flow/",[],"GitLab Duoセキュリティレビューでスキャナーが見逃すロジックの欠陥を検出",{"title":298,"searchDepth":299,"depth":299,"links":300},"",2,[301,302,303,304,305,306,307],{"id":140,"depth":299,"text":140},{"id":164,"depth":299,"text":164},{"id":104,"depth":299,"text":104},{"id":124,"depth":299,"text":124},{"id":194,"depth":299,"text":194},{"id":230,"depth":299,"text":230},{"id":265,"depth":299,"text":265},"security","2026-09-16","AIによるレビューとSASTでは、検出できるバグの種類が異なり、それぞれに利点があります。どちらをどこで動かすかを判断する基準と、チームに両方が必要になるタイミングを、コスト・一貫性・監査証跡の観点から解説します。","md",null,true,"https://res.cloudinary.com/about-gitlab-com/image/upload/v1756989645/fojzxakmfdea6jfqjkrl.png",{},"/ja-jp/blog/sast-vs-llm-security-scanner",{"config":318,"title":5,"description":310},{"noIndex":319},false,"sast-vs-llm-security-scanner","ja-jp/blog/sast-vs-llm-security-scanner",[308,323,324],"features","product","BlogPost","2026-09-17","6-0RZPCVefgBl7BcBIy0t3-oFPoWDlOAGB8oAOePGuw",{"logo":329,"freeTrial":334,"sales":339,"login":344,"items":349,"search":679,"minimal":712,"duo":730,"switchNav":739,"pricingDeployment":750},{"config":330},{"href":331,"dataGaName":332,"dataGaLocation":333},"/ja-jp/","gitlab 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Shalem","transcend-dependency-firewall",{"title":1067,"description":1068,"heroImage":1069,"category":308,"date":1070,"authors":1071,"slug":1073,"externalUrl":312},"GitLab CISOが示すAI時代のソフトウェア防御3つのレイヤー","AIモデルの進化によって、脆弱性の発見と悪用はかつてないほど速く、安価になりました。エージェント型ソフトウェア開発を支える基盤もモデルとともに進化し、検出から検証済みの修正までの時間を絶えず短縮し続けなければなりません。GitLabのCISOが、そのために必要な3つのレイヤーと運用モデルを解説します。","https://res.cloudinary.com/about-gitlab-com/image/upload/v1759320418/xjmqcozxzt4frx0hori3.png","2026-09-18",[1072],"Chaim Mazal","securing-the-software-factory-at-machine-speed",{"header":1075,"text":1076,"button":1080,"disclaimer":1083},"無料の\n\n30日間のGitLabトライアルを開始\n",[1077,1078,1079],"GitLab Duo Agent Platformへのアクセス","GitLabの最も高度な機能を試す","AIで複雑なタスクを自動化する",{"text":335,"config":1081},{"href":1082,"dataGaName":338,"dataGaLocation":573},"https://gitlab.com/-/trials/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/ja-jp/blog/","クレジットカードなしでご登録いただけます。",{"header":1085,"blurb":1086,"button":1087,"secondaryButton":1091},"今すぐ開発をスピードアップ","DevSecOpsに特化したインテリジェントオーケストレーションプラットフォームで実現できることをご確認ください。\n",{"text":335,"config":1088},{"href":1089,"dataGaName":338,"dataGaLocation":1090},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/ja-jp/","feature",{"text":658,"config":1092},{"href":667,"dataGaName":343,"dataGaLocation":1090},1791952293118]