Book
生成AIの安全性入門
綿岡晃輝
Summary
生成AIが引き起こす有害コンテンツ生成や情報漏洩、誤情報拡散といったリスクを分類した上で、 OpenAIのモデルスペックなど他組織のルールを参照しながら「AIの理想的な振る舞い」を検討する。 安全性を測るベンチマークやレッドチーミングによる評価手法、事前学習・SFT・RLHFなど モデルレベルでの安全性向上技術、ガードレールによるシステムレベルの対策までを扱い、 エージェントAIやロボティクスAI、AGIといった将来のリスクにも触れる。
Target Readers
- 生成AIモデルの安全性評価・レッドチーミングに携わるエンジニア・研究者
- 生成AIプロダクトのリスク管理・ガードレール設計を担当する実務者
Tags
Colophon
- Publisher
- 技術評論社
- ISBN
- 978-4-297-15702-9
- Published
- Jun 2026
- List price
- ¥3,300incl. taxMay differ from the actual selling price on Amazon
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Prerequisites
- Related
Webセキュリティ担当者のための脆弱性診断スタートガイド 第2版
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Reason: After gaining hands-on diagnostic experience with tools like OWASP ZAP and Burp Suite that closes the gap between theory and practice, turn that same posture — verifying defenses in the field — toward a new target. Generative-AI risk is evaluated through a different form, benchmarks and red-teaming, but the idea of backing up desk assumptions with verification carries over.
Sources
Next Books
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Reason: Once you've learned generative-AI risk classification, red-teaming-based safety evaluation, and system-level countermeasures like guardrails, you advance to embedding them into a mechanism that actually keeps running in production. LLMOps' closing chapter covers responding to security risks such as governance, privacy, and prompt injection, plus building an observability pipeline — connecting the evaluation and countermeasure knowledge from the AI safety primer into concrete production-operation mechanisms.
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Reason: After learning the techniques of safety evaluation via benchmarks and red-teaming, you broaden your view to how the act of 'evaluating' concretely gets built into your own generative-AI product. This book on generative-AI application evaluation covers everything from building an evaluation perspective model to confusion matrices, RAG-oriented metrics, security evaluation, and AI agent evaluation along the development lifecycle, connecting the abstract idea of safety evaluation to your own product's quality-assurance process.
Sources