社会ガイド

AIセキュリティ

AI security protects models, data, tools, and surrounding services from unauthorized access or manipulation.

2分の読書最終更新日 Part of the AI Policy & Society learning path

概要

It includes ordinary software security and threats that target learning or model behavior. A secure design begins with the assets, adversaries, and trust boundaries of the actual application.

主なポイント

  • Threat-model the full application.
  • Enforce permissions outside the model.
  • Retest controls across system changes.

ディープダイブ

Identify what needs protection: private inputs, training data, model artifacts, credentials, connected accounts, and external actions. Record who can influence each input and what an attacker could gain from a failure. A public chatbot and an internal agent with write access have different threat models. Threats can affect different stages. Poisoned training material can alter learned behavior; adversarial inputs can manipulate predictions; untrusted retrieved content can redirect a tool-using application. Model output can also become dangerous when inserted into a database query, webpage, or command without appropriate handling. Apply controls at the software boundary. Enforce authorization in code, keep secrets out of model-visible context where possible, restrict tool scope, and validate outputs before use. A prompt asking a model to behave safely cannot replace account isolation or permission checks. Test representative failure paths in an authorized environment and maintain an incident process. Log enough information to investigate without collecting unnecessary sensitive content. Evaluate controls after changes to the model, retrieval sources, tools, and dependencies. Describe residual risk honestly; no single filter establishes complete protection.

技術的な洞察

A model refusing one malicious prompt does not prove that a system is secure. Different inputs, tools, modalities, and component boundaries can create distinct failure paths.

Locate the security boundary

  1. Imagine an assistant searching a private document store for a signed-in user.
  2. Apply the user’s access filter in the retrieval service before documents enter the model context.
  3. Test with a document belonging to a different account and verify that neither its contents nor identifying metadata appear in the result.

This defensive, hypothetical test checks authorization independently of the model’s willingness to follow instructions.

戦略的影響

リスクと安全性

AI による壊滅的な被害も日常的な被害も、誰がリスクを理解し、誰が行動できるかにかかっています。

より明確な判決

国民と専門家のリテラシーは、強力な安全政策が政治的に可能かどうかを左右します。

誇大広告を打ち破る

明確な説明は、誇大広告、研究室の PR、曖昧な倫理劇場に囚われることを減らします。

現実世界の実装

Check that one account cannot retrieve another account’s documents.

Validate generated fields before using them in a database operation.

リスクとガードレール

能力が複雑になる一方で、実存的なリスクを SF として扱います。

高度な自律性の下での調整による表面製品の安全性を混乱させる。

英語以外や専門家ではない聴衆には、低品質の情報源しか提供されません。

実装ロードマップ

1

製品の危害、誤使用、制御不能/調整不良のリスクを分離します。

2

どのような証拠がタイムラインと重大度についてのあなたの見方を変えるかを尋ねてください。

3

マーケティング上の主張よりも、一次情報源と具体的な評価を優先します。

4

意識だけでなく、キャリア、政策、資金、スキルなど、行動経路を 1 つ特定します。

出典とさらなる参考文献

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AIの安全性

よくある質問

Is a strong system prompt enough to secure an assistant?

No. Authentication, authorization, input and output handling, tool limits, and incident response remain necessary parts of the application.