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# 宣佈推出 Prisma Cloud AI-SPM：AI 支援的應用程式的安全性和合規性

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[Announcement](https://www.paloaltonetworks.com/blog/category/announcement-zh-hant/?lang=zh-hant&ts=markdown)  
[Accelerating Security with Precision AI](https://www.paloaltonetworks.com/blog/tag/accelerating-security-with-precision-ai-zh-hant/?lang=zh-hant&ts=markdown)

This post is also available in: [English (英語)](https://www2.paloaltonetworks.com/blog/cloud-security/ai-spm/ "Switch to 英語(English)") [日本語 (日語)](https://www2.paloaltonetworks.com/blog/cloud-security/prisma-cloud-ai-spm/?lang=ja "Switch to 日語(日本語)") [한국어 (韓語)](https://www2.paloaltonetworks.com/blog/cloud-security/prisma-cloud-ai-spm/?lang=ko "Switch to 韓語(한국어)")

由於[人工智慧](https://www.paloaltonetworks.com/cyberpedia/artificial-intelligence-ai) (AI) 無所不在，前所未見的安全挑戰也隨之而來。[AI 安全狀況管理 (AI-SPM)](https://www.paloaltonetworks.com/cyberpedia/ai-security-posture-management-aispm) 是解決這些挑戰的一組新功能，例如 AI 環境中的模型風險、數據暴露和潛在誤用等。

為了因應大規模部署 AI 和 Gen AI 的獨特挑戰，同時協助降低安全和合規性風險，我們很榮幸宣佈推出 [Prisma](https://www.paloaltonetworks.tw/prisma/cloud/ai-spm)®[Cloud AI-SPM](https://www.paloaltonetworks.tw/prisma/cloud/ai-spm)。

## 透過 AI-SPM 解決 AI 風險

AI-SPM 是一種新興的工具類別，能夠協助企業防範與 AI、ML 和 GenAI 模型相關的獨特風險，包括數據暴露、濫用和模型弱點。AI-SPM 採用[數據安全狀況管理 (DSPM)](https://www.paloaltonetworks.com/cyberpedia/what-is-dspm)、雲端安全狀況管理 (CSPM) 和雲端基礎結構權限管理 ([CIEM](https://www.paloaltonetworks.com/cyberpedia/what-is-ciem)) 等現有安全狀況管理方法中的元素，並加以調整以因應 AI 系統在生產環境中面臨的特定挑戰。Palo Alto Networks® 將此視為我們的程式碼到雲端™ 方法的延伸和整合部分。

AI-SPM 工具提供對 AI 模型生命週期 (從數據擷取、訓練到部署) 的可視性。透過分析模型行為、數據流和系統交互，AI-SPM 有助於識別傳統風險分析和偵測工具可能不容易發現的潛在安全和合規性風險。企業可以運用這些見解來執行政策和最佳實務，確保 AI 系統以安全且合規的方式部署。

此外，AI-SPM 可以監控特定於 AI 的威脅，例如數據污染、模型竊取和正確的輸出處理，向安全團隊發出潛在事件的警示並提供補救步驟的指引。隨著 AI 法規持續發展，AI-SPM 也可以透過將隱私和可接受的使用注意事項納入 AI 開發程序中，協助企業領先於合規性要求。

AI-SPM 的關鍵支柱是精準 AI，這是 Palo Alto Networks 專有的 AI 系統，透過使用豐富的數據和安全特定的模型來自動執行偵測、防禦和補救，協助安全團隊採信 AI 成果。Palo Alto Networks 的精準 AI 是功能強大的專有 AI 系統，有助於安全團隊逐漸採信 AI 成果。透過運用豐富的數據並運用安全特定的模型，精準 AI 能夠以極高的準確度自動執行偵測、防禦和補救程序。Palo Alto Networks 的創新技術是 AI 驅動的安全解決方案的支柱，可確保在當今複雜的威脅環境中簡化工作並獲得可靠的結果。

## AI-SPM 的關鍵功用

一如更廣泛的 AI 生態系統，AI 安全仍在持續發展。AI-SPM、數據遺失保護和數據安全之間的界限並非始終清晰可辨。不過，我們可以定義 AI-SPM 解決方案的一些基本功能。

### 1.AI 模型探索和庫存

**為何需要**：企業中的不同團隊可以部署託管、半託管和非託管模型。如此的情況會造成模型蔓延、未經授權的模型使用、缺乏監管和影子 AI 風險。安全團隊需要設法監督 AI 的部署，才能有效監控使用情況並防止下游風險。

**功能**：AI-SPM 工具能夠探索整個企業中已部署的模型、關聯資源 (包括運算、數據、應用程式) 的視覺化以及 AI 管道的點對點可視性。這有助於識別潛在風險、執行監管政策，並確保僅在生產環境中部署經過授權的模型。

**範例情境**：進行內部測試時，在虛擬機器上部署沒有內容防護措施的開放原始碼 LLM。隨著時間推移，該模型的使用變得普遍，包括敏感使用案例。AI-SPM 工具可以偵測到該模型正在運作以及哪些主體正在存取該模型。

### 2.數據暴露防範

**為何需要**：AI 模型 (特別是 LLM) 接受大量數據的訓練，這些數據可能包含敏感資訊、個人可識別資訊 (PII) 或其他受規範的內容。這可能是無意造成的結果，也可能是由於操縱訓練數據以將偏差或弱點引入模型的數據污染攻擊造成的結果。雖然使用第三方模型據稱將這種風險「外包」給模型供應商，不過，如果使用企業自己的數據進行微調或推理，這種風險可能會再次出現。

敏感數據成為模型的一部分後，就很難加以偵測 (回到「黑盒子」問題)，並且可能會透過常規模型回應或提示植入攻擊而暴露。

**功能**：AI-SPM 工具提供專為 AI 推理和訓練數據設計的數據探索和分類功能。這些工具可以識別暴露的數據資源，例如不安全的儲存空間陣列或數據庫，並針對潛在的數據洩露或未經授權的存取向安全團隊發出警示。AI-SPM 也可以監控數據流，藉以確保敏感資訊不會不慎被引入 AI 模型或透過模型輸出暴露。

**範例情境**：研究人員正在使用雲端儲存空間陣列來儲存新 AI 模型的訓練數據。AI-SPM 發現陣列包含客戶 PII。安全團隊收到警示後可以與研究人員合作對數據進行匿名化並確實遵循數據保護法規。

### 3.狀況和風險分析

**為何需要**：AI 系統相當複雜，通常涉及多個元件，例如數據管道、訓練環境和部署基礎結構。這些元件中的任何錯誤設定或薄弱存取控制都可能帶來重大的安全風險。如果沒有適當的保護，AI 模型也可能容易受到提示植入、擷取或拒絕服務攻擊。最後，遵循持續變化的法規需要對 AI 系統進行持續監控和評估。

**功能**：AI-SPM 工具分析 AI 系統的設定和存取控制，藉以識別潛在的風險和弱點。其中包括評估應用程式生態系統中的數據集、AI 部署和雲端資源所呈現的安全狀況。AI-SPM 也可以與 DSPM 工具整合，藉以強化有關敏感資訊流的脈絡數據的分析，進而對偵測到的弱點進行有效的優先順序排定。

**範例** **情境**：AI 開發團隊正在管理多個面向客戶的模型和內部模型。由於應用程式層級的錯誤設定，一個有權存取受保護的智慧財產權的內部聊天機器人向公眾開放。AI-SPM 偵測到模型的 API 端點不需要驗證即可公開存取，而且模型正在使用受保護的數據。相關開發團隊會收到警示，而且可以實施最低權限存取控制和驗證機制。
![AI-SPM 主要儀表板](https://www.paloaltonetworks.com/blog/wp-content/uploads/2024/05/word-image-319962-1.png)
AI-SPM 主要儀表板

## 開始使用 Prisma Cloud AI-SPM

保護生產中的 AI 安全是新的挑戰，而此時安全團隊已經不堪負荷，難以因應快速變化的威脅形勢。不過，由於 AI 對於企業展現巨大的價值，因此很少有安全領導者願意或能夠阻止 AI 的採用。

[Prisma](https://www.paloaltonetworks.tw/prisma/cloud/ai-spm)®[Cloud AI-SPM](https://www.paloaltonetworks.tw/prisma/cloud/ai-spm) 為企業採用新的 AI 模型和工具提供所需的可視性和控制。我們的程式碼到雲端平台可實現密切整合的解決方案，這套解決方案採用 Prisma Cloud 現有的 DSPM 和 CNAPP 功能，因此簡化安全工作、減少工具擴增，並且加速實現價值。

了解您為什麼需要 AI-SPM。[要求示範](https://www.paloaltonetworks.tw/prisma/request-a-prisma-cloud-trial)。

*** ** * ** ***

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