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2834 - 强化 AI 管道:使用 IBM Guardium 实现端到端安全.pdf

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1、Orlando,FLOctober 69IBM TechXchange 20252834Fortifying AI Pipelines:End-to-End Security with IBM GuardiumDevan Shah,STSM,Chief Architect Data Security,IBMMatt Simons,Program Director Product Management,IBMFortifying AI Pipelines84%64%64%Enterprises are embracing but have concernsSource:“Enterprise g

2、enerative AI:State of the market”2023 IBM Institute of Business Value Study Of enterprises face significant pressure to accelerate generative AI initiativesSee cybersecurity risk as the#1 roadblock to generative AI adoptionIdentified security as the#1 priority for generative AI use casesIBM TechXcha

3、nge|2025 IBM Corporation3Maturity of adoptionAs the value of AI continues to grow,so will the risksIBM TechXchange|2025 IBM Corporation4Attackers will target AIAI should be treated as a new attack surface,with new detection and response strategies required for model evasion,extraction,inference and

4、poisoningPrompt injection can drop defenses preventing generation of unwanted material,plus access to exploitable integrations and a wealth of sensitive training dataMalicious models can be uploaded to open repositories,with hidden behavior triggered long after theyve been deployedIBM TechXchange|20

5、25 IBM Corporation5Some emerging threats look familiar while others are newNew world trendsIBM TechXchange|2025 IBM Corporation6Risks are growing every day7Training data risksTransparencyTransparency Lack of training data transparency-Amplified Uncertain data provenance-AmplifiedData lawsData laws D

6、ata usage restrictions-Traditional Data acquisition restrictions-Amplified Data transfer restrictions-TraditionalPrivacy Personal information in data-Traditional Data privacy rights alignment-Amplified Reidentification-TraditionalFairness Data bias-AmplifiedIntellectual property Data usage rights re

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根据《Data》标记中的内容,全文主要围绕如何加强人工智能(AI)管道的安全性展开。以下是关键点: 1. 企业对AI的采用日益增加,但同时也面临安全风险。 2. 84%的企业将安全视为AI采用的主要障碍,64%的企业将安全视为AI用例的首要优先事项。 3. AI管道面临的风险包括数据风险、推理风险、输出风险、非技术风险等。 4. 需要建立一个框架来保护AI管道,包括数据映射、数据活动监控、动态数据保护等。 5. 建立AI治理,确保模型、使用和数据的安全。 6. 需要实施安全测试、数据保护、运行时安全等策略。 7. 政府正在评估和制定AI法规,以应对AI带来的挑战。
你准备好了吗?" "AI时代,数据安全如何守护?" 构建可信AI生态圈!"
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