<p><strong>阿联酋迪拜</strong> - 思科强调组织在扩大人工智能应用的采用时应考虑的四个优先关注领域。该指导方针概述了安全团队如何将经过验证的应用安全实践适应于人工智能，帮助中东地区的组织管理新兴风险并维护数字信任。</p>
<p>随着人工智能在中东地区的广泛应用，包括政府、金融服务、能源和关键基础设施，首席信息安全官（CISO）和 IT 领导者面临着确保人工智能应用在整个生命周期内安全的压力，从它们所依赖的数据到它们部署的模型。</p>
<p><strong>人工智能应用安全的四个关注领域</strong> <strong>:</strong></p>
<ul>
<li>开源扫描：人工智能应用开发在很大程度上依赖于开源模型、公共数据集和第三方库等组件。这些依赖项可能包含漏洞或恶意插入，危及整个系统。</li>
<li>漏洞测试：人工智能应用的静态测试涉及验证人工智能应用的组件，包括二进制文件、数据集和模型，以识别后门或被污染的数据等漏洞。动态测试评估模型在生产中如何应对各种场景。算法红队测试可以模拟多种多样且广泛的对抗技术，而无需手动测试。</li>
<li>应用防火墙：生成性人工智能应用的出现催生了一类新的人工智能防火墙，专门针对大型语言模型（LLM）的独特安全风险。这些解决方案作为模型无关的保护措施，检查人工智能应用在传输中的流量，以识别和防止故障，并执行减轻威胁（如个人身份信息泄露、提示注入和拒绝服务（DoS）攻击）的政策。</li>
<li>数据丢失防护：人工智能的快速普及和自然语言内容的动态特性使传统的数据丢失防护（DLP）变得无效。相反，人工智能应用的 DLP 检查输入和输出，以防止敏感数据泄露。输入 DLP 可以限制文件上传、阻止复制粘贴功能或限制对未批准的人工智能工具的访问。输出 DLP 使用保护过滤器，确保模型响应不包含个人身份信息（PII）、知识产权或其他敏感数据。</li>
</ul>
<p>思科中东、非洲、土耳其、罗马尼亚和独联体网络安全总监 Fady Younes 评论道：“随着人工智能在该地区的快速采用，组织正在迅速从试点转向生产，这一转变改变了风险概况。确保人工智能应用的安全需要超越传统的应用控制，以保护整个人工智能生命周期，从为模型提供数据和第三方组件到这些模型在现实世界中的表现。通过以特定于人工智能的方式应用熟悉的安全原则，中东地区的组织可以自信地扩大创新，同时降低如提示注入和敏感数据泄露等风险。”</p>
<p>保护人工智能应用从开发到生产</p>
<p>在人工智能生命周期的几乎每个环节都存在风险，从采购供应链组件到开发和部署。上述安全措施有助于减轻不同的风险领域，每个措施在全面的人工智能安全策略中都发挥着重要作用。</p>
<p>请将您的新闻稿发送至 pressrelease.zawya@lseg.com</p>
<p>免责声明：本新闻稿的内容由外部第三方提供。本网站不对该外部内容负责，也不控制该内容。该内容以 “现状” 和 “可用” 基础提供，未经过任何编辑。本网站及我们的附属机构不保证本新闻稿中表达的观点或意见的准确性。</p>
<p>本新闻稿仅供信息参考。内容不提供税务、法律或投资建议，也不对任何特定证券、投资组合或投资策略的适用性、价值或盈利能力发表意见。本网站及我们的附属机构对内容中的任何错误或不准确之处，或您依赖该内容所采取的任何行动不承担任何责任。您明确同意，您使用本文中的信息风险自负。</p>
<p>在适用法律允许的最大范围内，本网站、其母公司、子公司、附属机构及各自的股东、董事、高级职员、员工、代理、广告商、内容提供者和许可方对您不承担任何直接、间接、后果性、特殊、附带、惩罚性或示范性损害赔偿，包括但不限于利润损失、储蓄损失和收入损失，无论是因过失、侵权、合同或任何其他责任理论，即使各方已被告知可能性或可以预见到任何此类损害。</p>

思科

<p>思科概述了在中东地区人工智能应用加速采用的背景下，确保这些应用安全的四项关键策略。这些策略包括开源扫描以识别漏洞、对人工智能组件进行漏洞测试、使用人工智能防火墙来降低风险，以及加强数据丢失防护措施。思科网络安全董事总经理 Fady Younes 强调，组织需要将传统安全实践调整到人工智能生命周期中，以有效管理新兴风险。重点是保护人工智能应用从开发到生产的全过程，确保各个行业的数字信任</p>

<p><strong>Dubai, United Arab Emirates</strong>- Cisco highlights four priority focus areas organizations should consider to secure AI applications as they scale adoption. The guidance outlines how security teams can adapt proven application security practices to AI, helping organizations across the Middle East manage emerging risks and maintain digital trust.</p>
<div class="lb-trans"><p><strong>阿联酋迪拜</strong> - 思科强调组织在扩大人工智能应用的采用时应考虑的四个优先关注领域。该指导方针概述了安全团队如何将经过验证的应用安全实践适应于人工智能，帮助中东地区的组织管理新兴风险并维护数字信任。</p>
</div><p>As AI adoption scales across the Middle East, including government, financial services, energy, and critical infrastructure, CISOs and IT leaders are under pressure to secure AI applications across the full lifecycle, from the data they rely on to the models they deploy.</p>
<div class="lb-trans"><p>随着人工智能在中东地区的广泛应用，包括政府、金融服务、能源和关键基础设施，首席信息安全官（CISO）和 IT 领导者面临着确保人工智能应用在整个生命周期内安全的压力，从它们所依赖的数据到它们部署的模型。</p>
</div><p><strong>Four focus areas for AI applications security</strong> <strong>:</strong></p>
<div class="lb-trans"><p><strong>人工智能应用安全的四个关注领域</strong> <strong>:</strong></p>
</div><ul>
<li>Open-source scanningAI application development relies heavily on components such as open-source models, public datasets, and third-party libraries. These dependencies can include vulnerabilities or malicious insertions that compromise the entire system.</li>
<li>Vulnerability testingStatic testing for AI applications involves validating the components of an AI application, including binaries, datasets, and models, to identify vulnerabilities like backdoors or poisoned data. Dynamic testing evaluates how a model responds across various scenarios in production. Algorithmic red-teaming can simulate a diverse and extensive set of adversarial techniques without requiring manual testing.</li>
<li>Application firewallsThe emergence of generative AI applications has given rise to a new class of AI firewalls designed around the unique safety and security risks of LLMs. These solutions serve as model-agnostic guardrails, examining AI application traffic in transit to identify and prevent failures and enforce policies that mitigate threats such as PII leakage, prompt injection, and denial of service (DoS) attacks.</li>
<li>Data loss preventionThe rapid proliferation of AI and the dynamic nature of natural language content makes traditional DLP ineffective. Instead, DLP for AI applications examines inputs and outputs to combat sensitive data leakage. Input DLP can restrict file uploads, block copy-paste functionalities, or restrict access to unapproved AI tools. Output DLP uses guardrail filters to help ensure model responses do not contain personally identifiable information (PII), intellectual property, or other sensitive data.</li>
</ul>
<div class="lb-trans"><ul>
<li>开源扫描：人工智能应用开发在很大程度上依赖于开源模型、公共数据集和第三方库等组件。这些依赖项可能包含漏洞或恶意插入，危及整个系统。</li>
<li>漏洞测试：人工智能应用的静态测试涉及验证人工智能应用的组件，包括二进制文件、数据集和模型，以识别后门或被污染的数据等漏洞。动态测试评估模型在生产中如何应对各种场景。算法红队测试可以模拟多种多样且广泛的对抗技术，而无需手动测试。</li>
<li>应用防火墙：生成性人工智能应用的出现催生了一类新的人工智能防火墙，专门针对大型语言模型（LLM）的独特安全风险。这些解决方案作为模型无关的保护措施，检查人工智能应用在传输中的流量，以识别和防止故障，并执行减轻威胁（如个人身份信息泄露、提示注入和拒绝服务（DoS）攻击）的政策。</li>
<li>数据丢失防护：人工智能的快速普及和自然语言内容的动态特性使传统的数据丢失防护（DLP）变得无效。相反，人工智能应用的 DLP 检查输入和输出，以防止敏感数据泄露。输入 DLP 可以限制文件上传、阻止复制粘贴功能或限制对未批准的人工智能工具的访问。输出 DLP 使用保护过滤器，确保模型响应不包含个人身份信息（PII）、知识产权或其他敏感数据。</li>
</ul>
</div><p>Fady Younes, Managing Director for Cybersecurity at Cisco Middle East, Africa, Türkiye, Romania and CIS, commented: “As AI adoption accelerates across the region organizations are moving quickly from pilots to production, and that shift changes the risk profile. Securing AI applications requires looking beyond traditional application controls to protect the full AI lifecycle, from the data and third-party components feeding models to how those models behave in real world use. By applying familiar security principles in AI specific ways, organizations in the Middle East can scale innovation with confidence while reducing risks such as prompt injection and sensitive data leakage.”</p>
<div class="lb-trans"><p>思科中东、非洲、土耳其、罗马尼亚和独联体网络安全总监 Fady Younes 评论道：“随着人工智能在该地区的快速采用，组织正在迅速从试点转向生产，这一转变改变了风险概况。确保人工智能应用的安全需要超越传统的应用控制，以保护整个人工智能生命周期，从为模型提供数据和第三方组件到这些模型在现实世界中的表现。通过以特定于人工智能的方式应用熟悉的安全原则，中东地区的组织可以自信地扩大创新，同时降低如提示注入和敏感数据泄露等风险。”</p>
</div><p>Protecting AI applications from development to production</p>
<div class="lb-trans"><p>保护人工智能应用从开发到生产</p>
</div><p>Risk exists at virtually every point in the AI lifecycle, from sourcing supply chain components through development and deployment. The security measures highlighted above help mitigate different risk areas and each plays an important role in a comprehensive AI security strategy.</p>
<div class="lb-trans"><p>在人工智能生命周期的几乎每个环节都存在风险，从采购供应链组件到开发和部署。上述安全措施有助于减轻不同的风险领域，每个措施在全面的人工智能安全策略中都发挥着重要作用。</p>
</div><p>Send us your press releases to pressrelease.zawya@lseg.com</p>
<div class="lb-trans"><p>请将您的新闻稿发送至 pressrelease.zawya@lseg.com</p>
</div><p>Disclaimer: The contents of this press release was provided from an external third party provider. This website is not responsible for, and does not control, such external content. This content is provided on an “as is” and “as available” basis and has not been edited in any way. Neither this website nor our affiliates guarantee the accuracy of or endorse the views or opinions expressed in this press release.</p>
<div class="lb-trans"><p>免责声明：本新闻稿的内容由外部第三方提供。本网站不对该外部内容负责，也不控制该内容。该内容以 “现状” 和 “可用” 基础提供，未经过任何编辑。本网站及我们的附属机构不保证本新闻稿中表达的观点或意见的准确性。</p>
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<div class="lb-trans"><p>本新闻稿仅供信息参考。内容不提供税务、法律或投资建议，也不对任何特定证券、投资组合或投资策略的适用性、价值或盈利能力发表意见。本网站及我们的附属机构对内容中的任何错误或不准确之处，或您依赖该内容所采取的任何行动不承担任何责任。您明确同意，您使用本文中的信息风险自负。</p>
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<div class="lb-trans"><p>在适用法律允许的最大范围内，本网站、其母公司、子公司、附属机构及各自的股东、董事、高级职员、员工、代理、广告商、内容提供者和许可方对您不承担任何直接、间接、后果性、特殊、附带、惩罚性或示范性损害赔偿，包括但不限于利润损失、储蓄损失和收入损失，无论是因过失、侵权、合同或任何其他责任理论，即使各方已被告知可能性或可以预见到任何此类损害。</p>
</div>

ZAWYA-PRESSR：思科在中东地区人工智能应用快速普及的背景下，推出了确保 AI 应用安全的关键策略