<p>AI environments involve complex data pipelines, model-training infrastructure, APIs and third-party components, all of which introduce new security risks.</p>
<p>Modern security techniques– with and without AI — recognize that traditional trusted-network approaches are inadequate. AI systems ingest new data, interact with users and integrate with other platforms, creating multiple entry points for attackers. A zero-trust model with continuous verification, strict access controls and ongoing monitoring offers a practical framework for protecting AI systems without slowing innovation.</p>
<p>Read on to learn how to apply zero-trust principles to AI by securing data, models, workflows and people.</p>
<section class=”section main-article-chapter” data-menu-title=”AI security risks”>
<h2 class=”section-title”><i class=”icon” data-icon=”1″></i>AI security risks</h2>
<p>AI systems <a href=”https://www.techtarget.com/searchsecurity/feature/AI-powered-attacks-What-CISOSs-need-to-know-now”>create security challenges</a> that most traditional defenses do not address. Specific threats include the following:</p>
<ul class=”default-list”>
<li><a href=”https://www.techtarget.com/searchsecurity/tip/How-data-poisoning-attacks-work”>Data poisoning</a> manipulates the training data to alter the model’s behavior.</li>
<li>Model theft involves attackers extracting proprietary models through APIs or compromised infrastructure.</li>
<li><a href=”https://www.techtarget.com/searchsecurity/tip/Types-of-prompt-injection-attacks-and-how-they-work”>Prompt injection</a> and malicious inputs can include threat actors manipulating AI systems to reveal sensitive data or bypass safeguards.</li>
<li>AI supply chain risks occur when attackers exploit vulnerabilities in third-party data sets, models and libraries.</li>
<li>Sensitive data leakage involves confidential data exposed through AI outputs or logs.</li>
</ul>
<p>Because these risks affect every stage of the AI lifecycle, comprehensive security is essential.</p>
</section>
<section class=”section main-article-chapter” data-menu-title=”Building a zero-trust framework for AI”>
<h2 class=”section-title”><i class=”icon” data-icon=”1″></i>Building a zero-trust framework for AI</h2>
<p>To protect the entire AI lifecycle, it is essential to have an effective zero-trust framework that covers data ingestion, model training, model storage, deployment and inference, and ongoing monitoring.</p>
<p>To succeed, focus the framework on three key areas: securing AI data pipelines, protecting models and AI infrastructure and continuously monitoring AI workflows.</p>
<h3>Securing AI data pipelines</h3>
<p><a href=”https://www.techtarget.com/searchenterpriseai/tip/Tools-and-techniques-for-optimizing-AI-data-pipelines”>Data pipelines</a> are one of the most valuable — and vulnerable — parts of AI systems. Untrusted or manipulated data can compromise the entire AI system, so CISOs should prioritize pipeline security. Protect these data sets before they enter training or inference workflows by:</p>
<ul class=”default-list”>
<li>Verifying the origin and integrity of data sets.</li>
<li>Tracking data lineage and provenance.</li>
<li>Restricting who can access and modify data sets.</li>
<li>Implementing automated validation to detect anomalies or poisoning attempts.</li>
<li>Maintaining strict data set version control and access logs.</li>
</ul>
<h3>Protecting models and AI infrastructure</h3>
<p>AI models often represent significant intellectual property and operational value. Treat models as high-value assets. Protect models by:</p>
<ul class=”default-list”>
<li>Securing model registries with strong authentication.</li>
<li>Encrypting models at rest and in transit.</li>
<li>Limiting who can train, modify or deploy models.</li>
<li>Restricting access to inference APIs.</li>
<li>Implementing rate limits to reduce the risk of model extraction.</li>
</ul>
<p>Separating AI development, training and production environments can further reduce exposure and block attackers from <a href=”https://www.techtarget.com/searchsecurity/tip/Common-lateral-movement-techniques-and-how-to-prevent-them”>moving laterally</a> through the infrastructure.</p>
<p>The overall goal is to help prevent model theft, tampering and unauthorized use.</p>
<h3>Continuously monitoring AI workflows</h3>
<p>Zero trust requires continuous verification rather t
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