Protect AI models, data and decisions throughout the product life cycle.
As AI moves from the cloud into vehicles, industrial systems, medical devices and connected products, security becomes a critical design requirement. AI models contain valuable intellectual property, process sensitive data and increasingly influence real-world decisions. Protecting them requires more than securing the model alone.
NXP combines hardware-rooted security, trusted execution and AI enablement software to help developers build secure, scalable and trustworthy edge AI systems.
Compared with centrally managed cloud environments, edge devices are often deployed in physically exposed environments where attackers may gain access to hardware, software, memory and deployed AI models.
Protecting edge AI requires security across hardware, software, models and updates throughout the entire product life cycle.
Discover how to protect AI models, data and decisions with a system-level approach to security from silicon to inference.
Protect AI models, data and intellectual property from unauthorized disclosure.
Ensure AI behavior cannot be altered through model tampering or malicious inputs.
Verify that only trusted software and approved AI models are deployed and executed.
To protect AI models, data and decisions at the edge, organizations need a defense-in-depth approach that combines multiple layers of protection throughout the system life cycle:
Together, these capabilities create a stronger security posture, helping organizations protect AI models and data, support privacy and compliance, and build trust in edge AI deployments.
NXP combines embedded security technologies, AI enablement software and responsible AI guidance to help customers build, deploy and scale trusted edge AI systems.
Protect devices, software and AI systems with hardware-rooted security technologies including secure boot, secure update, root of trust and secure enclaves.
Safeguard valuable AI models against unauthorized copying and strengthen ownership claims with technologies such as eIQ® Model Watermarking, helping protect AI investments throughout deployment and inference.
Develop, optimize and deploy machine learning and generative AI applications using the eIQ® AI development environment, combining AI enablement with NXP's proven embedded security technologies.
Support trustworthy AI development through NXP's Ethical AI Framework and Responsible Edge AI initiatives, helping address governance, transparency and responsible deployment considerations.
Together, these capabilities help organizations accelerate AI innovation while protecting models, data and systems throughout the product lifecycle.
Tools and software for developing, optimizing and deploying machine learning applications on NXP platforms.
End-to-end model development and deployment for machine learning applications on NXP EdgeVerse processors.
Help protect AI model intellectual property and demonstrate model ownership against unauthorized copying.
Scalable AI acceleration for edge AI workloads, integrated across NXP's microcontroller and applications processor portfolio.
Edge AI performance for vision, generative AI and agentic AI applications in a discrete, scalable form factor.
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NXP Ethical Framework for Artificial Intelligence Guidance for responsible AI development and deployment. |
Whitepaper |
Sep 8, 2026 |
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Responsible Edge AI Enablement Best practices for secure and responsible edge AI systems. |
External file |
Jan 1, 2026 |
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Deploy Generative AI to the Edge Faster Guide to accelerating GenAI deployment on NXP platforms. |
Whitepaper |
Oct 13, 2025 |
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Scaling i.MX Applications Processors with Discrete AI Accelerators Technical overview of scalable AI architectures. |
White Paper |
Jul 10, 2025 |
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View all our documents in our document library. |
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Edge AI security protects AI models, data, software and devices deployed on edge systems such as vehicles, industrial equipment, medical devices and connected products. It combines traditional cybersecurity with AI-specific protections to help prevent model theft, tampering, unauthorized updates, privacy breaches and manipulation of AI-driven decisions.
AI models often contain valuable intellectual property, process sensitive data and increasingly influence real-world decisions. If an attacker can steal, modify or manipulate an AI system, the consequences can range from loss of competitive advantage to safety, privacy and compliance risks. Edge AI security helps organizations protect their AI investments, maintain trust in AI-driven decisions and ensure systems operate as intended throughout their life cycle.
Processing data directly on the device can reduce the need to transmit sensitive information to external systems or cloud services. This can help improve privacy, reduce latency and simplify compliance requirements, while still enabling AI-driven functionality.
Common edge AI security risks include model theft, model tampering, adversarial manipulation, unauthorized software or model updates, and exposure of sensitive data. Protecting against these threats requires security across hardware, software, AI models and device life cycle management.
Traditional embedded security focuses on protecting devices, software and communications. AI systems introduce additional assets that must be protected, including AI models, training data and AI-driven decisions. This creates new security challenges such as model theft, model tampering, adversarial manipulation and protection of AI intellectual property.
Yes. AI models often represent valuable intellectual property and may be targeted by attackers seeking to copy, extract or reuse them. Protecting deployed models requires multiple layers of security, including secure storage, memory protection, trusted execution and secure life cycle management.
Secure boot and hardware roots of trust help ensure that only authenticated software and approved AI models can run on a device. This creates a trusted foundation for AI deployment and helps prevent attackers from replacing software or models with unauthorized versions.
AI model watermarking embeds identifiable ownership information into the inference process of an AI model, helping the owners demonstrate authorship and detect any reuse that is unauthorized.
Protecting AI model intellectual property requires multiple layers of security. Organizations should restrict access to models through secure boot, trusted execution environments, memory protection and secure update mechanisms, helping prevent unauthorized extraction, modification or deployment. Technologies such as AI model watermarking can further support ownership claims and help demonstrate authorship of a model.
NXP helps protect AI models throughout their life cycle by combining hardware-rooted security, trusted execution, secure boot, secure updates and memory protection technologies. Together, these capabilities help safeguard AI models against unauthorized access, tampering, extraction and deployment of untrusted models.
NXP supports secure AI deployments through a combination of EdgeLock® security technologies, the eIQ® AI software environment, Neutron and Ara NPUs, and selected i.MX, i.MX RT and MCX devices. Together, these technologies help customers build, deploy and protect AI applications from the edge device to the AI model.