AI PCs are changing how endpoint security works—not just how devices perform. As organizations plan device refreshes, IT leaders and procurement teams need to evaluate how these systems handle AI workloads, where data is processed, and what that means for risk.
By enabling more AI workloads on the device, AI PCs can reduce reliance on the cloud and limit unnecessary data exposure. That can help organizations maintain tighter control over selected data flows and support faster user experiences. But it also puts more pressure on device-level protections, governance, and lifecycle management.
For organizations across industries, including those with strict compliance requirements, evaluating AI PCs means understanding how device architecture, hardware-based protections, and governance practices work together. This article outlines what makes AI PCs different from a security perspective, where new risks can emerge, and how to approach deployment with a stronger, more resilient endpoint strategy.
How AI PCs Change Endpoint Security
AI PCs combine a Central Processing Unit (CPU), Graphics Processing Unit (GPU), and Neural Processing Unit (NPU) to handle different types of workloads. The NPU is specifically designed to run certain AI tasks efficiently at the endpoint, which changes where and how data is processed.
With more local processing capability, organizations are no longer limited to cloud-only AI models. They can run some workloads locally, some in the cloud, or across both environments. While this creates more flexibility, it also changes the security model.
From a security standpoint, local AI processing reduces how much sensitive data is sent to external services for certain use cases and enables faster, on-device actions. At the same time, it increases the reliance on the endpoint. Because more sensitive processing may happen on the device, hardware protections, device compliance, and consistent security controls across endpoints become more important.
Why Hardware-based Security Matters More at the Endpoint
As AI workloads move closer to the endpoint, hardware-rooted security becomes more important. Security teams need strong protections below the operating system, not just within it.
This makes device architecture a critical factor in endpoint security. Microsoft Surface devices are designed as secure-by-design business devices, with models that meet the Secured-core PC standard. These devices integrate hardware, firmware, and Windows protections into a layered security approach.
Intel® platforms add hardware-based security and enterprise manageability features that support consistent control across endpoints. Features such as hardware-level threat detection, encryption acceleration, and secure boot support help strengthen the device foundation when properly enabled and managed.
When evaluating AI PCs, organizations should consider whether the device, operating system, and silicon work together to support:
- Secure boot and firmware protection
- TPM-backed security
- Encryption support
- Consistent policy enforcement
- Enterprise manageability across the device lifecycle
This layered approach—hardware, OS, and silicon working together—helps IT teams enforce security policies more consistently and reduce gaps between device protection and endpoint management.
Local and Cloud AI Create Different Risks
AI PC security also depends on where AI workloads run, because that determines where data is processed and risk is concentrated, and how organizations should manage risk.
When AI runs in the cloud, data typically leaves the device for processing. This supports larger-scale models and integrated workflows, but it also means organizations need strong controls for data sharing, external processing, and third-party access.
When AI runs locally, some processing stays on the endpoint instead of moving to an external service. This helps reduce unnecessary data exposure for certain tasks and support some governance or data-handling requirements. But it also places more responsibility on the device. Sensitive data remains on the endpoint, model files and outputs require protection, and security controls must be applied consistently across endpoints.
Most organizations will use a mix of both. Cloud AI will remain important for large-scale tasks and connected services, while local AI supports speed, privacy, and control for selected workloads. The goal is not to choose one model over the other—it is to secure both with clear policies and practical controls.
Applying Zero Trust to AI PCs
Zero Trust remains an effective framework for securing AI PCs because it focuses on verifying identity, securing the device, and protecting data access.
For AI PCs, the endpoint becomes a critical control point. If more AI processing happens on the device, then device trust, user trust, and data trust all carry more weight.
Applying Zero Trust to AI PCs requires enforcing controls across identity, device posture, and data protection. Key practices include:
- Enforcing multifactor authentication
- Checking device compliance before granting access
- Using conditional access based on device posture
- Applying data classification and protection policies
- Monitoring firmware, OS, and endpoint health
Microsoft Surface devices with Intel platforms support this model at the hardware level, giving IT teams a foundation for Zero Trust enforcement that starts before the OS loads. For organizations building modern workforce infrastructure, the alignment between identity, endpoint management, and security operations provides a stronger foundation for enforcing Zero Trust across endpoints.
AI PC Security Checklist
For organizations evaluating secure AI PCs, the following fundamentals should be in place:
Hardware
- Prioritize devices with hardware-rooted protections (e.g., Microsoft Surface Secured-core PCs)
- Enable supported Intel hardware security feature
Identity
- Require multifactor authentication
- Apply conditional access tied to compliance status
Data
- Define which AI tools are approved for use—and how to flag unauthorized or shadow AI activity
- Establish policies for what AI tools can access, store, and share
- Apply classification and protection policies
Device
- Keep firmware, drivers, and operating systems current
- Monitor endpoint health and compliance
AI Usage
- Publish acceptable-use policies that cover approved tools, data handling, and what to do when employees encounter unapproved AI workflows
- Train users on safe handling of prompts, files, and outputs
Why Device Choice Affects Security Outcomes
Not all AI PCs are built for the same enterprise requirements. Device selection should go beyond AI performance and focus on how well security, manageability, and standardization are supported across the environment.
A secure AI PC strategy should consider:
- Hardware-rooted security
- Windows security integration
- Enterprise manageability
- Fleet-wide standardization
- Lifecycle support and deployment readiness
Microsoft Surface devices powered by Intel platforms are built to meet these requirements. Surface delivers a secure-by-design foundation with Secured-core PC features and deep Windows integration. Intel contributes hardware-level protections and Intel vPro® manageability that give IT teams consistent control across deployments. Together, they help organizations reduce variability and strengthen endpoint security at scale.
Building a Secure Foundation for AI PCs
AI PCs can support both productivity and stronger endpoint security, but only when organizations deploy them intentionally. Local AI processing may reduce some external data exposure, but it also makes device-level protections, identity controls, and governance more important.
Organizations that take a structured approach, combining hardware-rooted security, Zero Trust principles, and consistent device standardization, are better positioned to manage risk while enabling AI-driven productivity.
Connection’s Endpoint Protection and Embedded AI Solutions resources offer additional guidance for teams planning a secure device refresh. To evaluate and deploy secure AI PCs with Microsoft Surface and Intel—aligned to your organization’s security and endpoint strategy—talk with a Connection specialist.
Doug Woodward
Doug Woodward is a Senior Product Manager at Connection, supporting Microsoft Surface and Microsoft hardware devices. Doug joined Connection in 2019 after spending 10 years at Apple, bringing more than 15 years of experience across various technology roles. He has a deep passion for technology and enjoys staying current with the latest solutions and innovations. Outside of work, Doug enjoys adventures with his wife and daughter and is an avid Volvo car enthusiast.
