The rapid rise of on-device artificial intelligence (AI) is reshaping business operations and providing innovative solutions across various industries. However, alongside this technological advancement comes a heightened risk of cyber threats. As organizations strive to capitalize on AI at the endpoint, it becomes critical to implement robust security measures to mitigate potential vulnerabilities. This article dives into the unique security challenges posed by on-device AI and discusses key strategies to safeguard organizational assets effectively.
The Expanding Attack Surface of On-Device AI
Emerging technologies, including on-device AI, introduce a myriad of cybersecurity challenges. The key to reducing risk lies in understanding the intricacies of what requires protection and why. To illustrate, consider a network as a system of pipes responsible for transporting essential resources—like water or gas—within a commercial building. If these pipes are compromised, the critical resources they carry become tainted or obstructed, leading to operational failures. Translating this concept to AI, organizations need to be aware of:
- The infrastructure, including PCs and corporate networks, which serve as the foundational “pipes” for AI operations.
- The data, applications, and algorithms that flow through these systems, acting as the valuable “contents” necessary for successful AI implementations.
Cyber adversaries continuously target both the infrastructure and the data. They are motivated to steal intellectual property or corrupt datasets and AI models, which can severely disrupt business operations, leading to financial losses and reputational damage. Regulatory scrutiny may also arise as a consequence of such breaches.
Identifying Security Risks Associated with AI at the Endpoint
Understanding the specific methods attackers may deploy is essential for effective risk management in AI environments. Common attack vectors include:
- Device Compromise: Endpoint devices are frequent targets of cyberattacks. Supply chain attacks are especially alarming, involving tampering with hardware or firmware before devices even reach an organization. For instance, a hypothetical scenario could see an investment firm receive PCs embedded with counterfeit components, jeopardizing data integrity.
- Identity Compromise: Stolen or compromised credentials remain a growing threat vector. Attackers can infiltrate networks and remain undetected for extended periods, potentially putting sensitive data and models at significant risk. The rise of generative AI (GenAI) is enhancing phishing techniques, making these breaches particularly costly and difficult to manage. A recent report by IBM highlights that organizations face substantial financial repercussions when credentials are compromised.
- Insider Threat: Insider attacks can be particularly damaging. Research shows that malicious insider incidents have resulted in the highest average costs, estimated at $4.99 million per event. These threats could emerge from various sectors, including hardware, software, and the model supply chain.
Mitigating Risks Associated with On-Device AI
While the challenges posed by these attack vectors are not inherently new, the adoption of a proactive security framework is crucial. Organizations can reduce their attack surface by layering security measures and continuously monitoring for suspicious activity. Implementing a **zero-trust** approach—where trust is neither assumed nor permanent—can help organizations stay ahead of potential threats. The foundational principles of never trust, always verify, and continuous monitoring are essential for maintaining security across the fleet.
Consequently, it is vital to conduct a comprehensive reassessment of organizational infrastructure, particularly those systems and processes interacting with AI. Organizations should reflect on what countermeasures can be enforced to mitigate risks related to device compromise, identity compromise, and insider threats.
Foundational Security for On-Device AI Workloads
Effective security for AI workloads can be categorized into two primary segments:
- Below-the-OS Security: This level of defense is pivotal for protecting the AI devices utilized by an organization. Devices need to be secure by design, ensuring they are developed with security principles and maintained within secure supply chains. Furthermore, the use of hardware with built-in security measures can provide visibility and protect against unauthorized changes.
- Above-the-OS Security: This layer involves safeguarding access to AI models and the corporate networks where they operate. Implementing software security solutions, such as Dell’s partnerships with CrowdStrike Falcon XDR and Absolute Secure Access, can apply zero-trust principles to safeguard AI model supply chains. Granular access control mechanisms help prevent unauthorized access to sensitive data and models.
This comprehensive security framework, addressing both above and below the operating system, forms the basis of effective protection for on-device AI implementations.
Secure AI at the Endpoint with Dell Trusted Workspace
The potential of AI holds immense promise for businesses; however, many enterprises find themselves unprepared to leverage its full capabilities effectively. According to the 2025 Absolute Resilience Risk Index, a significant portion of the PC population lacks the capacity to absorb new AI capabilities broadly.
Dell can help organizations integrate security features seamlessly into their AI deployment strategies through Dell Trusted Workspace. By upgrading to Dell Pro or Dell Pro Max systems, users can unlock security benefits while securing AI workloads with what has been deemed “the world’s most secure commercial AI PCs.” For more information on how Dell’s security specialists can assist, reach out directly through their contact channels.