What is an AI PC?

Leveraging AI to complete tasks has become a part of daily workflows in many industries, creating a need in the market for server-grade and consumer-grade AI hardware to keep up with demand. 

AI PCs combine consumer-grade PCs with emerging, specialized hardware intended for localized use of AI. Traditionally, PCs include a CPU for processing regular instructions and a GPU for running graphics operations. Though some modern PCs are AI-capable, the AI PC integrates cutting-edge hardware such as a neural processing unit (NPU) that is purposely built for AI tasks.

AI PCs can perform AI tasks locally instead of relying on the cloud, alleviating concerns regarding data residency or data privacy. The demand for AI PCs will only grow as AI tools become indispensable across industries, including cybersecurity. 

In this post, we’ll cover some use cases for AI PCs, how they are used in cybersecurity specifically, and various challenges associated with these new machines.

The architecture of an AI PC

Let’s begin with a brief overview of the main components of an AI PC.

NPU

The NPU component distinguishes the AI PC from a traditional PC. These units are optimized for AI workloads and are capable of performing data processing and computations in parallel while using much less power than their GPU equivalents.

CPU and GPU integration

Like other PCs, AI PCs execute instructions and provide graphics outputs to users. This means they have CPU and GPU components alongside an NPU. Even within machine learning (ML) applications, you still need traditional computing elements to display content with the GPU or manipulate memory in the machine with the CPU. The AI PC executes these instructions as part of an AI workflow that uses the NPU for inference or training.

AI PCs vs. traditional PCs

On the GPUs of traditional PCs, running AI workloads is energy inefficient. The NPU is far more energy efficient for executing AI tasks and still achieves either the same or better performance. Furthermore, AI PCs balance performance with efficiency, as they are optimized to hand off tasks appropriately among the CPU, GPU, and NPU.

Use cases for AI PCs in professional environments

Industry professionals often use tools such as ChatGPT or GitHub Copilot to perform tasks. However, AI PCs address the growing concerns about privacy and data sharing by executing tasks locally. In this section, we’ll explore some of the fields that can benefit from AI PCs.

Productivity and creativity

AI demonstrates powerful productivity functions when helping developers write code or automating repetitive tasks, such as when working with documents. Using AI tools increases a worker’s productivity and gives them more time to focus on essential tasks.

AI tools may also help with design. For example, by using text-to-image generation tools, you can quickly prototype and storyboard a design for your organization. You can also use these tools for quick touch-ups and enhancements of your work, empowering you to become your own creative pipeline.

Real-time data analysis

Data analysis historically relies on models, and these models are growing in sophistication. Now, you can use AI to summarize and index large volumes of data in moments. You can interact with a retrieval-augmented generation (RAG) system to get insights without turning data over in spreadsheets or notebooks.

Advanced software development 

When developing your own AI models, you normally rent space with a cloud provider to access very powerful — and expensive — hardware, such as an NVIDIA A100 or H100 Tensor Core GPU. However, with an AI PC, you can bring the model development, training, and testing to your own machine, giving you a faster feedback loop and keeping data as local as possible.

Executing AI workloads from a local machine also means you don’t need to send any of your data to an external system for processing or training. 

For example, an appropriate situation to keep your data local is processing HIPAA data that you can’t send to software as a service (SaaS) AI services. Another example is handling data that must stay within a geographical region to adhere to compliance and data residency requirements. Running the workload on an AI PC would protect a developer from uploading training data in a way that violates regulations.

AI PCs in cybersecurity

The AI PC is also making an impact on cybersecurity.

Reducing cloud reliance

AI systems are a natural part of an evolving cybersecurity strategy, generally operating in the background while monitoring a set of systems. 

However, relying on a set of cloud-based cybersecurity monitoring solutions requires transmitting sensitive data, impacting data residency during cybersecurity operations. AI PCs allow you to reduce your cloud reliance by keeping that monitoring and analysis local. 

Enhanced endpoint security

AI PCs enhance endpoint security, using AI models running locally to improve detection speed. Endpoint security traditionally relies on fixed rules and pattern matching. In contrast, AI systems continually train and retrain on relevant data and can infer if a new transmission is secure.

Enhanced threat detection

AI inference requires energy-inefficient hardware running in the cloud to perform cybersecurity operations. In contrast, the NPUs of AI PCs provide an efficient, low-energy, local way to detect malware, phishing, and advanced persistent threats. Advanced analysis of cybersecurity data is faster and more energy efficient using a local NPU.

The benefits of adopting AI PCs in cybersecurity

Adopting AI PCs in cybersecurity offers many benefits, including:

• Scalability: Each machine is responsible for its own security tasks instead of relying on an unbounded set of machines needing to receive and process information in the cloud. 
• Speed and efficiency enhancements: Performing these operations locally and in real time is highly beneficial, especially compared to their asynchronous, cloud-based counterparts. 
• Cost-effective AI implementation: Using an AI PC pushes the hardware cost upfront instead of relying on cloud hardware rentals to perform AI tasks.
• Improved performance with advanced analytics: AI PCs can provide advanced security analytics directly on the device, offering deep insights without consuming as many resources as traditional PCs.

Challenges and risks associated with AI PCs

Though running AI workloads locally is preferred, storing sensitive data locally instead of in the cloud introduces challenges. When using local data storage for model training or handling sensitive data, the local machine must then be protected against attacks seeking sensitive files from the file system. Additionally, this may introduce compliance challenges, especially if processing or using data on local machines violates regulations in any way.

These new AI workflows also give attackers new attack vectors. For example, malware can focus on exploits specific to AI systems, like using inference attacks or exploits that uniquely target the NPU and its interaction with the rest of the PC.

Furthermore, running an AI PC means there are additional components to maintain: the models and the software infrastructure that supports them. Keeping the models updated prevents model drift and maximizes detection efficacy (preventing false positives or false negatives).

CrowdStrike Protection

AI PCs offer a familiar CPU and GPU setup that is optimized for AI workloads with the addition of the NPU. These NPUs can make AI tasks possible locally, and they are more energy efficient and capable of higher throughput than a cloud-based AI system. These AI tasks help make us more productive, help automate repetitive tasks, and provide us with an easier interface to manage our data.

AI PCs also represent a step forward in cybersecurity by:

• Improving endpoint protection via local analysis
• Enhancing cybersecurity operations by increasing their speed and efficiency
• Allowing security teams to detect threats on their machines faster

CrowdStrike has a set of AI-native cybersecurity capabilities that you can use to empower your AI PCs and your traditional PCs with best-in-class security. Learn more about how CrowdStrike can help secure your organization’s AI-enabled devices.