Although deploying artificial intelligence at the Edge is generally beneficial, organisations may unintentionally expose themselves to cyberthreats. Careful planning is necessary for mitigating common cybersecurity challenges.
The benefits of deploying AI at the Edge
Traditional AI deployments rely on Cloud-based service architecture. Since Cloud resources are not always readily available, delays and performance issues are likely. Edge computing offers a better alternative for time-sensitive applications.
Edge AI benefits from lower latency for accurate real-time analysis and faster decision-making. It also lowers bandwidth because devices process information locally instead of sending it to the cloud. Companies mitigate network congestion and lower data transfer costs this way, so this approach is ideal for endpoint-heavy environments.
Data privacy is another benefit of deploying AI at the Edge. Keeping sensitive information on-premises minimises exposure to cyberthreats. These advantages are why the global Edge AI market value was over $20.78 billion in 2024. If current trends continue, it will see a 21.7% compound annual growth rate through 2030.
A localised approach generally improves security because companies only transfer essential information instead of large volumes of raw data. However, misconfigurations and vulnerabilities can introduce risks.
4 cybersecurity challenges of Edge AI
Professionals can only secure their ecosystem after understanding the risks of deploying Edge AI.
- Increased attack surface
Edge ecosystems rely on many endpoints, exponentially expanding the attack surface. If one compromised device is not adequately protected against lateral movement, it could infect the rest, causing a cascade of issues.
- Lack of oversight
Remote monitoring and management are necessary since Edge environments are often deployed in faraway or hard-to-reach locations. Remote access software is a prime target for cyberattacks. Moreover, a lack of comprehensive visibility limits oversight, meaning infiltration may go unnoticed for weeks or months.
- Weak security protocols
The low-power hardware Edge AI runs on has only enough resources for core functions, so it will have nothing left to respond to cyberthreats unless professionals intervene. It may also have weak security protocols, making it susceptible to tampering.
- Model theft and tampering
If bad actors get hold of the model, they can reverse engineer it to uncover its purpose and weaknesses. They can use these insights to launch follow-up cyberattacks or build a copy, potentially causing reputational and financial damage. IBM’s 2025 Cost of a Data Breach report found 63% of United Kingdom businesses have not deployed AI access controls.
Addressing these cybersecurity challenges
Deploying AI at the Edge exposes security weaknesses and network vulnerabilities. AI compute security is essential for protecting sensitive information and model integrity. The zero-trust architecture is a good foundation. Strictly enforcing identity checks for all users and devices attempting to access the network may cause delays, but it will help protect Edge environments.
Other best practices include leveraging encryption for secure data transmission and regularly patching firmware, software and operating systems. Organisations should deliver trusted updates with signed code to prevent tampering.
After securing hardware, organisations can focus on the model. In federated learning, multiple devices train it concurrently without sending local data to a central server. This approach safeguards datasets and enhances performance. In one case study, it improved performance by over 27% on average. However, it requires significant computing power.
Differential privacy is an alternative option. It adds random noise to training datasets, making it difficult for attackers to identify specific data points within the model. It is particularly useful for algorithms shared across devices or users. However, the more noise there is, the harder it is for the AI to learn effectively. The trade-off for security is accuracy.
With watermarking, companies embed a unique signature within the model’s architecture to establish ownership. If a bad actor intercepts or modifies its output, the watermark will become apparent, revealing their tampering. They will also reveal themselves if they attempt to alter the algorithm itself. In response, professionals can fall back on version control.
Ensure security when deploying Edge AI
Addressing Edge AI security challenges involves strengthening model integrity, enhancing system reliability and optimising resource allocation. No matter the solution, coordination and communication are key.
About the author:
Zac Amos is the Features Editor at ReHack. With over four years of writing in the technology industry, his expertise includes cybersecurity, automation, and connected devices.
