Edge AI Technology: A New Solution for Data Security

NGÀY ĐĂNG 18/09/2025

Admin

In the era of strong digitalization, when data becomes a valuable asset, the issue of security is increasingly put on top. Technology Edge AI – the combination of artificial intelligence (AI) and edge computing – is emerging as an advanced solution, helping to process and protect data at the endpoint, minimize the risk of information leakage and enhance real-time response capabilities. So what benefits does Edge AI bring and how is it applied in today's data security?

What is Edge AI and how does it work?

Edge AI stands for “Edge Artificial Intelligence” – a concept used to refer to the deployment of models edge AI on hardware devices at the edge of the network (edge devices), instead of relying on data centers or the cloud for processing. Edge AI allows endpoint devices such as cameras, phones, IoT sensors, etc. to process data right where it originates, while without having to send it back to the server.

How does Edge AI work?

According to technical documents published by Intel and NVIDIA, the Edge AI workflow typically includes the following steps:

1. Data collection at the edge device
   Devices such as cameras, sensors, or smartphones collect data from the environment (e.g., video, images, audio signals, sensor information).

2. Process local data using built-in AI models
   Data is processed directly using machine learning or deep learning models pre-installed on the device.
   This processing can be can be supported by chips such as Intel Movidius, Google Coral TPU, NVIDIA Jetson, or Apple Neural Engine.

3. Respond or take action on the spot
   Based on the results processing, the device can take action (like sending alerts, unlocking, automatically adjusting settings, etc.).

4. (Optional) Send summary data or results to the cloud for long-term storage or to train advanced AI models

What are the benefits of Edge AI for data security?

1. Enhanced data privacy

Local processing: Sensitive data is processed directly on edge devices, close to the source of the data. This reduces or eliminates the need to transmit raw data to the cloud or a centralized data center.

Reduced risk of leakage: When data never leaves the device, the risk of interception, theft, or leakage during transmission is significantly reduced. Only aggregated, encrypted information is Newly encrypted or non-sensitive data is sent to the cloud (if required).

Regulatory compliance: [Inference] On-premises data processing can help organizations easily comply with data protection and privacy regulations such as GDPR, HIPAA, etc., since personal data is processed on-premises. iˆn.

2. Minimize latency and respond instantly

Fast detection and response: Edge AI enables near-instant detection of security threats (e.g., intrusions, anomalous behavior, cyberattacks) and response. There is no latency from sending data to the cloud, processing, and get a response back.

Important for real-time applications: This is extremely important for applications that require quick response such as security surveillance systems, autonomous vehicles, industrial robots, where every millisecond counts in preventing incidents.

3. Minimize the attack surface

Distribute data and processing: Instead of centralizing all data and processing capabilities into a single point (the cloud), Edge AI distributes them to multiple edge devices. This reduces the "attack surface" that hackers can exploit.

Damage limitation: [Inference] Even if an edge device is compromised, the damage can be limited to the local scope of that device, without affecting the entire system.

4. Stable operation in poor or no connection environment Connectivity

Offline Capability: Edge AI devices can continue to operate and perform security tasks even when the internet connection is lost or unstable. This ensures security system continuity in remote, harsh environments or in the event of network disruption.

5. Save bandwidth and costs

Reduce network load: By processing data locally, Edge AI significantly reduces the amount of data that needs to be transmitted over the network. This saves bandwidth, reduces network congestion, and optimizes costs associated with data transmission and cloud storage.

Reduces storage costs: Only important or compressed/summarized data is sent to the cloud, reducing long-term storage costs.

6. Enhanced Scalability

Horizontal Scaling: [Inference] Deploying additional edge devices with AI capabilities can be done without without significantly upgrading the cloud infrastructure. This can enable flexible and cost-effective scaling of the security system.

Applications of Edge AI in Data Security

Edge AI has many important applications in the field of data security, taking advantage of local processing and instant response capabilities. Here are some typical applications:

1. Monitoring and Physical Security

• Detect intrusions and abnormal behavior: Security cameras equipped with Edge AI can analyze real-time video at the device to detect suspicious activities such as people entering restricted areas, violent behavior, or abandoned objects. This helps reduce the load on the cloud system and allows for faster response.

• Identify and Authentication: Facial or object recognition systems on edge devices (e.g., smart doorbells, access control systems) can identify authorized people or vehicles and alert them to unfamiliar ones. This helps to tightly control access to sensitive areas.

• Reduce false alarms: Edge AI can be trained to differentiate between intrusive and malicious (e.g., shaking trees, small animals) and real threats, significantly reducing the number of false alarms and improving the efficiency of security teams.

2. IoT (Internet of Things) Security

• Detect and prevent attacks on IoT devices: IoT devices are often resource-limited and vulnerable to attacks. Edge AI can monitor network traffic and IoT device behavior to detect attack patterns such as denial of service attacks (DDoS) or unauthorized access, then isolate the affected device.

• Access control and authentication: Edge AI can perform local user and device authentication, ensuring only authorized devices and users can access the IoT network and its data.

• Analyze sensor data to detect anomalies: In industrial or smart home environments, Edge AI can can analyze data from hundreds or thousands of sensors to detect unusual activities (e.g., sudden temperature increases, unusual pressure changes) that may indicate an attack or security incident.

3. Industrial Security (OT/ICS)

• Monitoring and Threat Detection in Industrial Control Systems: Operational Technology (OT) and Industrial Control Systems (ICS) are Critical infrastructure is highly susceptible to cyberattacks. Edge AI can be deployed on edge devices in factory environments to monitor network traffic, data from PLCs (Programmable Logic Controllers) and other devices, detect abnormal behavior or unauthorized control commands.

• Protect against attacks on critical infrastructure: By processing data locally, Edge AI can can provide an additional layer of protection, helping to isolate and prevent widespread attacks in OT/ICS systems.

4. Securing Autonomous and Connected Vehicles

• Processing real-time sensor data for road safety: Autonomous and connected vehicles generate large amounts of data from cameras, radars, LiDAR. Edge AI processes this data instantly on-board to detect obstacles, pedestrians, traffic signs, and potential threats, allowing the vehicle to make safe decisions in a split second.

• Protect user data and on-board control systems: Edge AI helps keep sensitive driver and passenger data processed and stored locally, reducing the risk of theft or unauthorized access when transmitted over external networks.  It can also monitor control system activities to detect and prevent hacking attempts or unauthorized alterations.

5. Personal Data Security on Mobile and Wearable Devices

• Local biometric authentication: Security features such as fingerprint and facial recognition on smartphones are performed using Edge AI. The user's biometric data is processed and compare right on the device, without sending to the cloud, helping protect privacy.

• Detect malware and suspicious behavior: Edge AI on mobile devices can analyze application behavior, network traffic, and usage patterns to detect malware, malicious apps, or unusual behavior that indicates the device has been compromised. compromised.

• Health data security on wearables: Health trackers can use Edge AI to analyze heart rate, sleep, activity level data and detect abnormalities, while keeping this sensitive health data safe on the device.

Challenges and Prospects of Edge AI in Data Security

Challenges

Hardware limitations at the Edge device

• Edge devices (cameras, sensors, gateways...) often have limited resources in terms of CPU, RAM, storage, leading to difficulties &o; deploy large and complex AI models.

• Local processing can easily lead to overload or delays when executing advanced security algorithms.

Difficult to update and patch security flaws

• Since Edge devices are widely distributed and not always connected to the network, software updates and security patches can be can be delayed, creating the risk of long-term security vulnerabilities.

Vulnerability to physical attacks

• Edge AI is often deployed in the field (e.g., in factories, self-driving cars, public cameras, etc.), where the design vulnerable to physical access by malicious actors to tamper with hardware or extract data.

Managing complex cryptographic keys

• Protecting data with cryptography requires a management mechanism efficient keys. But with thousands of Edge devices, securely managing and distributing keys is a daunting task.

Model reliability AI Models

• AI models at the edge can be vulnerable to “adversarial” attacks (attack using noisy input data), leading to incorrect decision making, affecting data security.

Prospects

Minimize data leakage by handling on-premises

• Edge AI processes data on-device, reducing the amount of data sent to the cloud, thereby reduce the risk of theft or man-in-the-middle attacks.

Increase immediate response capabilities

• Thanks to on-site processing, security systems can can respond quickly to threats, regardless of network latency.

Personalization and on-device learning

• Some Edge AI systems have ability to learn from local data, creating security models that are more appropriate for each environment without sharing data centrally.

Combined with Blockchain or end-to-end integration

• Edge AI can integrate with blockchain to capture anomalous behavior, or combine it with matilde End-to-end encryption (E2EE) to enhance data security right from the point of origin.

Supports Zero Trust Model

• Edge AI fits into a Zero Trust Security strategy, where every device and user requires continuous authentication. Edge AI can be used to analyze behavior and make automatic authentication decisions.

Edge AI Camera

What is Edge AI Camera?

Edge AI Camera is a camera that is integrated with a processing chip AI (like VPU – Vision Processing Unit, or NPU – Neural Processing Unit) to perform tasks like:

• Facial recognition.

• People/vehicle counting.

• Analysis behavior.

• Intrusion detection.

• License plate recognition.

• Detect weapons, fires, abandoned objects, moving in the wrong direction...

All these things This happens directly inside the camera, without the need for server processing. center.

 Outstanding advantages of Edge AI camera

 Core technology integrated into the Edge AI camera

 Practical applications in various fields

 Which camera do you send Edge AI technology?

From the list you provided, the models with Edge AI technology (i-PRO AI series) include:

Yes Edge AI:

• WV-S8564L

• WV-S85402-V2L

• WV-S66600-Z3L / Z3LN

• WV-S65501-Z1G

• WV-S4576LA

• WV-S2536LNA / WV-S2536LA

• WV-S2136LA

• WV-S15700-V2L

• WV-S15501-Z3L / Z1L / V3L / V3LN / V3LK

• WV-S1536LA

 Future prospects of Edge AI cameras

• Integrated with 5G: Instant wireless data transmission with near-zero latency.

• Incorporate blockchain for authentication and video data tracking.

• AutoML (AutoML) right on the camera – the camera can learn to improve analysis according to the real-world environment.

•  Edge AI is not just for smart cities, but in households, small shops