embedded award 2025: Start-up nominees

Young companies are driving technological development. The start-up scene in the embedded industry is correspondingly lively. Reason enough to dedicate a separate category to start-ups at the embedded award. Take a look at the future of the industry …

start-up Area @embedded world — The start-up scene in the embedded industry is lively

An end-to-end security platform specifically designed for IoT devices, an end-to-end secure ecosystem and a secure storage for AI models

Periphery Insights & Detect

Exhibitor: Periphery
Hall/Booth: 2-412

The cost of cybercrime globally is projected to hit $10.5 trillion annually by 2025, with critical infrastructure increasingly becoming a target. IoT devices in sectors like energy, defence, and healthcare are particularly vulnerable due to their limited security capabilities and growing regulatory pressures. New regulations like the EU Cyber Resilience Act (CRA) and NIS2 directive now demand robust cybersecurity measures, creating urgent challenges for manufacturers. Failure to comply can result in significant fines, market access delays, and reputational damage. A 2023 study found that the cost of compliance alone can exceed $1.5 million per product for companies operating in highly regulated environments, excluding the cost of responding to breaches, which averages $4.5 million.

Periphery addresses this urgent need by providing an end-to-end security platform specifically designed for IoT devices. Our solution accelerates time-to-market by streamlining compliance processes, including SBOM risk analysis, and ensures devices are secure post-deployment with real-time monitoring powered by proprietary machinelearning models. By addressing the dual challenges of evolving threats and rising regulatory demands, Periphery empowers manufacturers to protect their devices and secure their place in the market.

Our platform addresses significant gaps left by both network-based and embedded IoT security solutions. Network-based tools monitor traffic but miss critical host-level data, which is where most threats emerge and where the greatest impact can be achieved by stopping malicious activity early. Current embedded solutions, while assisting with secure coding and hardening firmware during development, fail to address operational security once devices are deployed. These solutions are static, unable to detect evolving threats or adapt to new risks, leaving devices vulnerable in real-world environments.

Unlike traditional embedded solutions, our platform evolves with the device. Our Central AI continuously updates threat intelligence, automates compliance alignment, and eliminates the need for manual configuration or rule updates.

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At embedded world Exhibition&Conference 2025 from March 11 to 13, 2025,
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CyberWhiz IoT Cyber Security Solutions for Edge·Mobile·Cloud

Exhibitor: CyberWhiz IoT Cyber Security
Hall/Booth: 5-374

At CyberWhiz, we address the critical challenge of securing IoT ecosystems, which involve three distinct domains: edge devices, mobile applications that are used for monitoring and control those edge devices, and required cloud services.

CyberWhiz Embedded can be embedded into any embedded device whether it has a MCU or MPU. We can work in any kind of operating system from bare metal to RTOS and Emebdded Linux/Android. We also have a secure Bluetooth&Wi-Fi combo module, which has an Elliptic Curve Cryptography based HSM from Microchip, helps IoT device manufacturers to be 100% compliant to pcoming regulations like Radio Equipment Directive(RED) DA and Cyber Resilience Act(CRA) very easily.

CyberWhiz Mobile secure any kind of mobile application that is used to control/monitor and that device, independent from whether it is designed with Native iOS&Android, React Native or Flutter, with less than a 1 minute. It is just a one line of code integrated into the mobile application, and then making it 100% OWASP Mobile Top 10. This will help not to have any vulnerability in end-to-end IoT ecosystem during penetratoin tests because CyberWhiz Mobile increases the cyber resilience of the mobile application a lot and it cannot be bypassed. So IoT device manufacturer should not necessarily declare any known vulnerability.

Finally, CyberWhiz Defence Center can monitor and edge device and/or mobile application in the field continuously during the lifecycle of the IoT product in the field, and detects instantly if there is an attack and cyber anomaly at the edge device and/or its mobile application. Accordingly, related IoT manufacturer is being informed immediately.

With integrating our softwares and/or hardwares into their edge devices and/or mobile applications, IoT device manufacturers will have an end-to-end secure ecosystem for the sustainability and the continuity of their businesses while having a privilige of monitoring their devices' cyber state continuosuly in the field.

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AI Bunker

Exhibitor: Accelerat
Hall/Booth: 4-625

Embedded devices increasingly rely on AI models to perform disparate tasks. Companies need to spend a lot of effort, time, and money to build AI models, such as deep neural networks, due to a long process that includes onerous design and development phases. AI models are however susceptible to theft, tampering, and unauthorized access due to the vulnerabilities of hardware and software environments. Concerning theft, when no proper countermeasures are in place, attackers could steal intellectual property that required enormous investments to be generated.

Protecting AI models on embedded devices while maintaining a seamless usage workflow is hence a major challenge. AI Bunker addresses this by enabling secure storage of AI models within a dedicated, strongly-isolated software partition on the embedded device, ensuring high levels of protection. At the same time, it transparently allows invoking the model‘s inference using a standard workflow, making it appear as if the model resides in a regular environment.

AI Bunker empowers embedded device manufacturers to:

Safeguard proprietary AI models from theft, reverse engineering, and unauthorized access
Ensure seamless model invocation with standard AI workflows, reducing development and deployment complexity
Deliver real-time inference performance for secure AI use cases on embedded devices
Provide secure model storage in isolated partitions
Implement advanced security protocols and AI model updates
Support for tamper detection

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