Cybord warns of dangers of the stability illusion.docx

Reliability of electronic components has a direct impact on driver safety due to the growing integration of digital technology into vehicle operating and management systems – the evolution of the software-defined vehicle.

Legacy quality management tools and systems are not geared to detect faulty chips before they are fitted into vehicles. For auto makers the risks are reputational and financial – having to recall thousands of vehicles to replace components or settling legal claims for system failures resulting in death or injury.

In response to the challenges, Cybord has developed advanced visual artificial intelligence (Visual AI) inspection systems which are able to identify defective components in real time, allowing manufacturers to remove them before they are fitted.

Oshri Cohen, chief executive officer of Cybord, is on a crusade to educate manufacturers and assemblers about the need to focus as much on the hardware as the software.

The application of Visual AI goes far beyond routine quality assurance. It provides a practical way to integrate hardware cybersecurity into the production process.

Cybord research has identified the need to protect ADAS (Advanced Driver-Assistance Systems) and autonomous vehicles from hardware-based failure modes as being mission critical for the auto industry.

“While software and network vulnerabilities often get attention, hardware cybersecurity in the supply chain needs urgent focus. In manufacturing and supply chains, companies spend years building trusted brands, but even resilient organizations can face major failures without proactive risk management,” he says.

In an industry which invests billions of dollars in information technology and software defenses, the major vulnerabilities of the manufacturing line and supply chain are often overlooked. With the rapid advancement of AI and data-driven in-vehicle technology, automotive manufacturers no longer have the luxury of ignoring supply chain risks, he believes.

Hardware-based cyber threats include counterfeit components, unauthorized replacements, and malicious implants. Supply chain pressure, which is a factor of life in the auto industry, can   lead manufacturers to source from secondary or unverified suppliers.

Faulty components are not detected because equipment such as pick-and-place and automated optical inspection (AOI) machines are primarily designed to focus on the assembly process. They detect deviations in component placement and identify significant defects, rather than assessing the overall quality of components.

They do not detect subtle anomalies like remarked chips, aged components, or tampered packages. Even minor imperfections in components, be they a low-cost capacitor or high-end processor, can lead to the failure of high-value products.

OEMs are urged to overcome the inertia in updating their quality systems. Cohen warns that companies risk becoming complacent due to a combination of having a legacy of excellence leading to overconfidence in systems and suppliers, and leadership changes.

Complacent quality teams stop actively searching for vulnerabilities, believing their established processes are sufficient. That is when disaster strikes in the form of counterfeit components, cybersecurity breaches, or supply chain failures.

Cohen believes strongly that there needs to be a fundamental shift in supply chain risk, which he says is still largely reactive — triggered only after a failure. In the highly competitive automotive landscape this is a risk OEMs and Tier suppliers can no longer afford to take.

There needs to be an organizational mindset change from reactive crisis response to proactive prevention.

The good news is that the tools needed exist. Cybord is a world leader in the development of AI-powered solutions tailored for electronics manufacturing which provide real-time monitoring, predictive insights, and automated mitigation.

Automotive manufacturers require a three-layer proactive defense strategy to ensure long-term resilience of their quality control systems, according to Cohen.

Actionable technology implementation: These tools work in real-time, scanning and blocking threats before they enter production. They guard against defective, counterfeit, or malicious components.

Advanced alerting capabilities: This system monitors supply chain data and market signals, raising early alerts for OEMs to enable them to manage the disruptions.

Data-driven analytics: This layer analyzes extensive historical and real-time data on supply chain incidents, counterfeit trends, market shifts, and cybersecurity threats. Through examining long-term patterns, organizations refine detection algorithms, correlate warnings with actual failures, and stay ahead of evolving risks.

By adopting a structured, intelligence-driven defense strategy, automotive companies will not just survive, they will define the future of trusted manufacturing, says Cohen.

Visual AI

Unlike rule-based systems, Cybord’s Visual AI adjusts to natural board-level variations, reducing false positives while still identifying genuine threats. It operates in real time and at scale, allowing for comprehensive inspection coverage rather than just sampling.

Visual AI uses deep learning models to analyze high-resolution images of components captured during various stages such as pick-and-place, post-reflow, or final assembly. These images are compared against extensive databases of verified components to identify discrepancies in top markings, physical wear, probe marks, and other indicators of tampering or inauthenticity.

One of the most compelling aspects of Visual AI is its ability to work without relying on BOMs (bills of materials) or CAD (computer aided design) files. This independence makes the technology ideal for multi-tiered manufacturing environments where design documentation is incomplete, outdated, or inaccessible. The AI models can analyze what is on the board physically without needing to be told what should be there.

This capability not only accelerates deployment but also futureproofs the inspection process in dynamic production environments.

In essence, the integration of Visual AI into electronics manufacturing represents a major shift in how the auto industry approaches supply chain integrity, product authenticity, and embedded system security.

The tools are now available for OEMs and Tiers to see what is really happening on production line and to act before problems escalate.

To learn more, read the full technical article:

Visual AI-Driven Secure Electronics Manufacturing – TechRxiv

Case studies

The following case studies illustrate the points made by Cohen and Weiss.

An electronics manufacturing services (EMS) provider serving top-tier OEMs in the automotive industry.

In early 2025, one of the EMS provider’s OEM customers received a notification from their chip supplier that several component lots shipped over multiple quarters were potentially defective. The vendor advised that any board containing chips from those lots should be recalled and replaced immediately.

The OEM turned to the EMS and requested a list of all affected boards assembled since 2024. In total, 50,000 boards were at risk.

The OEM faced the costly and reputationally damaging prospect of a full recall. Cybord’s Micro-Traceability solution saved the day.

Below is a rough breakdown of the expected operational costs if all 50,000 boards had to be recalled:

These figures reflect only operational costs. They exclude reputational damage, customer retention efforts, or crisis PR often associated with large-scale recalls.

The EMS immediately turned to Cybord’s micro-traceability module which utilizes top-side visual analysis to read and accurately interpret each manufacturer’s specific coding, conventions, and variations.

This information is used to create a comprehensive repository of every component assembled on every board. It includes granular details such as manufacturer part number, date code, and lot code. Every component is linked to its corresponding board serial number, enabling unmatched micro-traceability across the production line.

Cybord’s micro-traceability module delivered the following:

• Analyzed every board image in detail
• Matched component-level data to the affected chip lot codes
• Identified only 974 affected boards – just under 2% of the total batch

Cost after Cybord analysis

With Cybord’s analysis, the OEM could now isolate and address only the affected boards:

The Bottom Line:

• Operational savings: $8.82M
• Risk avoided: Full-scale recall
• Time to resolution: Under 1 hour

A case for proactive traceability

This case underscores the value of real-time, component-level traceability integrated during production.

With Cybord’s traceability module in place from the start, the team could benefit from immediate access to verified component data — eliminating the need for emergency post-production analysis and enabling fast, clear decision-making.

Customer comment (EMS Site Manager): “Cybord’s micro-traceability capabilities are truly impressive. Their solution delivered exactly what we needed – full traceability and the ability to pinpoint issues with 100% accuracy. Thanks to this, our OEM customer avoided a major recall and saved significant time and cost.”

Component substitution

A leading global electric vehicle (EV) manufacturer renowned for its innovation and scale relies on high-volume SMT (surface mount technology) lines to produce safety-critical electronic assemblies. In partnership with the local EMS provider, Cybord’s Visual AI solution was deployed to inspect and analyze every component mounted on the PCBA.

During routine inspection on the SMT line, Cybord’s system detected a subtle anomaly on the top-side marking of a commonly used component. It was from a legitimate franchise manufacturer and appeared identical to all the other components approved by the OEM. But Cybord flagged the deviation: one small marking — the letter designating the automotive-grade version — was missing.

This component was being used in a vehicle control system and was expected to meet automotive-grade standards, particularly regarding thermal and environmental resilience.

On investigation it was found that the component, although authentic and from the correct vendor, was an industrial-grade variant not suited for automotive use. Without Cybord’s inspection, the substitution would have gone unnoticed.

Thousands of units could have been assembled and shipped with non-compliant components. This would have triggered widespread quality and safety risks, potential recalls, and regulatory consequences.

Thanks to Cybord’s component-level visual AI and real-time detection, the issue was caught early—before a single defective board left the factory

Result

• Immediate line-wide alert for out-of-spec components
• Prevented mass contamination of a production batch
• Protected the EV manufacturer from a potential recall and safety breach
• Proved the value of top-side visual-AI inspection for subtle anomalies and AVL enforcement