I recently engaged in a conversation with a senior executive of a large semiconductor company about an AI advisory opportunity. Our discussion stayed with me for days.
The conversation covered AI’s role in the semiconductor industry, evolving regulations, and adversarial AI. A key theme was cross-border compliance, specifically how voluntary standards like NIST AI Risk Management Framework (NIST AI RMF), and the mandatory EU AI Act can impact operations in the same company across borders, the consequences of inconsistencies, and ways to address them.
For example, imagine the regulatory consequences of a semiconductor company not applying project appropriate risk measures and shipping semiconductors to Europe. A key takeaway of our discussion was that risk management cannot be an afterthought.
We also agreed that AI’s risks transcended industries, with only the use cases and mitigation strategies at risk for changing.
For example, in semiconductors, explainable AI may translate to providing transparency in bandwidth allocation methods. In healthcare, explainable AI could mean a physician explaining AI-diagnosed medical conditions.
In the semiconductor industry – which is one of the 16 critical infrastructure sectors that needs to be protected for national security alignment – risk management is more than a regulatory compliance. It is a business imperative for staying competitive and earning the trust of one’s customers.
This article explores why AI risk management in semiconductor design, manufacturing, and design is a road to market leadership and is not solely about mitigating risks. Note that while this was written for the semiconductor industry, this article is equally applicable to all industries.
AI Risk Management as a Competitive Advantage
For semiconductor companies, AI risk management is not just about avoiding pitfalls and penalties. It is a market differentiation strategy. Semiconductor leadership companies in responsible AI earn regulator’s trust, gain customer confidence, and attract investors as proactive AI governance reduces legal and reputational risks.
By embracing consistent AI governance frameworks across the enterprise, semiconductor manufacturers can create a sustainable AI-powered semiconductor ecosystem while reducing exposure to security threats, regulatory penalties, and ethical dilemmas.
I have seen technology risks, especially AI preparedness and AI-related risks over and over across various companies’ board discussions. Additionally, I have brought up responsible AI discussions with my client company boards and CXOs, and at conferences – albeit in different industries, as AI risks transcend industries.
AI and Semiconductors-driven Telecom Innovation
Semiconductors are the backbone of telecom infrastructure. They enable 5G, fiber optics, and edge computing. For example, semiconductors also support IoT applications, industrial automation, healthcare devices, and much more. Some of the AI-driven advancements, and AI-on-the-chip have led to:
· Dynamic spectrum allocation, as AI optimizes network bandwidth in real time
· Automated bandwidth management by balancing traffic loads, and
· Intelligent routing of traffic ensuring efficient data transmission
Integrating AI in semiconductors is difficult, especially when opposing requirements collide, such as compute demand and energy efficiency. For example, deep learning models require high computational power which is opposite of prioritizing energy efficiency for mobile devices, IoT, and edge computing. Manufacturers have to balance computational needs and power efficiency.
Semiconductor Manufacturing and AI Risks
However, AI’s complexity also introduces risks, which include bias, regulatory and compliance, and security vulnerabilities. Let us examine three critical ones:
1. Explainability and Transparency
AI-powered chips rely on black-box models, making it challenging to or audit their decision-making process. Without transparency, compliance risks become high.
For example, AI-driven wafer detection models optimize quality control. However, if the model misclassifies a wafer as defective without any explainability, the entire batch could be wasted.
Explainability and transparency is also important for cross-border compliance. This is not a choice. For example, emerging laws, including the September 2024 Framework Convention on AI – signed by the US, EU, UK, and a few other nations – mandate non-discrimination and transparency. This is a binding treaty, so non-compliance is not an option.
Risk mitigation strategies include the following:
· Develop interpretable AI models to ensure decision clarity
· Implement explainable AI (XAI) techniques like LIME (Local Interpretable Model-Agnostic Explanations), and SHAP (Shapely Additive explanations)
· Require model documentation and audits for compliance
For further insights, Deloitte’s Trustworthy AI and my XAI article in Colorado AI News provide additional perspectives.
2. Bias and Fairness Risks
Bias in AI models can lead to resource allocation that is discriminatory. For example, in network management, biased training data could cause the AI to prioritize certain regions or demographics unfairly.
Another example: T-Mobile is developing an AI Radio Access Network (AI-RAN) through partnerships with AI leaders like Nvidia and OpenAI to create a self-optimizing network that intends to use AI for dynamic resource management. Without built-in transparency, AI can unintentionally prioritize certain demographics over others violating the fairness requirement.
Risk mitigation strategies include the following:
· Train AI models on diverse, representative datasets to minimize systemic bias
· Implementing bias detection frameworks throughout development
· Follow risk-appropriate NIST AI Risk Management Framework (NIST AI RMF) to ensure explainability
For more information on this topic, see my article here.
3. AI Security Vulnerabilities
AI-driven semiconductors are prime targets for adversarial attacks, model poisoning, and data extraction exploits. A compromised AI chip could lead to network outages, privacy breaches, or even state-sponsored cyber warfare.
For example, AI-driven traffic optimization can be exploited by tricking AI models to misclassify traffic, causing network congestion or security vulnerabilities. Additionally, AI-driven Distributed Denial of Service (DDoS) attacks can cripple entire telecom networks.
Risk mitigation strategies include the following:
· Conducting AI security testing to identify vulnerabilities
· Using secure enclaves and encryption techniques to protect AI models at the chip level
· Implementing real-time anomaly detection to flag potential cyber threats
Staying ahead of evolving AI regulations globally, such as the EU AI Act, U.S. federal and state AI guidelines, GDPR, and others is a critical way to avoid reputational harm and penalties.
Security risks and countermeasures of adversarial attacks are well-explained in a similarly titled ScienceDirect.com article.
Conclusion
Semiconductors power the next-generation of telecom innovation, and AI-powered chips are key to unlocking smarter networks, optimized performance, and greater automation. However, AI presents inherent risks that need to be strategically managed. These include explainability, bias and fairness, and security risks. Embedding robust AI governance directly at the chip level is a strategic necessity.
AI risk management is now a competitive strategy. Therefore, it’s no longer simply a regulatory requirement. Companies that prioritize transparency, fairness, security, and compliance will be able to comply and position themselves as trusted, responsible leaders.
References
IDC (2024). Global Semiconductor Market to Grow by 15% in 2025, Driven by AI and HPC Demand. https://www.idc.com/getdoc.jsp?containerId=prAP528
Semiconductor Supply Chains, AI and Economic Statecraft: https://cetas.turing.ac.uk/sites/default/files/2024-04/cetas_research_report_-_semiconductor_supply_chains_ai_and_economic_statecraft.pdf
Risk Management for sustainable growth in the semiconductor industry: https://www.ey.com/en_nl/insights/risk/risk-management-for-sustainable-growth-in-the-semiconductor-industry
