Harnessing Silicon Lifecycle Management For Chip Security

Silicon lifecycle management is starting to be used in ways that extend well beyond its original mission of ensuring a chip functions to spec throughout its expected lifetime. While tracking aging effects and component failures are still important, the technology also is being deployed to proactively monitor, authenticate, and respond to potential threats in real-time.

In fact, not applying silicon lifecycle management to semiconductors and systems for security purposes poses significant risks across the entire supply chain and operational life of a device. The technology can be used to ferret out counterfeit chips, hardware trojans and intrusions, and IP theft that are otherwise difficult to detect. Additionally, within the supply chain, the lack of ongoing monitoring and management during a chip’s operation can lead to exploitation of latent vulnerabilities, skipped updates and patch management, and side-channel attacks. It can even have systemic consequences, such as critical infrastructure disruption, financial and reputational damage, and compromised trust.

“Silicon lifecycle management, first and foremost, is a way to measure what the chip is doing right now, and that can be used during manufacturing, which is where the technology originated,” said Dana Neustadter, senior director of product management for Security Solutions at Synopsys. “A lot of today’s silicon lifecycle management technology is similar to what fabs do to monitor their own processes. But it’s also a sensitive subject. People are concerned that by putting it on the die and making it a design feature of the chip, the chip buyers — the people who are the fabs’ customers — can start to understand what the fab is actually doing and measure the performance of the fab, its consistency, and a lot of the things the fab cares about when it’s trying to get its processes to yield. They measure those things on an ongoing basis as a quality metric, but it all stems from managing yield and understanding whether the process is drifting or what’s happening, so there’s a lot of technology built around that. Silicon lifecycle management can reproduce that on the actual products that are coming out of the fab, and the people who are buying those, the people who have designed that component, can measure the fab’s performance. This gets very sensitive if somebody has reliability issues, or they’re seeing performance that’s different from what they designed, because then you start talking about who’s responsible for this. And if it’s serious enough, who’s going to pay to repair that.”

Silicon lifecycle management capabilities on a chip are useful for architects and design engineers, as well. “One of the reasons a lot of our customers are putting silicon lifecycle management components in their parts is to measure the performance in real time and to measure the performance at a level below the chip,” Neustadter said. “You’re looking at functional units throughout the chip, and you can understand things about how the chip is loaded, and whether work needs to be redistributed to different pieces within the chip. This is very true for AI systems, because you tend to have very large arrays of very simple things that run very fast, which means they consume a lot of power. If you’re not careful to distribute the workload over the available area, then you get local hotspots that represent parts of the chip that are working harder than others.”

Given these technical and operational advantages, chip architects and designers now recognize that silicon lifecycle management is an indispensable part of today’s device security strategies, and there are practical benefits and compliance implications of real-time monitoring and proactive risk mitigation.

“By embedding sensors and monitors into the silicon, we gain real-time visibility into chip behavior, which is essential for detecting anomalies and emerging threats,” said Adiel Bahrouch, director of business development for Silicon IP at Rambus. “This continuous feedback loop enables a proactive security stance, especially in high-assurance environments.”

Silicon lifecycle management also directly supports compliance with safety-critical applications across heavily regulated markets such as automotive. “By delivering continued visibility, traceability, and resilience at the hardware level, silicon lifecycle management enables OEMs and the supply chain to comply with the Cybersecurity Management System (UNECE R155) and Software Update Management System (UNECE R56) requirements,” Bahrouch explained. “In addition, it strengthens the supply chain’s ability to meet the ISO/SAE 21434 standard, ensuring cybersecurity risk management from concept through to vehicle decommissioning.”

Against this backdrop, it’s important to recognize the unique challenges faced within specific sectors, such as the automotive industry, where the security landscape is constantly evolving and demands a forward-thinking approach.

“In the automotive sector — unlike the functional safety risk landscape, which is essentially static for a given function — the security threat landscape is very dynamic, with the type and complexity of cybersecurity attacks changing throughout the whole lifecycle of the vehicle,” said Lee Harrison, marketing director, Tessent at Siemens EDA. “A weakness can take many years to show up. This is compounded by the fact that in most vehicle developments, the technology is already a number of years old before the vehicle hits the market. Therefore, with this dynamic security landscape, it is entirely possible that security features and safeguards built into the system today could be out of date even before the vehicle goes into production, unless the security technology is also extremely dynamic and adaptable to whatever future threats present their selves.”

To address this, many experts advocate hardware-based security solutions. “With a very clear and comprehensive threat analysis for each of the different attack scenarios, it is then possible to relate this analysis to the system functional hardware,” Harrison said. “In most modern IC designs, there are several points of intersection that are easy to monitor, such as the AXI interconnect. Due to the nature of the data at these points, this provides a very good view of the internal operations occurring during the operation of the device. To monitor this type of ADAS system, a hardware-based security solution can be created and an embedded analytics monitoring architecture inserted.”

Fig. 1:  Example of an embedded analytics deployment. Source: Siemens EDA

One crucial element of threat modeling is that it should not be static. “It should not be allowed to go stale,” Harrison said. “Everyone must start somewhere, and the time invested in the initial threat modeling is likely to pay dividends over time, if it is maintained and adhered to by the development teams. It is unlikely that there will be an industry-wide threat model. However, individual OEMs will have very similar models, as will their suppliers. Future developments are likely to see further automation, but this discipline will still be one of critical thinking.”

Lifecycle management spreads
The challenges and approaches to silicon lifecycle management extend beyond the functioning of a chip or system over time. The broader industry perspective reveals increasing complexity and heightened security requirements that influence every stage of the chip development process.

Simon Rance, general manager and business unit leader, Process and Data Management at Keysight EDA, observed that security is becoming a growing concern across industries like aerospace, defense, automotive, and medical equipment. “The key challenges include the prevention of data hacking and unauthorized access, protecting sensitive design and lifecycle data, as well as managing data sharing across different teams and company boundaries.”

Further, security in silicon lifecycle management involves multiple considerations, such as involving IT, legal, and export control teams, using blockchains for data encryption, securing data buses between processors, and controlling data access and preventing potential misuse.

“Security is now a critical part of the design flow, with teams spending 6 to 8 months analyzing tools and processes to ensure data protection,” Rance said, noting this is also driving organizational changes. “Different industries approach security in lifecycle management with varying levels of rigor, depending largely on the sensitivity and regulatory requirements of their fields. For instance, in aerospace and defense, there is a need for particularly strong security measures. These companies face strict regulations concerning data protection, export controls, and sharing information across teams or international boundaries. They often involve IT, legal, and export control departments to assess and approve any new lifecycle management or AI/ML tools — a process that can delay projects by 6 to 8 months. Techniques like blockchain for secure data tracking, encrypted data buses, and thorough compliance checks are often explored here.”

In automotive and medical industries, security is critical. But it’s also becoming more complicated as devices become remotely accessible. These sectors are starting to implement similar data protection strategies and internal controls as found in aerospace and defense.

“Across all these industries, there is a shift,” he said. “Engineers are increasingly required to work closely with specialists in IT, legal, and compliance to ensure that lifecycle management solutions are secure and compliant before deployment. So, while approaches differ in strictness and process, all these industries are focusing more on lifecycle data security, a thorough vetting of tools, and cross-departmental collaboration to mitigate risk.”

Implementing security
Silicon lifecycle management encompasses a series of mechanisms and best practices that are active throughout a chip’s entire existence, from provisioning to decommissioning, in order to improve device security. “Silicon lifecycle management enables continuous monitoring and anomaly detection (telemetry across the chip lifecycle), allowing detection of unexpected or malicious behaviors in real time,” said Mohit Arora, senior director for architecture at Synaptics. “Health telemetry via embedded sensors tracks device state, usage patterns, and potential security violations, supporting rapid responses before damage occurs. This technology also enforces secure provisioning and unique key installation during manufacturing, reducing the risks from compromised supply chains or unauthorized device cloning.”

Specific measures include:

• Policy-driven responses, such as key wiping, resetting, or disabling affected chips, to help prevent attackers from recovering secrets, even in the field.
• Support of firmware resiliency standards like NIST SP 800-193, enforcing firmware integrity and enabling recovery from corruption or attacks.
• Ensuring secure decommissioning by requiring the erasure of keys and secrets, preventing recovery from decommissioned devices.
• Defining and enforcing trust boundaries throughout the device’s lifecycle, minimizing attack surfaces, and supporting ongoing threat model updates as technology evolves.

“Collectively, these practices help transition security from a one-time design consideration to a continuous, proactive, and adaptive process,” Arora said.

Rambus’ Bahrouch noted that silicon lifecycle management is one of the most effective approaches for implementing security, particularly when layered with secure boot, encryption, and a hardware root of trust. “It enhances these by providing operational intelligence that connects design, deployment, and defense in a continuous loop throughout the lifecycle of chips and vehicles. It also shifts security from a static, design-time task to a dynamic, runtime discipline, allowing engineers to detect issues, adapt in the field, and maintain trust over the chip’s operational life. For mission-critical systems, this level of resilience is not just beneficial. It is essential to ensure safety, compliance, and long-term reliability in a highly connected ecosystem with evolving cybersecurity threats, and in software-defined vehicles.”

Conclusion
Big picture, silicon lifecycle management is redefining security practices in the semiconductor industry by enabling continuous monitoring, adaptive threat response, and robust operational intelligence. As these systems evolve, the integration of AI and analytics offers promising new capabilities for population-wide security monitoring but also introduces new risks that demand thoughtful access controls and trusted data handling. Ultimately, the proactive, dynamic approach championed by silicon lifecycle management is essential for safeguarding devices and systems in an increasingly connected and complex technological landscape.

Building on these advancements, it is important to consider how real-world adoption and operational realities are shaping the next steps for silicon lifecycle management.

“While we haven’t gotten yet to the point of being able to use this capability really well for security monitoring purposes, the same kinds of things show up in systems that are under attack, or systems that an adversary has taken over because parts of the system may be working harder than they should be for the workload that’s running right now,” said Synopsys’ Neustadter. “With silicon lifecycle management, you can measure and monitor that. That means that data is very sensitive, and it’s important that it be handled very carefully within the chip. That data needs to be collected by something that is trusted, something that’s able to keep it secret. If you’re sending it off-chip, you need to do that through secure channels and all those kinds of things. And once you have this for one chip, then you can communicate it somewhere else. Generically, we can say to a cloud database, ‘If I can do that for a population of chips, now I’ve got a very powerful capability to watch what that whole population is doing, and I can see when parts of the population are behaving differently from other parts of the population.’ I might be able to measure in real time the spread of an infection, of some kind of attack, by watching how the silicon lifecycle management systems are applying analytics. This is a huge opportunity for AI to be able to do population security monitoring. And if you do that, all of that needs to be secure. It needs to be traced back to a root of trust in that ecosystem of products. There are incredible opportunities here. None of them is being exploited yet. This is a few years away. But it’s about the right time for people to start exploring this.”

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