Holistic Approach to Embedded Cybersecurity
Two major technology shifts are transforming the cybersecurity requirements of embedded systems.
The first is the rapid adoption of Artificial Intelligence at the edge, where AI models are increasingly deployed directly inside devices rather than in the cloud. The second is the emergence of quantum computing, which is driving the transition toward Post-Quantum Cryptography (PQC) and challenging the long-term viability of today’s cryptographic foundations.
While these trends may appear unrelated, they create a common challenge for product developers: ensuring that embedded devices remain secure throughout increasingly long and complex lifecycle.
AI-enabled products must protect valuable data, algorithms, and intellectual property. At the same time, connected devices designed today may remain operational for ten, fifteen, or even twenty years, requiring security mechanisms that can evolve as new cryptographic standards emerge.
This reality is pushing cybersecurity beyond isolated protection features. Security must be designed as a lifecycle capability, combining protection, evaluation, and adaptability from silicon design to field deployment and long-term maintenance.
Evolving Threats Require a Lifecycle Security Strategy
Embedded devices are becoming simultaneously more intelligent, more connected, and more exposed.
Whether in automotive, industrial automation, healthcare, critical infrastructure, IoT, or edge AI applications, organizations must now secure multiple layers of their systems:
- Hardware and silicon assets
- Embedded software and firmware
- Data at rest and in transit
- AI models and inference processes
- Connected infrastructures and cloud services
At the same time, regulatory requirements, certification schemes, and cyber resilience expectations continue to increase.
Addressing these challenges requires more than adding security controls at individual points in the system. Organizations need a holistic cybersecurity approach that combines three complementary dimensions:
Protection
Protecting critical assets against physical and logical attacks, from silicon IP and firmware to cryptographic keys and AI models.
Security Evaluation
Continuously assessing the effectiveness of security mechanisms through threat analysis, side-channel evaluations, fault injection testing, vulnerability assessments, and certification activities.
Security Lifecycle Management
Designing systems with the ability to adapt over time through secure updates, crypto-agile architectures, and lifecycle security management, ensuring resilience against future threats that may not yet exist today.
These three pillars become particularly important when addressing the two major cybersecurity transitions shaping embedded systems: AI security and PQC readiness.
Protecting AI Models at the Edge
AI models have become some of the most valuable assets in modern embedded systems. They often represent years of research, development effort, and proprietary know-how.
When deployed on edge devices, however, these models become accessible to attackers seeking to:
- Extract model parameters
- Steal intellectual property
- Reverse engineer algorithms
- Modify model behavior
- Bypass licensing mechanisms
- Manipulate inference results
Beyond traditional cybersecurity risks, AI introduces entirely new attack surfaces. Model tampering, adversarial manipulation, and unauthorized model replication are becoming increasingly relevant as edge AI adoption accelerates.
Protecting AI therefore requires dedicated security mechanisms capable of safeguarding models throughout their lifecycle, from manufacturing and provisioning to deployment and in-field operation.
Secure-IC’s Securyzr™ S300 for AI Model Protection addresses these challenges by protecting AI assets directly within the device. It helps prevent model extraction, reverse engineering, and unauthorized execution while ensuring model integrity and securing inference outputs.
But protecting AI models raises a broader question: how can organizations ensure that the protection mechanisms deployed today remain trustworthy over the next decade?
This is where the transition toward Post-Quantum Cryptography becomes critical.
Preparing for the post-Quantum Era
The same long product lifecycles that make AI model protection essential also amplify the risks associated with quantum computing.
Many embedded systems entering development today will still be operating when quantum computers become capable of breaking widely used public-key cryptography. As a result, security decisions made now may determine a product’s resilience for years to come.
For many organizations, the question is no longer whether they should prepare for Post-Quantum Cryptography (PQC), but when.
Increasingly, that “when” is becoming now.
The growing reality of “harvest now, decrypt later” attacks, combined with regulatory momentum such as U.S. government directives and emerging initiatives from organizations including ANSSI, is accelerating the need for quantum-safe architectures.
Preparing for PQC is not simply a matter of replacing one cryptographic algorithm with another. It requires building systems that are inherently crypto-agile, enabling cryptographic mechanisms to evolve as standards mature and threats change.
Future-ready platforms must therefore combine:
- Strong hardware-based protection
- Secure key management
- Secure update capabilities
- Cryptographic agility
- Long-term lifecycle management
In other words, the same lifecycle mindset required to protect AI assets is also required to ensure a successful transition toward PQC.
Security Beyond Protection: Evaluation and Expertise
Deploying security technologies alone is not enough.
Organizations must also verify that security mechanisms effectively resist real-world attacks and continue to perform as expected throughout a product’s lifespan.
This requires security evaluation activities across the entire development process, including:
- Threat modeling and risk assessment
- Pre-silicon security analysis
- Side-channel evaluation
- Fault injection testing
- Vulnerability assessments
- Certification support
These activities provide evidence that security controls are functioning as intended and help identify weaknesses before products reach the market. Equally important, they ensure that security architectures remain aligned with evolving requirements, whether related to AI protection, post-quantum migration, regulatory compliance, or emerging threat vectors.
Combining protection technologies with cutting-edge security evaluation and cybersecurity expertise enables organizations to build stronger foundations for trust, resilience, and certification readiness.
Looking Ahead
AI at the edge and Post-Quantum Cryptography are often discussed as separate cybersecurity challenges. They are symptoms of a broader transformation.
Both require organizations to rethink how security is designed, validated, deployed, and maintained throughout the lifecycle of embedded products. Protecting AI models is not sufficient if cryptographic foundations become obsolete. Migrating to PQC is not enough if devices cannot securely protect their most valuable assets. Long-term resilience comes from combining protection, security evaluation, and the ability to continuously evolve security mechanisms over time.
This is precisely the vision driving Secure-IC.
As Cadence’s Security Excellence Center, Secure-IC provides a unique combination of cybersecurity technologies, evaluation expertise, and post-quantum readiness solutions. From AI model protection and silicon security to advanced attack assessment and crypto-agile architectures, Secure-IC enables semiconductor vendors, device manufacturers, and system integrators to build trusted, resilient, and future-proof products throughout their lifecycle.
In a landscape shaped by AI innovation and quantum disruption, organizations need more than point solutions. They need a trusted cybersecurity partner capable of securing today’s products while preparing them for tomorrow’s threats.