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Imagine an engineer waking up to find their morning coffee isn’t the only thing automated — their entire embedded development workflow now leans heavily on AI. From coding to testing, AI tools speed development but inject fresh risks that could jeopardize vital systems controlling everything from medical devices to power grids. This unexpected dependency raises crucial questions: How secure is AI-generated code? What pitfalls lurk unseen? Let’s delve into the top risks and the pathways to safer AI-embedded innovations as revealed by RunSafe’s 2025 AI in Embedded Systems Report.

The Current State of AI Adoption in Embedded Systems

AI in embedded systems has moved from experimental trials to mainstream adoption, fundamentally reshaping how embedded engineers approach development. According to RunSafe’s 2025 AI in Embedded Systems Report, AI tools are now a core part of daily workflows across critical sectors, including medical devices, automotive systems, industrial controls, and energy infrastructure.

Widespread Integration of AI Tools

The report’s survey of over 200 embedded systems professionals reveals that 80.5% currently use AI tools in their development processes. These tools are not limited to code generation; they also automate test creation, analyze logs, and scan for vulnerabilities. This widespread adoption is not confined to a single industry. From life-saving medical equipment to complex industrial machinery, AI-generated code is now running in production environments across a diverse range of applications.

• 80.5% of embedded engineers use AI tools in development workflows
• 83.5% have deployed AI-generated code into production systems
• 93.5% plan to increase AI usage in the next two years

Acceleration of Embedded Development

AI’s integration into embedded development is delivering significant productivity gains. Teams report faster coding, more efficient test generation, and improved log analysis. Automated vulnerability scans powered by AI help identify potential security issues earlier in the development lifecycle. This acceleration is enabling organizations to bring new products to market more quickly and to respond to evolving requirements with greater agility.

“AI is now woven into the everyday workflows of embedded engineers. It writes code, generates tests, reviews logs, and scans for vulnerabilities. But the same tools that speed up development are introducing new risks—many of which can compromise the stability of critical systems.” — RunSafe’s 2025 AI in Embedded Systems Report

Emerging Risks and Security Gaps

While AI-generated code is accelerating embedded development, it is also expanding the attack surface. The report highlights that 73% of professionals rate the cybersecurity risk of AI-generated code as moderate or higher. Despite this, the pace of AI adoption continues to outstrip the evolution of security practices. Traditional security validation and traceability methods are struggling to keep up with the volume and complexity of AI-generated code.

• 53% cite security as their top concern with AI-generated code
• 33.5% experienced a cyber incident involving embedded software in the past year
• 60% of teams are using runtime protections to address vulnerabilities introduced by AI tools

Sector-Wide Impact and Future Growth

The integration of AI in embedded systems is not limited to early adopters. The report documents a sweeping trend across all critical infrastructure segments, with 93.5% of organizations planning to increase their use of AI tools in the coming years. At the same time, 91% anticipate boosting their investment in embedded software security, signaling a growing recognition of the need to address emerging embedded development risks.

As AI-generated code becomes more prevalent in production systems, the challenge for organizations is to balance the benefits of accelerated development with the need for robust security and resilience in embedded environments.

Six Most Critical Risks of AI-Generated Code in Embedded Systems

According to RunSafe’s 2025 AI in Embedded Systems Report, the rapid adoption of AI-generated code is transforming embedded development across medical, automotive, industrial, and energy sectors. However, this shift introduces significant security concerns and operational risks. The following outlines the 6 most critical risks identified by embedded professionals, with a focus on memory safety vulnerabilities, static analysis embedded code challenges, and the growing need for runtime protections.

1. Reuse of Insecure Legacy Code Patterns

AI models often learn from vast repositories of legacy C and C++ code, some of which contain outdated or insecure patterns. This reuse can reintroduce memory safety vulnerabilities—such as buffer overflows and use-after-free errors—into new embedded applications. The report notes that memory safety issues account for 60-70% of embedded software exploits, and AI-generated code is not immune. As a result, organizations must scrutinize AI outputs for inherited weaknesses.

2. Missed Real-Time Deadlines

Embedded systems frequently operate under strict timing constraints. AI-generated code may not always account for real-time requirements, leading to missed deadlines that can cause system instability, operational failures, or non-compliance with certification standards. The report highlights that failure to meet real-time constraints can jeopardize both reliability and regulatory approval, especially in safety-critical environments.

3. Increased Complexity and Maintainability Challenges

AI tools can generate large volumes of code rapidly, but this often results in increased complexity and reduced readability. Such complexity makes it harder for engineers to maintain, debug, and verify code, increasing the risk of hidden defects and long-term technical debt. The report emphasizes that maintainability is a growing concern as AI-generated code becomes more prevalent in production systems.

4. Insufficient Traceability of AI Tool Usage

Traceability—the ability to track how, where, and why code was generated—is essential for auditing and compliance. However, AI-generated code often lacks clear provenance, making it difficult to determine which tools or models contributed to specific code segments. This insufficient traceability complicates root cause analysis and hinders efforts to meet standards like MISRA C 2025, which are expected to require greater transparency for AI-assisted development.

5. Inadequate Static and Dynamic Analysis Coverage

Traditional static analysis embedded code and dynamic testing tools are not always equipped to handle the scale and variability of AI-generated code. As a result, vulnerabilities may go undetected, especially when code is generated or modified at high velocity. The report urges teams to adapt their analysis methods and increase investment in automated code review and runtime protections to address these gaps.

6. Expanded Attack Surfaces

The speed of AI-driven development often leads to rapid integration of third-party libraries and components. This can expand the system’s attack surface, introducing new entry points for adversaries. The report found that 33.5% of organizations experienced a cyber incident involving embedded software in the past year, underscoring the need for robust runtime protections and continuous vulnerability monitoring.

• 53% cite security as their top concern with AI-generated code.
• 73% rate the cybersecurity risk of AI code as moderate or higher.
• Runtime resilience and adaptive security practices are becoming critical defenses.

Runtime Protections and Emerging Security Practices

As AI-generated code becomes a mainstay in embedded systems, organizations are rethinking their approach to embedded systems security. According to RunSafe’s 2025 AI in Embedded Systems Report, 60% of teams now implement runtime protections—such as exploit mitigation and memory relocation—to address new risks introduced by AI-assisted development. These runtime protections are not just a best practice; they are quickly becoming a necessity as static analysis alone cannot catch every vulnerability in complex, AI-generated code.

Why Runtime Protections Matter

AI-generated code can accelerate development but often introduces subtle bugs and security gaps, especially in memory management. Traditional static analysis tools are effective for catching certain classes of issues, but they may miss vulnerabilities that only appear during execution. Runtime resilience—the ability of a system to detect and respond to threats as they occur—serves as a crucial line of defense. Techniques like Address Space Layout Randomization (ASLR), stack canaries, and control flow integrity help reduce exposure to memory safety issues, which remain a top concern in embedded environments.

Hybrid Security Approaches

The report highlights a shift toward hybrid security models that combine static analysis, dynamic analysis, and hardware-aware validation. This layered approach is gaining traction as organizations realize that no single method is sufficient for securing AI-generated code. For example:

• Static analysis tools scan code for known patterns and vulnerabilities before deployment.
• Dynamic analysis monitors software behavior at runtime, catching issues that static tools miss.
• Hardware-aware validation ensures that protections are effective on the actual devices where code runs.

This multi-layered validation is essential for maintaining software hardening in embedded systems, especially as AI-generated code increases the complexity and unpredictability of attack surfaces.

Defining Boundaries: Human Oversight and AI Collaboration

A practical mitigation strategy emerging from industry leaders is to define clear boundaries between AI and human responsibilities. Many organizations now use AI tools to generate boilerplate or non-critical code, while reserving architectural decisions and safety-critical components for experienced engineers. This approach leverages the speed of AI while maintaining the oversight needed for high-assurance systems.

“Runtime protections are central to mitigating the unique risks of AI-generated code. Continuous, multi-layered validation is now the standard for embedded systems security.”
— RunSafe 2025 AI in Embedded Systems Report

Security Investment and Evolving Standards

The report notes that 91% of organizations plan to increase investment in embedded software security over the next two years. This surge is driven by rising incident rates—33.5% of teams experienced a cyber event involving embedded software in the past year—and the growing complexity of AI-generated code. Emerging standards, including IEC 62443 and NIST 800-82, are pushing for more rigorous assessments and continuous validation loops throughout the software lifecycle.

In summary, runtime protections and evolving security practices are at the forefront of defending embedded systems against the risks posed by AI-generated code. Organizations are adopting layered, adaptive strategies to keep pace with the rapidly changing threat landscape.

Future Priorities: Balancing AI Innovation with Embedded Security

As AI-generated code becomes a mainstay in embedded systems development, organizations face a pivotal challenge: how to harness the speed and efficiency of AI tools while maintaining robust embedded software security. According to RunSafe’s 2025 report, 93.5% of teams expect to increase their use of AI in the next two years, and 91% plan to boost investment in security. This dual commitment signals a new era—one where innovation and risk management must advance together, especially for critical infrastructure cybersecurity.

The growing reliance on AI for code generation, testing, and vulnerability scanning brings undeniable benefits. Automation now enables faster identification of flaws and more efficient production of Software Bill of Materials (SBOMs), which are essential for managing supply chain visibility and third-party risks. As embedded systems become more complex and interconnected, automation and resilience are no longer optional—they are foundational to operational safety and compliance.

However, the report highlights that regulatory requirements are evolving just as quickly as the technology itself. Standards like IEC 62443 and NIST 800-82 now demand more than periodic audits or checklist-based compliance. They call for proactive, continuous risk management and the ability to demonstrate traceability throughout the software lifecycle. Organizations must establish clear policies for AI tool usage, define boundaries for automated code integration, and ensure that every change is documented and reviewable. This level of oversight is critical for meeting both current and future regulatory requirements, as well as for building trust with customers and stakeholders.

Another key insight from the report is the industry’s shift toward AI-assisted threat modeling and security automation. As the volume of AI-generated code grows, manual review is no longer feasible at scale. Security teams are turning to automated tools that can analyze code, monitor runtime behavior, and flag anomalies in real time. This approach not only improves detection and response but also supports compliance with emerging standards that emphasize runtime resilience and continuous monitoring.

The cultural shift underway is equally important. Organizations are moving away from viewing AI as an infallible solution and instead recognizing it as a powerful tool that requires human oversight and clear boundaries. This mindset supports safer deployment practices, encourages ongoing education, and fosters collaboration between development, security, and compliance teams.

Looking ahead, the priorities are clear: organizations must invest in both AI innovation and embedded software security to protect critical infrastructure. This means adopting automation for vulnerability management and SBOM generation, staying ahead of regulatory requirements, and building resilient systems that can withstand evolving threats. By balancing speed with safety and innovation with oversight, the industry can ensure that AI-generated code strengthens rather than undermines the security of embedded systems.

TL;DR: AI-generated code is widespread in embedded development, accelerating innovation but introducing significant security risks. The RunSafe 2025 report highlights six critical hazards, from insecure legacy pattern reuse to runtime vulnerabilities, urging a shift toward runtime protections, automated vulnerability detection, and stringent security practices to safeguard critical infrastructure.