As the automotive industry accelerates toward software-defined vehicles (SDVs), the EU AI Act is emerging as a pivotal regulatory framework that developers and OEMs must understand and integrate into their workflows. Designed to ensure trustworthy, ethical, and safe AI systems, the european AI Act introduces stringent requirements, especially for high-risk applications like autonomous driving and advanced driver assistance systems (ADAS).
Key Challenges for Automotive AI teams
The main challenges when developing AI models for SDVs are:
- High-Risk Classification: AI systems in SDVs often fall under the “high-risk” category, triggering mandatory conformity assessments, documentation, and audit readiness.
- Complex Compliance Landscape: with over 450 pages of legal text, translating regulation into engineering tasks is a daunting challenge. Manual reviews and fragmented documentation slow innovation and increase costs.
- Audit Pressure: teams must prepare for both scheduled and surprise inspections. Non-compliance can result in fines up to €7.5 million or 3% of global turnover.
AI Trust & Compliance frameworks
To address these hurdles, it is key to develop modular, automated compliance frameworks tailored for SDVs to enable:
- End-to-End System Assessments: rapid, audit-ready evaluations aligned with the EU AI Act.
- Configurable Reports: targeted assessments for specific AI components, such as dataset balance or model transparency.
- Audit Phase Interface: tools for third-party assessors, streamlining feedback and reducing evaluation time.
Who should worry about this?
- OEMs: responsible for system-level compliance, they must ensure every AI component meets regulatory standards before vehicle launch.
- Tier 1 Suppliers: developers of critical AI modules must demonstrate component-level compliance to OEMs, enhancing collaboration and market competitiveness.
Industry-wide implications
The EU AI Act is not just a legal hurdle, but a strategic opportunity. By embedding compliance into the development lifecycle, automakers can build more resilient, transparent, and future-proof AI systems.
The Act encourages:
- Cross-functional collaboration: AI, cybersecurity, safety, and regulatory teams must work in tandem.
- Lifecycle accountability: from design to post-market monitoring, traceability becomes a core requirement.
- Innovation through structure: automated tools and frameworks transform compliance from a bottleneck into a catalyst for better engineering practices.
As SDVs become the norm, the EU AI Act will shape how automotive AI is built, validated, and deployed. Forward-thinking developers and suppliers who embrace structured compliance will not only avoid penalties but will lead the next wave of intelligent mobility.
GPAI & GenAI Under the EU AI Act: what developers must know
The GPAI Code of Practice, finalised in May 2025, provides critical guidance for providers of General-Purpose AI (GPAI) models, including Generative AI (GenAI) systems. The EU AI Act distinguishes between complex GenAI models -those with systemic risk– and simpler GenAI models, each facing different compliance burdens:
- Complex GenAI (systemic risk models): These models exceed thresholds like 10²⁵ FLOPs in training compute or demonstrate high-impact capabilities across domains. Providers must conduct adversarial testing, risk assessments, and incident reporting, and ensure cybersecurity protections. They must also notify the European Commission for public database inclusion and maintain detailed documentation of model architecture and evaluation strategies.
- Simple GenAI Models: These are not classified as systemic risk and face lighter obligations. Providers must publish a summary of training data, ensure copyright compliance, and maintain technical documentation for downstream users. Transparency is key: outputs must be labeled, and users informed when interacting with AI systems.
The Code of Practice serves as a blueprint for demonstrating compliance, helping developers navigate the AI Act’s layered requirements while fostering innovation and trust in GenAI technologies.