AI Technology
deployed to manage critical safety protocols.
For these systems, an organisation cannot apply a CE Mark unless the high- risk requirements of the AI Act have been entirely fulfilled alongside the existing safety directives.
Enforcement timelines and the penalty structure
Following the May 2026 AI Act Omnibus, the European Parliament and Council amended the legislation to ease the transition for businesses. The enforcement deadlines for high-risk AI rules have been extended to 2 December 2027 for standalone systems, and 2 August 2028 for systems embedded within regulated products.
Organisations failing to establish verifiable compliance face a three-tiered penalty system: Unacceptable risk violations – fine of up to €35 million or 7 per cent of total worldwide annual turnover, whichever is higher.
Non-compliance with obligations – violations relating to data governance
or transparency carry fines up to €15 million or 3 per cent of total worldwide annual turnover.
Misleading authorities – supplying incorrect information results in fines up to €7.5 million or 1 per cent of total worldwide annual turnover To foster innovation, regulatory simplifications regarding technical documentation have been extended beyond SMEs to include small mid-caps (SMCs).
Bridging the gap
The EU AI Act takes AI from being a technical issue to a board-level compliance priority. However, regulatory compliance is merely the floor. To engineer genuine operational trust, businesses must shift from traditional perimeter defence to securing the intelligence itself. This systemic resilience is achieved by implementing the ‘Three Pillars of Trust’: 1 Technical resilience – hardening systems against logic-based manipulation, such as prompt injection, to ensure the AI cannot bypass safety filters.
2 Ethical governance (The Compass)
– aligning algorithmic behaviour with legal requirements through bias monitoring and mandatory human-in- the-loop oversight.
3 Supply chain transparency – creating complete visibility across the AI stack and using an AI asset registry to track data provenance and eliminate unauthorised shadow AI.
As an organisation cannot secure what it has not catalogued, a critical component of governance is continuous maintenance of the AI asset registry. Organisations should therefore integrate the six core principles of the AI Essentials framework: Pillar 1 – Security and Data Integrity: Securing data pipelines and deploying semantic firewalls to defend against adversarial attacks.
Pillar 2 – Transparency and Explainability: Providing clear notification to stakeholders that they are interacting with an AI system.
Pillar 3 – Human-in-the-Loop and Accountability: Mandating that any AI output leading to a high-risk operational decision receives final sign-off from a designated human authority.
Pillar 4 – Fairness and Bias Mitigation: Actively testing datasets and outputs to ensure the AI does not produce discriminatory results.
Pillar 5 – Reliability and Hallucination Management: Grounding the AI in verified, factual data using Retrieval- Augmented Generation (RAG) to minimise fabrications.
Pillar 6 – Societal, Ethical, and Consequential Impact: Executing comprehensive assessments to evaluate the broader effects of AI deployment on the workforce and society.
AI-enabled systems must be designed for the safety of the end-user, not just to satisfy a regulator. By the end of the August 2028 transition window, the electronics manufacturers that thrive will be those that didn’t just ‘tick the box’ of the EU AI Act, but those that actively embed frameworks like AI Essentials to foster a verifiable and human-centric AI ecosystem.
www.tuvsud.com/en-gb/cybersecurity
www.cieonline.co.uk
Components in Electronics
October 2026 19
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