The AI act: transforming regulatory constraints into trust and performance levers

1. Context and background

Adopted in 2024 by the european union, the AI act is the world's first legal framework specifically dedicated to artificial intelligence.

Its objective: to ensure safe, transparent development and use of AI that respects fundamental rights, while stimulating innovation.

2. Core principles of the AI act

The AI act adopts a risk-based approach:

1. Unacceptable risk: prohibited systems (e.g., social scoring, behavioral manipulation).
2. High risk: strict obligations on data quality, human oversight, traceability, and robustness.
3. Limited risk: transparency obligations (e.g., indicating that content is AI-generated).
4. Minimal risk: no specific obligations.

The regulation also introduces:

• Specific rules for general purpose AI (gpai) models and systemic risk models (e.g., powerful llms).
• An "AI literacy" requirement: training and awareness for teams on safe and ethical AI use.
• Technical documentation and risk management obligations throughout the systems' lifecycle.

3. Major elements to remember from the regulation

• AI lifecycle management: risk management and continuous updates.
• Data quality: relevance, representativeness, absence of excessive bias.
• Human oversight: avoiding uncontrolled fully automated decisions.
• Transparency and traceability: documentation, event logs, content marking, indication that it's AI.
• Supplier compliance: due diligence and appropriate contractual clauses.
• End-user protection: clear information, rights, and recourse.

4. Our vision and support offerings

This regulation presents a strategic opportunity:

• To strengthen client and partner trust.
• To improve AI system quality and adoption.
• To create competitive advantage through controlled, ethical, and high-performing AI.

Operationally, this translates into the need to implement:

1. AI regulatory diagnostics

Map all your AI systems and classify them according to the AI act framework. Identify high-risk use cases, analyze current processes, and propose a clear compliance roadmap.

2. AI governance & risk management

Design a comprehensive governance framework covering:

• Data quality, representativeness, and security.
• Validation processes and human oversight.
• Performance indicators and continuous model monitoring.

Objective: integrate compliance from design and reduce operational risks.

3. Supplier due diligence

Audit your AI solution partners and suppliers to verify their alignment with the AI act.

Integrate precise contractual clauses on data quality, traceability, cybersecurity, and transparency to secure your entire value chain.

4. Training & AI literacy

Implement tailored programs to develop your teams' AI culture.

From executive awareness to technical training for operators, ensure a common understanding foundation, guaranteeing safe and compliant AI use.

5. Gpai & systemic risk models

Support compliance for the most powerful systems (llms, general-purpose models).

Covering:

• Technical evaluations and adversarial testing.
• Technical documentation and training data summaries.
• Enhanced cybersecurity processes and incident management.

Conclusion

The AI act is not just a regulatory constraint: it's an opportunity to structure your AI approach to maximize benefits while minimizing risks. StratImpulse supports you in this transformation, making compliance a lever for performance and trust.