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Our company Manimama Law Firm offers its clients services for opening accounts in banks and electronic payment institutions (EMI) around the world. We combine a deep understanding of regulatory requirements with practical experience in working with banks and financial partners to provide full support to the client - from choosing a payment solution to communicating with the bank and preparing transaction justifications.
As artificial intelligence becomes more deeply embedded in business operations, it’s also redefining legal responsibilities. AI systems make decisions, analyze behavior, and personalize services while processing large volumes of personal data. This introduces a new layer of risk, especially around transparency, data subject rights, and legal compliance.
With the EU’s General Data Protection Regulation (GDPR) and the upcoming AI Act setting the legal framework, businesses need more than just a technical understanding — they need a solid legal strategy. In this article, we’ll break down the key obligations, risks, and legal benchmarks to consider when working with AI systems that handle personal data.
Automated Decisions Under the GDPR: How AI Affects Data Subject Rights
Modern algorithms increasingly make decisions directly impacting individuals — from credit denials to automated application screening. AI is crossing the line from being a “tool” to becoming a true agent of influence.
In this landscape, Article 22 of the GDPR acts as a legal safeguard against excessive automation. It ensures that people aren’t left at the mercy of opaque algorithms when decisions carry significant legal or personal consequences. For businesses, every AI-driven project must be backed by clear mechanisms for transparency, human oversight, and accountability.
This applies especially to cases like:
- Algorithmic credit scoring;
- Automated approval or rejection of applications (for services, rentals, leasing, etc.);
- Customer profiling for pricing strategies or targeted advertising.
Particular caution is needed with so-called “black box” models — systems whose internal logic even their creators can’t fully explain. Under GDPR, such processes must be:
- Transparent – individuals have the right to understand how decisions were made;
- Controllable – there must be meaningful human oversight;
- Impact-assessed – a Data Protection Impact Assessment (DPIA) is required to evaluate potential risks.
How the EU Plans to Regulate AI: Key Requirements of the AI Act
In 2024, the European Union finalized the AI Act — the world’s first comprehensive law regulating artificial intelligence, built around a risk-based approach. Shortly, the higher the potential risk to individuals’ rights and freedoms, the stricter the obligations on AI developers and users.
The AI Act classifies AI systems into four categories:
- Prohibited – practices like behavioral manipulation, social scoring, and real-time biometric surveillance for crowd control (with narrow exceptions);
- High-risk – systems involved in credit scoring, HR decisions, critical infrastructure management, and medical triage;
- Limited risk – including generative AI and chatbots, which must meet transparency requirements;
- Minimal-risk – systems with no significant legal impact, such as video games or spam filters.
High-risk systems must meet several key requirements:
- Implement a risk management system and conduct internal audits;
- Maintain comprehensive technical documentation;
- Ensure human oversight and provide explainability of the AI’s logic;
- Guarantee cybersecurity and resilience against misuse.
Practical Recommendations for Businesses
If your company is adopting AI solutions, there are three essential — yet strategically critical — steps you should take to get started:
1. Diagnose: Is your system considered AI?
If your product uses machine learning, data classification, or adaptive algorithms, it likely qualifies as artificial intelligence. Even if it looks like a traditional CRM or analytics platform at first glance, it may still fall under the scope of the AI Act.
2. Define your role
- Are you a provider? Then you’re responsible for ensuring that the model meets all technical, ethical, and security standards.
- Are you a user or controller? You’ll need to implement transparency policies, train your staff, and establish mechanisms to protect data subjects’ rights.
3. Embed legal safeguards into your technical architecture
Every system should:
- Be able to explain how and why a decision was made;
- Allow individuals to contest decisions or request human review;
- Obtain clear consent when required by the context.
These steps form the foundation of responsible AI — and are crucial for building trust in today’s digital environment.
In Conclusion
When algorithms process personal data, efficiency alone isn’t enough — responsibility is key. Today, GDPR compliance is more than a legal checkbox; it’s the foundation of digital trust, reputational resilience, and market legitimacy across the EU.
To work confidently with AI, businesses need more than technical know-how — they need a legal strategy that anticipates regulatory risks, ensures transparency, respects data subject rights, and adapts to the evolving landscape of AI governance.
Manimama Law Firm supports companies in integrating AI solutions with legal precision — from conducting DPIAs and drafting internal policies to auditing algorithms and navigating the AI Act. We help our clients turn technology into an asset, not a legal liability.
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The content of this article is intended to provide a general guide to the subject matter, not to be considered as a legal consultation.
