1. Data Dependency of AI
AI usually requires large volumes of personal data, which conflicts with privacy principles like data
minimization.

2. Informed Consent Issues
It is difficult to obtain specific, meaningful consent for complex and evolving AI uses.

3. Purpose Limitation
Data protection laws restrict using data for purposes other than originally stated, limiting AI adaptability.

4. Cross-Border Data Flow Restrictions
Limits on international data transfers hinder access to diverse datasets for AI training.

5. Compliance Burden
Strict data protection requirements (like audits, data retention, security measures) may slow down
innovation and increase costs.

6. Bias and Discrimination Risks
Inadequate regulation may lead to biased outcomes or discrimination in AI decisions, affecting fairness.

7. Lack of Transparency
AI algorithms are often opaque, making it difficult to ensure lawful and fair data processing.

8. Accountability Challenges
It remains unclear, who is responsible when AI systems misuse data or cause harm—developer, user, or
platform?

9. Dynamic Legal Landscape
Constantly changing global privacy laws create uncertainty for developers and startups.

10. Need for Ethical AI
Balancing innovation with responsible practices requires frameworks that go beyond legal compliance,
emphasizing fairness, safety, and human rights.