1. Lack of Binding International Law - No comprehensive global treaty imposes direct human rights obligations on MNCs.

2. Corporate Veil and Separate Legal Personality - Parent companies often escape liability for actions of their foreign subsidiaries due to separate legal identity.

3. Jurisdictional Barriers - Victims face difficulty in suing MNCs in their home countries due to lack of jurisdiction or forum non conveniens rules.

4. Weak Domestic Legal Systems - Host countries where violations occur often have weak enforcement mechanisms, judicial corruption, or fear of losing investment.

5. Complex Corporate Structures - MNCs use layered ownership and supply chains to obscure accountability and responsibility.

6. Limited Extraterritorial Application of Laws - Few countries allow domestic laws to be applied to corporate conduct abroad, and even when allowed, enforcement is rare.

7. Political and Economic Influence of MNCs - MNCs may use lobbying and economic leverage to avoid or dilute liability.

8. Lack of Access to Justice for Victims - Victims often lack legal representation, resources, or knowledge to pursue claims across borders.

9. Non-binding Nature of Guidelines - Instruments like the UN Guiding Principles on Business and Human Rights are voluntary and lack enforcement power.

10. State-Corporate Nexus - When states are complicit or benefit from corporate activities, holding MNCs accountable becomes even more difficult.

Balancing data protection and innovation in AI-based platforms is challenging because AI systems rely heavily on large datasets to improve performance, often conflicting with data protection principles like minimization and consent. Ensuring user privacy while enabling data-driven innovation requires navigating complex regulations, such as GDPR or India’s DPDP Act. Challenges include obtaining meaningful consent, preventing data misuse, ensuring transparency in automated decisions, and avoiding algorithmic bias. Additionally, enforcing the right to erasure or data portability in AI models is technically difficult, often limiting compliance and slowing innovation.