1. Understanding AI Models: The complexity and opacity of AI algorithms, especially deep learning models, make it difficult to fully understand and assess their behaviour.

2. Data Governance: Ensuring the quality, integrity, and security of data used by AI systems is challenging, given the vast and diverse data sources.

3. Bias and Fairness: Identifying and mitigating biases in AI models is crucial yet complex, as biases can be subtle and embedded in training data.

4. Ethical Considerations: Assessing the ethical implications of AI decisions and ensuring that AI systems adhere to ethical standards is a significant challenge.

5. Regulatory Compliance: Keeping up with evolving regulations and ensuring AI systems comply with legal and regulatory requirements is demanding. In India, DPDP requirements is to be considered.

6. Continuous Monitoring: AI systems require ongoing monitoring to ensure they continue to perform as expected and do not deviate from intended behaviour.

7. Transparency and Explain ability: Providing clear explanations of AI decision-making processes to stakeholders is essential but often difficult due to the "black box" nature of many AI systems.

8. Interdisciplinary Knowledge: Effective AI risk assessment requires knowledge across multiple domains, including AI technology, data science, ethics, and industry-specific regulations.