• Shreelakhshmi K
• Aparna Arun

Every powerful Artificial Intelligence model is fueled by enormous amounts of personal data, archives of human experiences often harvested without user consent. This silent collection of data is used to feed machine learning algorithms to enable them to identify patterns and to refine their predictive capabilities which raises concerns of privacy infringement. This paper focuses on AI systems in India, the techniques used by it to acquire user data and the lack of transparency in data accumulation processes. Further, these datasets are used for training AI models to enhance predictive analytics, targeted advertising, and automated decision-making. AI Models actively collect countless interactions and transactions, perpetually generating vast databases for training purposes. It studies whether the Digital Personal Data Protection Act (DPDP), 2023 is sufficient to safeguard users from exploitative AI training methods. This paper compares different international frameworks such as the European Union’s General Data Protection Regulation (GDPR) and other Data Privacy Laws which emphasises stronger consent, transparency and accountability measures. This paper seeks to contribute to enhancing Responsible AI in India by offering suggestions. This includes strengthening informed consent mechanisms and strong privacy protection regime and investing in user awareness programmes to ensure AI development which aligns with dignity, trust and autonomy.

AI Training, Data Privacy, Privacy Infringement, Publicly Available Personal Data

"AI Training : Strenghthening DPDP Act to enhance data privacy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 11, page no.e92-e97, November-2025, Available :http://www.jetir.org/papers/JETIR2511412.pdf

"AI Training : Strenghthening DPDP Act to enhance data privacy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 11, page no. ppe92-e97, November-2025, Available at : http://www.jetir.org/papers/JETIR2511412.pdf