Yogeshwari Yawalkar, Mahek Jameer Pathan, Misba Shakil Khan, Pravin Sakharam Barela

DOI: https://doi.org/10.5281/zenodo.24129238

Multi-domain artificial intelligence (AI) assistants—systems capable of handling diverse tasks spanning conversation, code generation, information retrieval, planning, and tool use—have rapidly become integral to personal, enterprise, and public-sector workflows. Their versatility expands the attack surface and amplifies risks that single-purpose models do not fully exhibit. This literature-based research paper synthesizes findings from more than twenty peer-reviewed studies, industry frameworks, and policy documents published primarily between 2021 and 2026. It examines technical risks (prompt injection, excessive agency, hallucination, data leakage), socio-technical risks (bias, misinformation, representational harms), and systemic risks (supply-chain vulnerabilities, multi-agent failure modes). Special attention is given to the Indian context, including the implications of the Digital Personal Data Protection (DPDP) Act, 2023, linguistic and cultural diversity challenges, digital public infrastructure exposure, and emerging governance gaps. A comparative review of 22 key works is presented. The paper concludes with recommendations for layered technical controls, organizational practices, and policy interventions suited to India’s regulatory and socio-economic landscape.

multi-domain AI assistants, large language models, agentic AI, prompt injection, hallucination, bias, privacy, DPDP Act, India, AI governance, systemic risk

Multi-domain AI assistants concentrate capability and, with it, risk. The literature reviewed in this paper demonstrates that technical vulnerabilities (prompt injection, excessive agency, hallucination, data leakage), socio-technical harms (bias, misinformation, representational injury), and systemic threats (supply-chain compromise, multi-agent cascade failures) are interrelated and amplified by the very features that make these systems useful. India’s rapid adoption, combined with the specific contours of the DPDP Act, linguistic diversity, and digital public infrastructure, produces a distinctive risk environment that existing global research only partially addresses. Progress will depend on layered defenses that do not place sole reliance on model alignment, on governance mechanisms that can keep pace with agentic capabilities, and on research that centers the needs and constraints of diverse user populations. The comparative review and risk synthesis offered here are intended as a foundation for such continued work. As multi-domain assistants move from experimental tools to infrastructure, the quality of risk management will determine whether their benefits can be realized safely and equitably.

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Yogeshwari Yawalkar, Mahek Jameer Pathan, Misba Shakil Khan, Pravin Sakharam Barela (2026). Risks in Multi-domain AI Assistants: A Literature-Based Analysis with Focus on the Indian Context. International Journal of Computer Techniques, 13(6), 651–663. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.24129238