How Harm Has Evolved (2021–2026)
AI misuse in India has not remained static. Four distinct phases are visible in the data, each characterised by new tools, new targets, and new scales of harm.
Where Incidents Are Concentrated
High-connectivity states dominate the top of the incident count — but no state is untouched. The concentration in urbanised, high-internet-penetration regions reflects both higher incidence and higher reporting rates. Rural and lower-connectivity states likely have significant under-reporting.
Haryana/Delhi NCR continues to lead, reflecting its role as both a tech hub and a target for sophisticated digital fraud. Maharashtra's rise to 21 incidents — now firmly second — reflects new documented cases including celebrity deepfakes, AI voice fraud, and high-value digital arrest scams (including a ₹10.3 crore CBI-probed case). Uttar Pradesh enters the top five for the first time, driven by documented digital arrest scams and deepfake political content. Assam and Andhra Pradesh now have verified, sourced entries for the first time following parliamentary source research.
Who Gets Hurt the Most
A substantial proportion of documented incidents involve harms specifically targeting women. Unlike financial fraud — where the goal is theft — the primary objective in gendered AI harm is reputational damage, coercion, or silencing. Key patterns:
- Explicit deepfakes created to shame victims or extract money through blackmail (sextortion)
- Political deepfakes targeting female candidates and elected officials to suppress participation
- Morphed images circulated in professional networks to damage career prospects
- AI-generated intimate content used as leverage in domestic abuse contexts
Critically, shame stops most victims from reporting. The social stigma attached to being the subject of explicit deepfakes — even when the victim is entirely blameless — means actual incidence is significantly higher than documented cases suggest.
Rapid COVID-era digital onboarding brought millions of first-time internet users online — many of them elderly or from previously unconnected communities — without accompanying safety literacy. This population became prime targets for voice cloning fraud and digital arrest scams.
- Elderly victims often believe the money is irretrievably gone and the system will not help — so they do not report
- Rural victims may not know cyber police stations exist or how to access the national cybercrime helpline 1930
- The shame of having been deceived creates an additional barrier to coming forward
Why Our Numbers Are a Floor
Our database almost certainly represents a significant undercount. Sextortion victims do not report due to shame. Elderly fraud victims believe the system will not help. Rural victims face access barriers to reporting channels. Every number in this database is a floor — the real scale of AI-enabled harm in India is larger, possibly by an order of magnitude.
This is not unique to India: under-reporting is a documented feature of AI harm globally. What makes the Indian context distinct is the scale of the digitally new population combined with the absence of accessible, trusted, local-language reporting pathways. Building those pathways is as important as documenting incidents after the fact.
India vs EU Governance Approach
India and the EU have taken fundamentally different approaches to AI governance. The comparison reveals both where India has moved quickly and where structural gaps remain.
Values That Should Guide the Response
Technical and legal solutions are necessary but insufficient. Five values should guide how India — government, platforms, civil society, and individuals — responds to AI harm:
The Delhi Declaration marks a significant moment: India has moved from a largely reactive domestic posture to active participation in setting the global norms for AI governance. How this translates into binding domestic regulation will be the key test over the next legislative cycle.