Indian entities are expected to lose Rs 20,000 crore due to cyber crimes this year, cyber security intelligence firm CloudSEK said in a report.
The company claims to have done a detailed analysis of 200 companies across sectors, over 5,000 domain takedowns, data of about 16,000 brand abuse, official data from the Indian Cyber Crime Coordination Centre (I4C) documenting nationwide cybercrime patterns and financial impacts etc for artificial intelligence based projection.
"The biggest revelation from our research is the sheer scale of financial losses of over Rs 20,000 crore due to cybercrime, of which Rs 9,000 crore is due to brand name abuse alone. The fact that brand abuse is implicated in nearly one-third of all cybercrime incidents and a staggering 70 per cent of high-value scams is a wake-up call for India," Pavan Karthick M, Threat Intelligence Researcher at CloudSEK said.
Cybercrime complaints are expected to exceed 25 lakh, with 5 lakh explicitly mentioning brand impersonation.
"The white paper builds upon previous research documenting losses of Rs 11,333 crore in the first nine months of 2024 by I4C. By applying advanced predictive modeling techniques to comprehensive datasets, we project the trajectory of these losses into 2025, providing stakeholders with actionable intelligence to combat this growing threat," the report said.
According to the analysis done by CloudSEK Nexus platform , banking and financial services will bear the maximum brunt of the losses of around Rs 8,200 crore, followed by retail and e-commerce of Rs 5,800 crore and government services Rs 3,400 crore.
"Investment scams (pig-butchering) and advance-fee scams are the primary drivers of financial losses," the report said.
Fraudulent domains (website names) are projected to increase by 65 per cent, while fraudulent apps are expected to surge by 83 per cent, particularly in financial services, the report said.
(Only the headline and picture of this report may have been reworked by the Business Standard staff; the rest of the content is auto-generated from a syndicated feed.)
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