“A positive and enabling digital future is integral to a truly empowered and inclusive society. Such a future can only be built through constructive collaboration and continuous dialogue among key stakeholders. It is imperative for industry to make the required investments to build a sustainable digital ecosystem and maintain citizen’s trust through transparent and responsible conduct with regard to privacy and data.”
What Bharti Airtel is doing
Examples demonstrating areas in which Airtel is addressing specific challenges relating to digital future principles
Security of Customer Data is paramount at Airtel and hence Airtel uses Enterprise Standard Solutions to ensure safety and security of data as well as infrastructure.
Measures include risk assessment of processes and infrastructure used during the entire lifecycle of data, Privileged Access Management for monitoring privileged accounts and regulatory compliance, User Behavior Analytics to monitor deviation from baseline behavior, Authentication and Authorization, Data Encryption, the use of firewalls and security patches and thorough Vulnerability Audit.
Big Data for Social Good
UN SDG target – Ending TB epidemic by 2030 – India has committed to an even more ambitious target to end TB by 2025. Meeting this commitment requires innovative strategies and technologies.
Bharti Airtel (Airtel) and Be [email protected], Be Mobile (a joint initiative between WHO and the International Telecommunications Union), together with the GSMA, are seeking to understand how the big data generated by mobile networks could provide relevant insights.
Together, the parties developed a proof of concept (PoC) in the Indian states of Uttar Pradesh and Gujarat, which uses mobile network data to help pinpoint geographical locations at risk of increasing TB incidence.
PoC used anonymised, aggregated mobile network data showing regular population movements (such as commuting, attending education and other habitual daily journeys) provided by Airtel. The scale, granularity and immediacy of mobile data enabled the identification of areas that have low TB incidence rates, but are highly connected to areas with high TB incidence.
Statistical analysis showed that regular population movement is a stronger indicator of TB incidence than location proximity between high and low TB regions. Understanding these patterns makes it possible to implement targeted prevention, diagnosis, and adherence measures in these areas, and to gain new insights, to better address TB.