New Delhi, July 19 (UNI) Union Home Minister Amit Shah on Friday called for greater synergy between all agencies to dismantle the terror networks and their supporting eco-system, to address the evolving security threat scenario of the country.
Shah chaired a high-level meeting here with various heads of security and law enforcement agencies to review the functioning of Multi Agency Centre (MAC) of the Intelligence Bureau (IB), responsible for tackling security challenges in the country.
Addressing the meeting, Shah instructed various heads of security agencies from across the country and other intelligence and enforcement agencies to adopt a whole-of-the government approach towards national security, a Home Ministry statement said.
While reviewing the overall internal security situation in the country and the fight against terrorism, he impressed upon all the participants to increase engagement in the Multi Agency Centre and make it a cohesive platform that brings together all the law enforcement agencies, anti-drug agencies, cyber security and intelligence agencies, for decisive and prompt action.
The Home Minister emphasized that MAC has earned the trust of its constituents and it must continue to work 24X7 as a platform for pro-active and real-time sharing of actionable intelligence, amongst various stakeholders, including last-mile responders.
During the meeting, the Home Minister also stressed on constituting a team of young, technically proficient and passionate officers drawn from all agencies involved in national security to dismantle the terror ecosystem utilizing Big Data and Al/ML driven analytics and technological advancements.
He reiterated that in the face of new and emerging security challenges, we must always be one step ahead in our responses.
Shah also informed that under the leadership of Prime Minister Narendra Modi, the MAC framework was poised to undergo a major technical and operational revamp to increase its reach and effectiveness.
He exhorted all the stakeholders to bolster these efforts further through prompt responses and aggressive follow up of shared inputs.
UNI RBE RC