DoD taps up Torch.AI to strengthen cyber security capabilities

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The US Department of Defense (DoD) has struck a deal with data infrastructure AI expert Torch.AI to implement a new insider threat analysis capability under the moniker System for Insider Threat Hindrance (SITH).

“Insider threats are one of the primary reasons for data record breaches, which are growing at nearly 200% per year,” stated Torch.AI in a press release.

Building on the firm’s NEXUS software, the new SITH system combines disparate, complex government and commercial datasets and automates key tasks using advanced machine learning to bolster threat detection.

NEXUS by Torch.AI also synthesizes data objects while rapidly connecting source systems — without human intervention — to make data machine-readable instantaneously. The objectized data helps SITH circumvent limitations imposed by system-by-system data integrations, migrations, and duplications, all of which are a hindrance to data analysis.

In stark contrast, Torch.AI utilizes a flexible data mesh to integrate new data rapidly, accurately, and continuously without disrupting an agency’s processes and mission.

Per reports, Torch.AI developed SITH for Defense Counterintelligence and Security Agency (DCSA) with support from the Chief Digital and AI Office's (CDAO) Directorate for Digital Services (DDS).

"There are few situations where the quality and availability of data is more important than cyber and insider threat. It's a huge problem. It's getting worse every day," said Brian Weaver, founder and CEO of Torch.AI.

"Torch.AI software is mission-ready today. We are proud the DoD has selected our solution for this critical capability and the critical part we are playing to protect our warfighters and enhance our nation's security and economic viability," added Weaver.

Earlier this month, the DoD added new members to its Defence Business Board. The board offers the secretary and deputy secretary perspectives and lessons from the private sector unavailable within the Department of Defense otherwise.