Network Graphs for Precision Targeting

Data Aggregation and Actionable Intelligence Generation for Network Collapse Applications

The Problem: Internationally Networked Threat Groups Produce Massively Fragmented Data

Law enforcement, intelligence agencies, militaries and critical national infrastructure providers face an exponentially increasing number of geographically distributed, networked, and highly adaptive threat groups. The investigations into these groups often result in unpredictable yet exponential increases in data volume, complexity and variation. While the types of hostile activities range widely, the threat domains with the most recently observed acceleration are:

• Subversion operations

• State-sponsored synthetic narcotics trafficking

• Financial sanctions evasion

These groups operate seamlessly across national borders and generate often-fragmented, disparate data trails behind them in faint signal form. For example, in recent years we have witnessed continuously expanding interoperability between organized criminal syndicates and terrorist groups, hostile states and non-state proxies, and even the infiltration, subversion, and hostile nation-state capture of key domestic and international institutions across the world.

Whether someone is a criminal investigator, intelligence analyst, uniformed force commander or compliance director at an investment bank, these developments pose a range of network mapping and targeting challenges. Previous notions of ‘red lines’, or actions that a threat group is perceived to be risk-averse to taking, demonstrably no longer have validity in the current threat environment. This has been evidenced by the increasingly number of direct actions that are being witnessed and the patterns that are being discovered and assessed by those working at the cutting edge of the field of multi-domain network graph generation.

See below Network Graph example developed leveraging Data Abyss (https://www.dataabyss.ai/)

Network Graphs for Precision Targeting

Data Aggregation and Actionable Intelligence Generation for Network Collapse Applications

The Problem: Internationally Networked Threat Groups Produce Massively Fragmented Data

Law enforcement, intelligence agencies, militaries and critical national infrastructure providers face an exponentially increasing number of geographically distributed, networked, and highly adaptive threat groups. The investigations into these groups often result in unpredictable yet exponential increases in data volume, complexity and variation. While the types of hostile activities range widely, the threat domains with the most recently observed acceleration are:

• Subversion operations

• State-sponsored synthetic narcotics trafficking

• Financial sanctions evasion

These groups operate seamlessly across national borders and generate often-fragmented, disparate data trails behind them in faint signal form. For example, in recent years we have witnessed continuously expanding interoperability between organized criminal syndicates and terrorist groups, hostile states and non-state proxies, and even the infiltration, subversion, and hostile nation-state capture of key domestic and international institutions across the world.

Whether someone is a criminal investigator, intelligence analyst, uniformed force commander or compliance director at an investment bank, these developments pose a range of network mapping and targeting challenges. Previous notions of ‘red lines’, or actions that a threat group is perceived to be risk-averse to taking, demonstrably no longer have validity in the current threat environment. This has been evidenced by the increasingly number of direct actions that are being witnessed and the patterns that are being discovered and assessed by those working at the cutting edge of the field of multi-domain network graph generation.

See below Network Graph example developed leveraging Data Abyss (https://www.dataabyss.ai/)