Intelligent Threat Detection in Government

Whether created maliciously or unwittingly, insider threats can result in grave damage to national security interests. Traditional methods of detection rely on monitoring structured data, such as system access logs. However, critical insights that can help thwart an attack lay hidden in massive volumes of unstructured data. Unstructured data represents typically 80% of available information and typically remains unobserved and underutilized.

Cognitive computing provides the means to tap into this vast reservoir of knowledge to reveal hidden risks and behaviors. Combining natural language processing with advances in machine learning, cognitive computing provides the capability to establish continuous monitoring programs that surface anomalous indicators to detect insider threats.

This discussion will highlight how agencies are approaching insider threat as they take steps to secure our nation.

We’ll Discuss:

• Delineate the challenges your agencies are encountering with insider threat.
• Describe how cloud migration is impacted by insider threats.
• Explore managing insider threats in these scenarios: protecting data during eDiscovery, forensic investigations, and email migrations to the cloud.
• Explain the strengths and weaknesses of Security Continuous Monitoring and the Continuous Diagnostics and Mitigation program.
• Define insider threat hunting and its use in Department of Homeland Security.
• Review the surge of cyber threats from insider threats and how to prevent them.