Empowering Threat Hunting with the Cybermonic Knowledge Graph
In the ever-evolving landscape of cybersecurity, threat hunting plays a critical role in proactively identifying and mitigating potential risks. To enhance the effectiveness and efficiency of threat hunting, organizations are turning to advanced technologies like knowledge graphs. In this blog post, we will explore the concept of knowledge graphs and discuss how leveraging the Cybermonic Knowledge Graph can empower security teams to bolster their threat-hunting capabilities.
Understanding Knowledge Graphs
A knowledge graph is a powerful tool that represents information in a structured manner, connecting various data points and their relationships. It serves as a foundation for organizing, analyzing, and extracting insights from vast amounts of data. By mapping out entities, attributes, and their connections, knowledge graphs enable a holistic view of information and facilitate advanced analytics.
The Power of the Cybermonic Knowledge Graph
The Cybermonic Knowledge Graph is a specialized resource that encompasses a wide range of cybersecurity-related data, including threat intelligence, vulnerabilities, attack patterns, and indicators of compromise (IOCs). By consolidating and linking this information, the Cybermonic Knowledge Graph offers a comprehensive understanding of the threat landscape, enabling security teams to effectively hunt for potential threats.
1. Contextual Insights
The knowledge graph provides valuable context by interlinking different entities and their attributes. Security analysts can leverage this contextual understanding to identify potential attack vectors, detect patterns, and make informed decisions during the threat-hunting process. By uncovering hidden relationships between threat actors, campaigns, and targeted assets, the knowledge graph facilitates a proactive and comprehensive approach to threat detection.
For example, see our blog post titled “Are you getting the most out of your threat intelligence?” where we discuss the challenges of full correlation across all data sources such that threat intelligence is fully utilized. When we ingest data into the Cyber Knowledge Graph, we can achieve that full correlation, meaning that analysts will have complete visibility into what activity is occurring related to threat intelligence.
2. Cross-Domain Analysis
One of the key advantages of the Cybermonic Knowledge Graph is its ability to bridge gaps between different domains of cybersecurity. It consolidates information from various sources, such as threat intelligence feeds, vulnerability databases, user behavior, and security alerts, into a unified framework. This cross-domain analysis allows security teams to correlate seemingly unrelated data points, leading to more accurate threat identification and faster response times.
For example, see our blog post titled “Obstacles in understanding a security event” where we discuss the challenges of performing cross-domain analysis. Our system is able to take disparate data feeds, such as email logs, cloud authentication logs, and endpoint logs, and stitch together meaningful relationships that allow for quick and efficient analysis of, for example, a phishing event involving a malicious email, malicious URL, and a malicious logon event.
3. Automated Insights and Recommendations
By leveraging machine learning and artificial intelligence techniques, the Cybermonic Knowledge Graph can generate automated insights and recommendations. These intelligent capabilities help analysts uncover hidden threats, prioritize investigations, and even suggest mitigation strategies based on historical data and patterns. This automation augments the efficiency and effectiveness of threat hunting, enabling analysts to focus on high-priority threats and strategic decision-making.
4. Continuous Learning and Adaptability
The knowledge graph is not static; it continuously evolves and adapts to incorporate new threat intelligence and emerging trends. This iterative learning process ensures that security teams have access to the most up-to-date information, empowering them to stay ahead of adversaries and respond effectively to evolving threats.
The data ingest is based on a streaming model and updates at near-real time speed. The graph analysis and incident generation is continuously running, analyzing new data as it comes in, and generating incidents. There are various reporting modules that can be configured to generate emails, Slack messages, or other forms of notifications when critical events occur.
Summary
Threat hunting is a complex and critical activity in cybersecurity, requiring advanced tools and techniques to stay one step ahead of adversaries. The Cybermonic Knowledge Graph offers a robust solution for enhancing threat-hunting capabilities. By leveraging its contextual insights, cross-domain analysis, automated recommendations, and continuous learning, security teams can proactively identify and mitigate threats. Integrating the power of the Cybermonic Knowledge Graph into threat-hunting workflows enables organizations to bolster their cybersecurity posture and protect their digital assets in an increasingly challenging threat landscape.
About Cybermonic
Cybermonic was founded by researchers with PhDs in graph systems, graph analytics, graph AI/ML, and a track record of DARPA funded research on cybersecurity challenge problems. They have perfected their graph systems and graph algorithms in order to supercharge cybersecurity analysts.