Cybersecurity threats are becoming increasingly complex and dynamic, requiring advanced data solutions to correlate and analyze connections at a real-world scale. A knowledge graph creates a digital twin of an organization's environment, enabling cybersecurity analysts to query and take action on it. The graph can be analyzed by data scientists to build models detecting malicious activities. Network infrastructures can be modeled as graphs, allowing for actionable insight and analysis. Knowledge graphs can be populated with metadata API services from cloud providers or third-party tools like Cartography and Bloodhound. This enables the creation of a digital twin of an organization's IT environment, which has tactical advantages in assessing changes to the environment, detecting potential security threats, and predicting attack paths. Graph databases like Neo4j provide a powerful platform for modeling, managing, and transforming ever-changing cybersecurity landscapes. They offer security at the database level through role-based access control and include developer tools for efficient query writing and visualization. With Neo4j's graph data science capabilities, organizations can analyze and gain insights from their graph datasets, identifying vulnerabilities and proactively applying relevant fixes.