Cyberint’s Annual Report shows ransomware attacks reached 5,414 published incidents in 2024—an 11% increase from 2023. The fourth quarter alone accounted for 33% of all attacks. These numbers highlight two key issues: cyber threats are evolving quickly, and organizations need better security strategies to keep up.

Traditional security systems rely on signature-based detection and rule matching to identify known attack patterns. While effective for common threats, they struggle with complex attacks that span multiple systems and evade detection. The growing sophistication of cyberattacks makes it clear that security teams need a broader view of their environments—one that connects threats, vulnerabilities, and network activity in a meaningful way.

This is where cyber graphs come in. Unlike conventional security tools that analyze events in isolation, cyber graphs map relationships between users, devices, applications, and network activity. They provide a structured way to analyze attack paths, detect anomalies, and uncover hidden threats.

In this article, we break down the core concepts of cyber graphs, their components, real-world applications, and the challenges of implementation. We also look at how PuppyGraph connects security data from diverse sources to improve threat analysis.

What is a Cyber Graph?

A cyber graph is a security-specific graph model designed to track cyber threats, attack surfaces, and adversary movements. Unlike general security graphs, which may focus on log analysis or asset management, cyber graphs establish direct relationships between threat indicators, vulnerabilities, attack vectors, and system behaviors. Traditional SIEM (security information and event management) or rule-based systems operate on isolated event logs. On the contrary, a cyber graph maps relationships between different cybersecurity elements—users, devices, vulnerabilities, and threats—into a structured, interconnected model. Instead of relying on static event logs, it continuously links security signals in real time, making it possible to analyze risks dynamically, predict attack paths, and detect anomalies that traditional, tabular security data often misses.

How a Cyber Graph Works at a Structural Level

Cyber graphs store and process cybersecurity data as a graph database, where the data boils down to graph theory elements in the following manner:

• Nodes represent entities such as users, endpoints, cloud services, malware signatures, or security alerts.
• Edges define relationships like user authentication events, API calls, privilege changes, phishing attempts, or suspicious traffic flows.
• Properties contain metadata like timestamps, geolocation, authentication methods, access permissions, and historical behavior patterns.

For example, here’s how a simplified cyber graph schema for a corporate IT network might look:

(Attacker: Unknown) -[EXPLOITS]-> (Vulnerability: CVE-2024-1234)  
(Vulnerability: CVE-2024-1234) -[COMPROMISES]-> (Machine: Workstation-42)  
(Machine: Workstation-42) -[ELEVATES_PRIVILEGES]-> (User: Alice - Role: Admin)  
(User: Alice) -[ACCESSES]-> (Database: Sensitive_Customer_Records)  
(Database: Sensitive_Customer_Records) -[EXFILTRATES]-> (Threat_Actor: Suspicious_IP_192.168.1.23)  

It illustrates how an attacker exploits a specific vulnerability (CVE-2024-1234) to initially compromise a workstation. From there, the attacker escalates privileges to impersonate Alice, an admin user. Using Alice's credentials, the attacker accesses sensitive data, which is then exfiltrated to an external IP address (192.168.1.23). Representing the attack as a connected sequence of events helps security teams quickly identify how individual incidents relate, improving their ability to detect, trace, and interrupt the attack before more damage occurs.

As you can see, representing security data like this exposes relationships between seemingly unrelated security events. Analysts gain much better tooling to visualize attack chains, investigate security incidents faster, and predict potential breach scenarios.

Cyber Graphs versus Traditional Security Models

Most cybersecurity tools rely on event logs stored in traditional databases or SIEM systems. These logs capture security events as isolated occurrences without explicitly modeling the relationships between them. Analysts must then manually piece together events to understand attack sequences. Cyber graphs, by contrast, store and query security data explicitly as a graph, capturing relationships between entities and events. This interconnected graph structure allows security teams to quickly analyze risks, predict attack paths, and trace anomalies—tasks that are cumbersome or impossible when dealing with disconnected logs.

Here’s a quick comparison highlighting key differences between traditional (non-graph) and graph-based security models:

For example, consider an attacker who steals credentials and silently moves through the network. Traditional tools often record these actions as separate events—multiple login attempts, suspicious processes running, or network access logs—but lack the ability to quickly correlate them. In contrast, a cyber graph instantly reveals how these seemingly unrelated events connect, highlighting the attacker’s movements clearly and enabling faster, more effective response.

How Cyber Graphs Work

Cyber graphs continuously connect cybersecurity events, threats, and vulnerabilities into a unified, queryable structure. This real-time model enables teams to rapidly detect, investigate, and respond to threats that conventional tools overlook. The workflow typically includes these phases:

Data Ingestion and Integration

Cyber graphs continuously ingest security data from multiple sources, including SIEMs (Splunk, ELK), endpoint detection platforms (CrowdStrike, SentinelOne), network security devices (firewalls, IDS), and cloud security logs (AWS CloudTrail, Azure Security Center). Unlike traditional systems that treat data sources independently, cyber graphs integrate and normalize these diverse datasets into a single, unified structure in real time. During this ingestion, data is deduplicated, enriched with critical context (timestamps, geolocations, threat intelligence), and standardized into a consistent format. This integrated view forms the foundation of effective threat detection, providing security teams with the comprehensive, connected data needed for deeper analysis.

Graph-Based Data Storage

Once data is ingested, it’s structured in a graph database specifically optimized for cybersecurity workloads. Entities such as users, endpoints, vulnerabilities, malware, and network resources become nodes in the graph. Relationships between these entities—such as login attempts, network connections, privilege escalations, or exploitation attempts—are represented as edges, enriched with contextual metadata such as timestamps, access levels, and historical behavior patterns. This interconnected data structure is uniquely suited for cybersecurity analysis, allowing analysts to rapidly traverse relationships, uncover complex attack sequences, and efficiently identify anomalous activity that isolated logs or traditional databases would overlook.

Real-time Analysis and Threat Detection

Cyber graphs utilize specialized query languages, such as Cypher and Gremlin, to quickly analyze security data. Analysts use these languages to traverse interconnected data, uncover hidden attack paths, identify anomalous behaviors, and detect suspicious activities in real time. Graph-based anomaly detection identifies deviations like unauthorized privilege escalation, unusual lateral movement, or unexpected interactions between users and systems. Community detection algorithms cluster related security events, revealing coordinated attacks or unusual network activity. Additionally, integrating threat detection models into cyber graphs enables prediction of attack paths by analyzing patterns of past breaches.

For instance, consider this example query:

MATCH (u:User {role:'Admin'})-[:AUTHENTICATES]->(m:Machine)-[:RUNS]->(p:Process {name:'suspicious.exe'})
RETURN u.username, m.hostname, p.name, p.timestamp

This query instantly identifies any admin-level user who authenticated to a machine running a suspicious process. Traditional security systems log such events separately, obscuring their relevance. A cyber graph, however, immediately recognizes the significance by linking the suspicious process with a privileged user, helping security teams rapidly detect potentially compromised accounts and disrupt attacks before damage occurs.

Cyber graphs also enhance threat detection using graph-based anomaly detection and clustering techniques. For example, when a low-privilege account suddenly escalates permissions and accesses sensitive data, the cyber graph recognizes this deviation from typical behavior and flags it instantly. This real-time correlation of otherwise isolated events provides security teams with an accurate, contextual view, allowing for quicker identification and response to sophisticated, multi-step cyberattacks.

Visualization and Investigation Interface

Cyber graphs provide interactive visualization tools, enabling security teams to clearly interpret complex cybersecurity data. Analysts can visually trace attacker movements, understand relationships between compromised accounts and assets, and quickly identify abnormal behaviors or high-risk threats in real time. Instead of manually correlating logs, security teams gain intuitive dashboards that dynamically highlight anomalies, potential attack paths, and critical vulnerabilities. Because security incidents often unfold rapidly, cyber graph visualizations give analysts a faster way to pinpoint threats, prioritize their investigations, and respond effectively before incidents escalate.

Applications of Cyber Graphs

The core strength of cyber graph applications lies in data analysis and threat detection. Below, we explore how cyber graphs solve specific cybersecurity challenges through relationship-based analysis.

Threat Detection and Attack Path Analysis

Cyber graphs allow analysts to visualize relationships between users, endpoints, vulnerabilities, and threats. Instead of analyzing isolated alerts, cyber graphs link multiple security events, revealing hidden attack paths. For example, an attacker might initially compromise an endpoint and quietly escalate privileges across several systems. Traditional security tools may treat these as separate events, missing the broader pattern. Cyber graphs quickly correlate these incidents, helping teams proactively identify and disrupt attacks early.

Ransomware and Malware Campaign Detection

Cyber graphs effectively track malware propagation and ransomware spread. By mapping infected hosts, malicious processes, and command-and-control servers, cyber graphs quickly reveal malware behaviors and the scale of an infection. For example, if multiple workstations simultaneously encrypt files—a classic ransomware behavior—the graph immediately pinpoints the originating machine, enabling rapid isolation and containment.

Insider Threat and Behavioral Anomaly Detection

Detecting insider threats is challenging because malicious actions often mimic legitimate user behaviors. Cyber graphs address this by creating baseline behavioral patterns for users. When a user's behavior deviates significantly—for instance, suddenly accessing sensitive files or elevating privileges—the graph highlights this anomaly instantly. Suppose an employee starts accessing sensitive databases outside regular working hours; cyber graphs quickly detect this abnormal pattern and alert security teams to investigate potential insider risks.

Cloud Security and Misconfiguration Detection

Cloud environments constantly change, making traditional monitoring tools ineffective. Cyber graphs track dynamic relationships between cloud resources, user permissions, and configurations. They quickly identify risky misconfigurations, such as previously private storage buckets becoming publicly accessible. For example, a cyber graph can detect when sensitive customer data becomes publicly exposed due to IAM role changes or accidental policy adjustments, allowing rapid corrective action.

Incident Response and Digital Forensics

When security incidents occur, swift and accurate investigations are critical. Cyber graphs streamline this process by automatically connecting security events into clear visual attack timelines. Instead of manually piecing together logs, analysts can instantly visualize how attacks unfolded—from the initial exploit to data exfiltration. For instance, following a breach, a cyber graph quickly highlights the compromised accounts, exploited vulnerabilities, and impacted assets, significantly speeding up incident response and remediation.

Challenges in Cyber Graphs

While cyber graphs clearly enhance cybersecurity analysis, implementing them effectively comes with several practical challenges. Below are the key issues organizations often encounter when adopting cyber graphs.

Performance at Scale

Cyber graphs must handle enormous, fast-moving security data, such as authentication logs, network events, and threat intelligence. Graph traversal complexity grows rapidly as datasets expand, leading to slower query performance. Ensuring graphs perform efficiently at scale, without delays in queries or data updates, remains a critical challenge, especially as organizations increasingly adopt real-time security analytics.

Real-Time Data Processing and Latency

Cybersecurity requires analyzing threats in real time. However, large-scale graphs involve complex, multi-step queries, potentially introducing latency and slowing incident response. Graph traversal performance declines as datasets grow, complicating real-time threat correlation. Achieving consistently low-latency performance—especially during active security incidents—remains a significant challenge for security operations.

Data Quality and Consistency

Cyber graphs depend on accurate, standardized, and enriched data from multiple security tools, including SIEMs, endpoint agents, cloud logs, and threat intelligence. But integrating diverse data sources frequently leads to inconsistent formats, missing information, or unreliable timestamps. Resolving these inconsistencies through normalization and enrichment is critical, but it can significantly increase operational complexity.

Operational Complexity and Expertise Requirements

Deploying and maintaining cyber graphs require specialized technical expertise—knowledge that many security teams may not have in-house. Effective cyber graphs depend on precise schema design, query optimization, performance tuning, and maintenance routines. Organizations without dedicated graph experts may find implementation difficult, slowing adoption and limiting the technology's potential benefits.

Graph Model Evolution and Adaptability

Cyber threats evolve continuously, making it challenging for cyber graphs to remain accurate over time. Graph schemas must frequently evolve to accommodate new threats, vulnerabilities, and techniques. Security teams must constantly adapt their graph models and analysis methods without disrupting real-time detection or historical investigation capabilities. Balancing stability and adaptability remains a significant ongoing challenge.

Best Tools for Cyber Graph

Choosing the right cyber graph tool is essential to effectively address the challenges mentioned above. This section introduces PuppyGraph and four other prominent tools that help security teams analyze threats, visualize complex data, and discover vulnerabilities across their cybersecurity environment.

PuppyGraph

PuppyGraph is the first and only real time zero ETL graph query engine in the market empowering data teams to query existing relational data stores as a unified graph model in under 10 minutes bypassing traditional graph databases cost latency and maintenance hurdles. Perfect for cyber graph analysis PuppyGraph eliminates complex extract transform and load ETL processes letting security teams leverage existing relational data directly. This reduces duplication cuts storage costs and maintains a single consistent dataset delivering real time insights into assets permissions and vulnerabilities.

With a metadata driven approach using JSON based schema files PuppyGraph offers flexible graph structures enabling multiple perspectives like IAM trust relationships or network exposure paths without changing the underlying data. It excels in real time analytics reflecting the latest security configurations and spotting risks fast. Built for scalability it handles petabyte scale graphs ensuring quick queries even with billions of relationships. Plus its user-friendly web interface supports openCypher and Gremlin with interactive visualization making it easy to explore and analyze cyber graphs effectively. PuppyGraph is a top pick for cybersecurity pros seeking powerful efficient tools.

Neo4j

Neo4j is a native graph database platform designed for managing connected data in cyber graph applications. It stores cyber assets, permissions, and relationships as nodes and edges, supporting use cases like threat detection and network analysis. With its property graph model and visualization through Neo4j Browser, Neo4j enables security teams to explore and scale graphs to billions of nodes efficiently.

OWASP Amass

OWASP Amass is an open source tool for building cyber graphs through network mapping and attack surface discovery. It uses DNS enumeration and over 55 data sources to gather information on cyber assets and connections. Results are stored in a graph database, with basic visualization options, making it useful for security teams analyzing external exposures and relationships.

Maltego

Maltego is a cyber intelligence tool that creates cyber graphs by mining data from sources like social media and public records. It visualizes relationships between entities—such as users, systems, or threats—in dynamic graphs. With flexible querying and integration capabilities, Maltego supports security analysts in mapping and investigating cyber connections for threat analysis or asset tracking.

Linkurious

Linkurious is a graph visualization tool tailored for exploring cyber graphs, such as those representing network interactions or access patterns. It offers intuitive exploration with filtering, time analysis, and geospatial displays. Supporting large datasets, Linkurious helps security teams visualize and analyze relationships in cyber data, enhancing insights into potential risks or configurations through a user-friendly interface.

Conclusion

Security teams face increasingly complex threats that standard tools struggle to detect. Cyber graphs offer a way to visualize and analyze relationships between security data, revealing hidden attack patterns. Effective implementation requires a platform that manages large datasets and integrates with existing systems.

PuppyGraph addresses these challenges by providing fast, scalable graph analysis. This enables security teams to identify threats and respond quickly. If you want to evaluate yourself how PuppyGraph transforms security intelligence, download the forever free PuppyGraph Developer Edition, or book a free demo today with our graph expert team.