CyberSage overview

Overview

CyberSage is an advanced graph-based inference engine designed to automate complex cybersecurity tasks such as threat modeling, attack path discovery, and breach detection. By mimicking human expert reasoning through chained logic and dynamic knowledge integration, it surpasses traditional rule-based systems, enabling organizations to enhance security postures efficiently and accurately.

Key Benefits

0. Handles Complex Logic Beyond Traditional Rule-Based Solutions

CyberSage's inference engine excels at managing intricate, interdependent logic that static rule-based tools like Splunk or AWS Security Hub struggle with, as those systems rely on simple if-else conditions and predefined patterns. For instance, in replicating the Capital One (Cap1) breach of 2019, CyberSage can dynamically discover attack paths by correlating misconfigured AWS IAM roles, SSRF vulnerabilities in web applications, and lateral movement through exploited metadata services. This involves forward-chaining inference—where initial conditions (e.g., unauthorized API access) trigger subsequent evaluations (e.g., data exfiltration risks influenced by network segmentation and encryption states)—uncovering hidden pathways that rule-based alerts might miss due to their inability to handle exponential complexity or adaptive threat sequences.

1. Automated Knowledge Fetching from Documentation

Unlike manual processes that require security teams to painstakingly extract and encode rules from technical documents, CyberSage directly ingests and processes knowledge from sources like system architecture diagrams, compliance guidelines (e.g., NIST SP 800-53), or vendor documentation. This eliminates human effort in rule creation, reduces errors from misinterpretation, and ensures up-to-date inferences. For example, when assessing ransomware propagation risks, it can pull endpoint protection details from vendor PDFs to infer how a phishing entry point might lead to network spread, factoring in variables like patch levels and user privileges without any manual input.

2. Direct Integration with External Systems via API Calls

CyberSage fetches real-time data through API integrations with tools like SIEM systems (e.g., Splunk API), identity providers (e.g., Okta), or vulnerability scanners (e.g., Nessus), bypassing the need for human interviews or manual data gathering. This removes subjectivity, minimizes errors from incomplete recollections, and enables seamless automation. In an insider threat detection scenario, it could query login APIs for anomalous patterns (e.g., unusual access times combined with device changes) and correlate them with HR systems for role-based entitlements, identifying subtle exfiltration attempts that might otherwise require expert forensic interviews.

3. Codeless Configuration for Non-Technical Users

With an intuitive, drag-and-drop interface, CyberSage allows business analysts and security consultants to build and deploy inference models without involving software developers. This democratizes advanced cybersecurity automation, speeding up implementation and reducing costs. Analysts can visually construct graph-based flows—such as linking business functions (e.g., fund transfers) to threats (e.g., CSRF) and mitigations—using pre-built nodes for logic, data fetches, and decisions, making it accessible for rapid prototyping in compliance-driven environments like PCI-DSS assessments.

4. Real-Time Adaptive Threat Correlation Requiring Human-Like Analysis

CyberSage automates the correlation of multiple events from SIEM or log sources to detect potential breaches that demand complex decision-making or AI integration, far beyond simple if-else logic. For use cases like advanced persistent threats (APTs), it chains events such as initial reconnaissance logs, privilege escalations, and lateral movements influenced by contextual factors (e.g., endpoint vulnerabilities and behavioral anomalies), adapting in real-time to new data. This replicates human analyst intuition—e.g., weighing probabilistic risks in ransomware spread or insider threats—reducing detection times from days to minutes and freeing experts for strategic tasks, while scaling to handle vast log volumes without performance degradation.