Cypienta provides a context layer that consolidates security telemetry by grouping related events, alerts, and logs based on causality and similarity. This semantic consolidation significantly reduces SIEM ingest volume and associated costs while providing richer, interconnected data units for analysis. The platform enhances the efficacy of SIEM, SOAR, and AI tools by delivering contextualized insights instead of raw, noisy data.
Funding
Funding not disclosed
Founders
Product
Problem
Security operations centers (SOCs) are overwhelmed by the volume of alerts, events, and logs, making it difficult to identify and prioritize coordinated attacks. Analysts often miss critical attack traces hidden within weak signals, leading to delayed incident response and increased mean time to resolution (MTTR). Traditional detection methods relying on rules and playbooks struggle to keep pace with evolving threat actor tactics.
Solution
Cypienta provides an AI-powered platform that automatically correlates and contextualizes security signals to expose attack kill chains in real-time. By fusing alerts, events, and logs using a Knowledge Graph Correlation Engine and Natural Language Processing, Cypienta provides a holistic view of attacks, enabling analysts to prioritize and respond to the most critical threats. The platform emulates the decision-making of a human cyber security expert to stitch together related signals into causal attack kill chains and attributes kill chains to threat actors using a Deep Generative Neural Network. Cypienta's probabilistic model then utilizes threat actor intelligence, environment threat modeling, and attack kill chain insights to predict the next logical step in the attack.
Target Audience
Cypienta is designed for security analysts, forensic investigators, incident responders, SOC engineers, threat hunters, security managers, and partners within security operations centers (SOCs) of medium to large enterprises and managed security service providers (MSSPs).
Features
- Complex Event Processing automatically fuses alerts, events, and logs that carry similar information.
- Natural Language Processing pipelines contextually determine what MITRE ATT&CK technique is reflected in each signal.
- Knowledge Graph Correlation Engine mines relationships between alerts, events, logs, vulnerability scans, and threat intel.
- Expert System emulates the decision-making of a human cyber security expert, and stitches clusters of signals into coherent and causal attack kill chains.
- Deep Generative Neural Network recognizes threat actors by their choices of threat vectors, techniques, attack strategies, and hands-on-keyboard behaviors.
- Probabilistic Model utilizes threat actor intelligence, environment threat modeling, and attack kill chain insights to predict the next logical step in the attack.
- Automatically generated STIX2 makes sharing the attack flow, patterns, sequences, and indicators easier.