Learn how Gurucul delivers real-time visibility and detection, prioritized investigations, and automated response across the entire SOC lifecycle with a unified platform.
By leveraging Artificial Intelligence and Machine Learning on massive volumes of data in a vendor agnostic data lake, Gurucul Next-Gen SIEM delivers all the features expected from a SIEM platform and adds capabilities that no conventional SIEM platform can match.
Gurucul Extended Detection and Response (XDR) is a cloud-native analytics driven XDR platform that improves threat detection and incident response with no vendor lock-in, allowing you to use best-of-breed security solutions. It provides a single interface for analytics, detection, investigation and response.
Behavior-based machine learning for identity and access often results in radical reductions for accounts and access entitlements. Identity is a threat plane requiring identity and access data science that predicts and prevents security risks, assists with high privilege access monitoring, excess access, compliance, and intelligent provisioning.
Gurucul Fraud Analytics provides a holistic risk-based approach for fraud detection of both internal and external users, using award-winning machine learning algorithms and an open big data architecture.
Identity as a threat plane is further amplified by cloud apps to detect insider threats, account compromise and fraudulent activity. Cloud apps require both identity access intelligence and user behavior analytics to reduce the attack surface for accounts, unnecessary access rights and privileges, and identify, predict and prevent breaches.
Create custom machine learning models without coding and minimal knowledge of data science. Gurucul STUDIOTM provides a step-by-step graphical interface to select attributes, train models, create baselines, set prediction thresholds and define feedback loops.
Investigate incidents quickly with Gurucul MinerTM. Only Gurucul offers contextual search using big data to mine linked users, accounts, entitlements, structured and unstructured data, along with risk score and peer group analytics.
Manufacturers develop cheaper and more scalable medical devices that run easily compromised operating systems, such as Windows, that are frequent targets of ransomware attacks.
Gurucul provides a comprehensive view of user and entity behaviors and detects risky outliers using a library of advanced machine learning models and identity-centric data science.
Gurucul has compiled the top twelve key use cases customers should build into their Insider threat program roadmap when deploying and rolling out their Gurucul Risk Analytics (GRA) platform.
Whether you have experience with a legacy SIEM tool or are deploying a tool for the first time, there are some best practices to follow to get the most benefit from a modern analytics-driven SIEM.
Streamline user access to digital content with risk based authentication. Enable real-time access decisions based on risk scores generated from Gurucul’s machine learning behavior analytics on big data.
Enjoy state-of-the-art program governance powered by Gurucul Identity Analytics. Our advanced machine learning algorithms and pattern matching expressions link identity and access to build contextual visibility across an enterprise’s entire hybrid environment.
Existing tools may be limited by a lack of context in that they do not see the whole picture. Deploying Gurucul Fraud Analytics can give an organization the comprehensive view necessary to identify aberrant behaviors and stop fraud.
Gurucul supports API based integration with Cortex XSOAR that allows the system to perform an on-demand retrieval of Gurucul’s data and create incidents.
Gurucul Risk Analytics uses machine learning and predictive anomaly detection algorithms to reduce the attack surface for accounts, and to eliminate unnecessary access rights and privileges.
Gurucul SmartStartTM is an easy and hassle-free installation service for the implementation of Gurucul’s Unified Security and Risk Analytics platform and products. This service deploys out-of-the-box anomaly detection with minimal customization that delivers consistent, predictable outcomes.
Gurucul NTA uses entity models to create behavior baselines for every device and machine on the network based on network flow data such as: source and destination IPs/machines, protocol, bytes in/out, etc.