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Gurucul Fraud Analytics delivers a holistic, risk-based approach to fraud detection for both internal and external users, using purpose-built machine learning algorithms across open choice, big data infrastructures.
In finance, 58% said they have emailed company documents to their personal accounts
Detecting threats from insiders with access to company resources and data is one of the most difficult security challenges organizations face today, as most organizations default to trust and productivity
Gurucul Unified Security Analytics is based on 10 years of research and customer success best practices deploying machine learning (ML) and artificial intelligence (AI) techniques with security-specific big data.
One of the biggest challenges associated with threat hunting is the manual labor involved in piecing together data from various sources to trace the origin, tactics and techniques across different stages of an attack
Gurucul customers using the MITRE ATT&CK Framework confirmed that these new advanced behavior models have been able to detect unknown threats associated with high risk third parties including customers
In response to this new threat environment, the Zero Trust security framework has emerged to fill the void created by the disintegration of moat and castle architectures.
Being the only security and fraud analytics as a service offering selected as a finalist in this year’s SC Awards is an honor and validates the market need for our approach.
Insider threats are not limited to employees. They extend to contractors, supply chain partners, service providers and account compromise attacks that can abuse access to an organization’s assets both on-premise and in the cloud.