Whitepapers and Reports
This report provides a compass for Fraud Reduction Intelligence Platforms by evaluating specific criteria that enable businesses in solving some of their largest gaps. Gurucul is a leader across the board scoring high in: Scalability, App Integration, Analytics Engine, Bot Intel, Device Intel and User Intel.
At the 2020 RSA Conference, Gurucul conducted a survey about the risky behaviors of IT Security professionals. Nearly 300 attendees, across all main vertical markets, completed the survey. Download the report for details on what we learned.
The Cybersecurity Insiders’ 2020 Insider Threat Report, sponsored by Gurucul, reveals how IT security professionals are dealing with risky insiders, and how they are preparing to better protect their critical data and IT infrastructure from the growing insider threat.
Gurucul Risk Analytics provides an interesting approach that combines access governance, risk management and the detection of cyber threats. Unlike other solutions that focus on network traffic or technical vulnerabilities this solution focuses on identity, access and user activity to detect and prioritize risk.
Gurucul Network Traffic Analysis is a highly effective means to quickly identify suspicious or risky activity on a network. Network Traffic Analysis uses data that NetOps team are already collecting, so there is low overhead to deploying this solution.
Across all industries, fraud and financial crimes are on the rise, causing losses that collectively reach into the trillions of dollars each year. Legacy fraud management platforms have too many limitations to be effective in today’s environment. Gurucul Fraud Analytics provides a holistic risk-based approach for fraud detection. In many cases, the fraud can be detected in real time such that action can be taken to prevent loss from the fraudulent activity.
Hacker innovations continue to rise unabated alongside leveraging compromised accounts, while traditional signatures, rules and patterns within defenses offer minimal capability to detect them. User/Entity Behavior Analytics (UEBA) plays a key role in addressing these critical issues. Learn more about the top use cases for UEBA and the environments where industry leaders are deploying this technology.
With the compromise and misuse of identity emerging as a serious threat plane, the prospect of preventing data exfiltration through phishing and social attacks has become an amplified and urgent concern. Heightening security leaders’ alarm is the realization that IAM (identity and access management) has outlived its standalone usefulness, and that a profound discovery gap exists with privileged access, where the majority of these access entitlements are unaccounted for in most organizations.
Cloud security analytics (CSA) has greatly expanded the breadth of use cases within advanced security analytics. This range of use case categories within advanced security analytics should be one of the first factors prospective customers should examine when considering adoption of an advanced security analytics solution. The scope of this white paper addresses the specific use cases of cloud security analytics.
Gurucul provides a robust security and fraud analytics platform which leverages advanced machine learning algorithms to detect fraud. Read this whitepaper to understand how Gurucul can detect and prevent financial cyber frauds, including: Anti-Money Laundering (AML), Account Take Over & Login Fraud, Transaction Fraud, Credit Card Fraud, Payment Fraud, Mobile Fraud and Customer Service Representative (CSR) Fraud.
Gurucul provides advanced security analytics to address a broad range of security issues facing healthcare providers and payers. Read this whitepaper, Healthcare Payers & Providers – Key Security Analytics Use Cases, for details.
While most organizations put their focus on defending against and detecting cyber attacks, a more insidious threat is on the rise. Information security (InfoSec) professionals say that insider attacks are far more difficult to detect and prevent than external attacks, and insider threats have become more frequent in the past year.
With the deadline looming, organizations are beginning to tool up to comply with the European Union General Data Protection Regulation. Failure to do so for all companies interfacing with any private citizen of the EU’s data will have stiff financial consequences. This white paper explores the aspects of user and entity behavior analytics (UEBA), along with identity analytics (IdA), and how they work to address a critical component of the GDPR requirements.
A behavior analytics solution’s capability for the delivery of risk scores with automated risk response has become a critical component for a number of forward-looking security leaders. The acceptance of feedback and data in a closed-loop deployment, as well as a vendor’s ability to facilitate customized use case requirements are key to addressing these needs.
User and Entity Behavior Analytics (UEBA) solutions monitor user and entity behavior in corporate networks detecting anomalies indicating potential threats from behavior profiles and patterns by applying algorithms, statistical analysis, and machine learning techniques.
Identity theft is a high stakes growing concern for organizations yet as the risks increase, employee awareness of the danger remains low. Add to that, modern data attackers employ unconventional methods to deceive users into becoming victims of account compromise.
With modern day insider threats on the rise and privileged access widely acknowledged as a prime target of hackers, customers employing traditional security solutions are recognizing growing challenges: the inability to discover both unknown privileged access and privileged access abuse.