All companies in the retail sector face serious cyberthreats. Whether it’s via malware on POS terminals, a hack on a web application, or some other type of attack, retailers risk exposing customer payment data. It’s the most-sought-after and lucrative commodity that threat actors want because it can be monetized quickly and easily.
The PCI DSS regulations provide a guideline for retailers to protect the payment data in their care. However, according to the Verizon 2020 Payment Security Report, only 27.9% of organizations achieved full PCI compliance in 2019, continuing a downward trend from previous years. But compliance alone is not security. Retailers need to balance the demands of fully securing their environment and data with PCI DSS compliance requirements.
- Monitor Point of Sale (POS) devices to detect fraudulent transaction attempts or insider theft
- Detect in-store employees who are abusing system access for personal gain
- Stop account takeover breaches by detecting suspicious / unseen web and mobile devices in real-time
- Provide out-of-the-box PCI-DSS compliance for monitoring access to network resources and cardholder data
- Detect and prevent POS fraud
- Identify credit card skimming
- Eliminate online payment fraud
- Curtail call center fraud
The Gurucul platform builds a baseline and digital footprint of each identity in the system. At the core of the platform are self-learning, self-training, contextually aware algorithms which score every transaction as they are evaluated near real time. Using machine learning techniques, Gurucul profiles behavior by evaluating transactions against a normal baseline, using pattern recognition, and compares it with dynamically defined peer groups. In addition, the platform runs each transaction through anomaly detection algorithms and threat libraries to evaluate and predictively model anomalous behavior patterns. All this contributes to detecting fraudulent behaviors and transactions from attackers, employees, and supply-chain partners.
TOP RETAIL USE CASES
Ransomware is a huge threat to retailers worldwide. According to the Sophos report The State of Ransomware in Retail 2021, 44% of retail organizations were hit by ransomware in the last year. The average bill for rectifying a ransomware attack in the retail sector was US$1.97 million. Gurucul’s risk analytics engine prevents these fileless attacks by stopping the malicious payload at download. Even if it were to successfully download onto an endpoint, the additional steps of running scripts and running exploits and attacks in memory can be stopped.
Credit Card & Online Payment Fraud
Gurucul uses a combination of supervised and unsupervised algorithms to detect outlier risky behavior indicative of credit card fraud such as: unusual account / profile changes, abnormal high value transactions, geo-location anomalies, unusual device usage, suspicious charges from merchants, etc.
Insider Threat Detection & Deterrence
Insider threats are a potent risk for every retailer. Whether an employee, service partner, or supply chain partner is malicious, careless, or accidental, a data breach can be costly and can lead to brand damage and loss of market share. Gurucul uses behavioral identifiers bolstered by pre-built machine learning models and artificial intelligence to accurately detect and mitigate insider threats before damage is done.
Gurucul monitors and reports on logs of all system components that store, process, or transmit CHD and/or SAD. The platform also monitors and report on logs of all servers and system components that perform security functions (firewalls, intrusion-detection systems/intrusion-prevention systems (IDS/IPS), authentication servers, e-commerce redirection servers, etc.).