Open-Source Intelligence Can Help Fight Organized Retail Crime

Organized retail crime (ORC), the large-scale theft of retail merchandise with the intent to resell the items for financial gain, is on the rise. Criminal enterprises are employing expert individuals and strategic networks to steal massive quantities of merchandise from brick and mortar and virtual retailers. In recent months, big-box retailers such as Lowe’s and Home Depot have been the primary targets of such attacks. With the holiday season upon us, this will only get worse.

With the advancement of technology, crime rings are frequently unloading their stolen goods through online sale sites and other digital forums, allowing them to hide behind the internet without fully revealing their identities.

The Impact of Organized Retail Crime

According to a recent report from the National Retail Federation, there are over $700,000 in ORC losses for every billion in sales and roughly 60% of retailers are prioritizing ORC more than they were five years ago. ORC is distinct from conventional shoplifting committed by individuals seeking goods for personal use in that it is far more strategic, invasive and systemically organized. Today’s ORC thieves are employing numerous methods including cloning or stealing gift cards to obtain merchandise, store credit or cash.

Big-box retailers have been dealing with ORC for many years, but the rise in “smash-and-grab” ORC instances across the nation has brought increased media and c-suite attention to the topic. Retail loss prevention teams facing security-related challenges on many fronts need accelerated intelligence solutions to clarify the cloudy scope of retail crime groups’ activities, which can be national or even international in scale.


Fighting Organized Retail Crime with Threat Intelligence

The rise of online media has increased retail fraud and organized theft rings, focused on large volume loss to retailers. Criminals are resorting to the most well-known platforms as well as newer, niche and less regulated avenues to share information and tactics from local events to consumer-targeted phishing attacks. Simultaneously, the dark web has grown to become a vast open market for cybercriminals. Every day, new reports of massive data breaches, scamming or cybercrimes occurring across the country are putting strains on our most favored retail brands.

In such an environment, a proactive approach is needed to deter offenders and enhance investigative capabilities. That’s where the latest open-source technology comes into play to help protect retail inventory. Automated open-source intelligence enables analysts and investigators to collect, analyze and interpret vast amounts of data sets that would otherwise be insurmountable through manual analysis and monitoring alone. Loss prevention specialists and law enforcement officials need automated tools for monitoring of online dark web ‘stores’ to determine where stolen goods are being sold to interdict and stop this illicit activity.

While open-source intelligence has been a key strategy for decades, especially in the intelligence world. The automation of many processes – searching, analyzing and monitoring – is critical today due to the volume of data generated online. No one person, or even a team of people, can manually search for stolen goods or online chatter about this type of theft in a timely and efficient manner. Automated tools are needed to keep loss prevention specialists, analysts and investigators focused on finding these threat actors and stopping this type of large scale theft.

Four Situations Where Automated Opens Source Intelligence Can Interdict Organized Retail Crime

With data from hundreds of online sources, open source intelligence tools supports the fight against real-world retail security risks as demonstrated in the scenarios below.

High End Large Volume Thefts

Loss prevention specialists and analysts can quickly gathers brand-relevant information from mainstream online media platforms, less-regulated and more obscure platforms, as well as the deep dark web. Automated tools allow users to monitor for brand names, specific product names and hashtags that may indicate illicit activity. Automated tools scan and monitor online sites where stolen items are listed for sale and can also find this content in online user posts bragging about thefts and other illicit behavior. Dark web discussion groups and forums can also be monitored for terms related to theft, shoplifting and mass retail exploitation. This information helps retailers adapt their online and in-store security practices in a timely fashion. It can also be used in ongoing investigations to locate suspects, and support law enforcement in their search efforts to recover stolen goods.

Gift Card Fraud

Like other forms of theft, information on potential or existing gift card fraud is often shared through online media, less regulated platforms, and the deep, dark web. This data includes information about how to create and use fraudulent cards, applicable marketplace listings and activity. With this information, big-box retailers can better understand fraudsters’ strategies and make the necessary adjustments to increase gift card security, online and offline.

Store scams

Automated tools can search for information online regarding how to perpetrate scams through message boards and online forums. Armed with this information, security personnel can alert store managers and employees on these nefarious activities that can distract employees, allowing thieves to make off with large volumes of merchandise. This information can also be used to educate store employees on how to handle returns and other activities that can indicate fraudulent practices against the store. With this data, retailers are better equipped to improve security practices to protect merchandise.

Cybersecurity

Automated open-source intelligence searches online media and the dark web, in addition to paste sites and breached data repositories, for potential threats to systems and store security. To effectively identify breaches, DDoS attacks, scams and other cyberattacks or imminent threats, open-source intelligence can be integrated into other data and analysis from security personnel to boost its findings and improve security practices. Through continuous monitoring or dark web forums and other media sites, security professionals can alert retailers if information is found that can expose company systems and data to attacks. During the holiday season, this is critical to continuously monitor to ensure breaches and cyberattacks are stopped prior to system penetration.

Open Source Intelligence is the Key to Stopping Organized Retail Theft

During a crisis scenario – or on a regular day – it’s impossible to manually sift through billions of online posts, sale listings and discussions to locate the vital information that retailers need to protect their brand and assets. While there are certain search and tracking tools that can help to reduce noise, at the end of the day, it’s like using an aspirin to stop the spread of disease.

An AI-based open-source intelligence solution allows big-box retailers to access the data and insights they need through a single platform with accurately aimed tools. Whether the retailer is focused on upholding a solid brand reputation, avoiding or managing a real-world crisis at their store, preventing cyberattacks on their systems and data, minimizing loss from retail theft, or stopping large scale organized theft rings, an AI-based OSINT solution is key to business continuity and a profitable holiday season.

John O’Hare

Lieutenant Johnmichael O’Hare retired from the Hartford Police (CT) in 2018. His career elevated investigative units that specifically attacked narcotics and firearms violence. In 2013, he was tasked with creating a Real-Time Intelligence Center that could support critical functions & provide analytical and forensic back support. He currently serves as a Business Development/Sales Director with Cobwebs Technologies with a focus on Threat Network Identification & Interdiction in the Web Intelligence Realm.

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