Cybercriminals compromised major retail networks across multiple continents in 2025 through sophisticated social engineering campaigns. These attacks exploited the sector's fundamental vulnerability that is retail employs more private-sector workers than any other U.S. industry while maintaining a hospitality culture that prioritizes customer service over security skepticism. Attackers manipulated help desk staff into resetting passwords, infiltrated response meetings, and struck during peak holiday seasons when overworked employees dropped their guard.

Modern retail environments compound these risks by spanning payment processing, inventory systems, and e-commerce platforms that generate complex behavioral patterns traditional security cannot track. Only AI-powered threat intelligence can monitor these interconnected systems at scale, detecting anomalies across millions of transactions while adapting to evolving attack techniques that bypass signature-based defenses.

According to the Verizon 2024 Report, 96% of retail breaches originate from external actors, with credential compromise representing 38% of all targeted data. Also, system intrusion, social engineering and basic web application attacks represent 92% of breaches.

DoS attacks (Denial of Service attacks) also remain a problem for retail organizations, causing disruption to their ability to serve their customers and make sales. In social-related breaches, pretexting has emerged triumphant over phishing as the top social action.

Looking at the numbers and facts, it’s evident that manual threat analysis becomes impossible at this scale, while legacy security tools often struggle to differentiate between legitimate business activity and malicious behavior. AI-powered threat intelligence analyzes behavioral patterns, identifies anomalies in real-time, and provides actionable insights that help security teams stay ahead of emerging threats.

Here are some actionable steps to help you implement AI-based threat intelligence in retail:

Successful AI implementation begins with understanding the unique attack patterns that target retail environments and their specific vulnerabilities. These include the following:

Retail environments face unique exposure through multiple authentication points like employee access to POS systems, vendor portal credentials, customer account databases, and administrative systems. Document all privileged access points that could provide lateral movement opportunities, including seasonal workers, third-party vendor access, and customer-facing applications.

E-commerce platforms create significant attack surfaces through customer portals, payment processing interfaces, and inventory management systems. Conduct comprehensive assessments of all web-based retail systems, particularly during high-traffic periods when performance optimizations may compromise security controls.

Business email compromise campaigns specifically target retail supply chains through vendor impersonation and invoice manipulation. Document your supplier communication patterns, payment authorization processes, and vendor onboarding procedures to identify potential social engineering vectors.

The next step is to choose a platform that integrates seamlessly with retail-specific systems while meeting compliance requirements essential for payment processing environments. Here are the two integrations to take into consideration:

The PCI Security Standards demonstrate that PCI DSS requirements map to NIST Cybersecurity Framework subcategories. Your threat intelligence platform must provide comprehensive monitoring capabilities for payment processing environments, including real-time transaction analysis and cardholder data protection validation.

Advanced platforms should analyze user behavior patterns across retail-specific contexts like seasonal purchasing trends, employee access patterns during peak periods, and supply chain communication flows. Look for systems that establish baseline behaviors for different user roles while adapting to legitimate business variations without generating excessive false positives.

Configure your AI models with retail-specific context as the next step. This helps improve accuracy and reduce false positives that can disrupt business operations.

For instance, retail operations experience significant variations during holiday periods, back-to-school seasons, and promotional events. Therefore, it’s important to train AI systems to recognize legitimate traffic spikes, increased transaction volumes, and temporary employee access patterns during these periods.

Establish baseline patterns for different seasonal scenarios:

• Black Friday traffic surges and holiday gift card redemptions

• Back-to-school inventory movements and end-of-season clearance activities

• Promotional campaign communications and vendor relationship intensification

These patterns help distinguish between legitimate business spikes and potential distributed denial-of-service attacks or coordinated fraud attempts.

Additionally, retail chains require AI models that understand legitimate inter-store communications, inventory transfers, and regional management structures. Train the AI systems to recognize normal customer lifecycle patterns while identifying potential account takeover attempts and fraudulent transactions.

The next step is to implement intelligent automation that prioritizes genuine threats while maintaining operational efficiency for retail security teams. These include the following:

AI-driven systems should automatically enrich security alerts with retail-specific context, including transaction values, customer account histories, employee access patterns, and business process contexts. Implement risk scoring algorithms that consider business impact alongside technical severity. Suspicious activity affecting payment processing during peak shopping hours should receive higher priority than similar activity during low-traffic periods.

Develop response protocols that can automatically isolate compromised accounts or systems without disrupting customer-facing operations. Systems should suspend suspicious user accounts while maintaining point-of-sale functionality, or block potentially malicious IP addresses while preserving legitimate customer access.

Next, maintain and enhance your AI-powered threat intelligence through ongoing adaptation and industry collaboration.

Participate in retail-specific threat intelligence sharing through organizations like the Retail and Hospitality ISAC. RH-ISAC provides collaborative space for cybersecurity professionals and is currently developing a Fraud Intelligence Playbook to strengthen industry-wide threat awareness.

Track key performance indicators that reflect both security effectiveness and business impact: threat detection accuracy, false positive rates, response times during peak business periods, and compliance maintenance. Organizations implementing AI can shorten their breach response times and also reduce the average breach costs.

Organizations implementing AI-enhanced security programs achieve significant quantifiable benefits that directly impact business operations. For retail environments, these improvements translate directly to business continuity benefits during critical sales periods.

Abnormal uses behavioral AI to detect and stop emerging threats that traditional threat intelligence might miss, including advanced business email compromise campaigns that specifically target retail supply chains. The platform models normal behavior based on thousands of identity attributes to detect impersonation attempts and leverages past communication patterns to identify suspicious interactions with vendors.

For retail organizations, Abnormal integrates with existing security infrastructure to provide enhanced protection against sophisticated email-based attacks that bypass standard security controls. The platform's AI-driven approach automatically adapts to retail-specific communication patterns.

CSC Generation, operating DirectBuy, One Kings Lane, Z Gallerie, and Sur La Table with thousands of vendors, lost $100,000 to payment fraud despite robust security measures when attackers compromised a vendor account.

Abnormal's behavioral AI platform transformed their vendor security and showcased:

• Zero vendor fraud incidents since deployment

• $100,000+ in prevented losses through fraud protection

• One-day API deployment with immediate protection

CEO Justin Yoshimura emphasized: "Since we installed Abnormal, there has been no payment or vendor fraud. None. They've completely removed this headache from our security and fraud teams."

Ready to implement AI-powered threat intelligence for your retail organization? Read more customer stories or book a personalized demo to see how behavioral AI can protect your payment processing systems and supply chain communications.