Cyber 5-Ready Retail: AI Strategies for the Holiday Rush

As Cyber 5 and the holiday season loom large, retailers are speeding up artificial intelligence adoption in personalization engines, chatbots, fraud detection, and operations. These technologies can enhance revenue and efficiency, but they also amplify risks like shadow AI, data leakage, and AI-driven attacks.

Cyber 5: A Stress Test for AI and Security Readiness

The holiday shopping rush is a high-stakes trial of retailers’ digital capabilities. Sudden traffic spikes, complex multi-site dependencies, and zero tolerance for outages turn minor gaps into major incidents. Retailers that still rely on fragmented legacy networks and siloed security point solutions risk bottlenecks, blind spots, and breaches when it matters most. At the same time, rapid AI rollouts increase the attack surface and require consistent security and connectivity controls at scale.

The Retail AI Journey: From Experimentation to Embedded Intelligence

Retailers are embracing AI in stages:

• Grassroots experimentation: Employees use free generative AI tools to draft content, analyze data, or brainstorm campaigns — often without IT oversight.
• Platform integration: Custom models drive product recommendations, dynamic pricing, and inventory optimization across digital storefronts.
• Embedded LLMs: API-based large language models (LLMs) are woven into apps to power chatbots and automated support.

Each phase introduces new risks. Shadow AI use and unmanaged experimentation can lead to data leaks. AI governance is essential. A stringent API security policy and continuous monitoring are essential for embedded LLMs.

Retail’s AI Risk Landscape

Retail’s digital edge is under siege with AI adoption adding more complexity:

• Shadow AI and data leakage: Employees uploading sensitive data to public AI tools can inadvertently expose customer personally identifiable information (PII), payment credentials, or proprietary business logic. Without centralized control, these data leaks go undetected.
• Model and API weaknesses: LLMs and third-party APIs can be exploited through prompt injection, data poisoning, or misuse. Threat actors may manipulate model outputs or hijack API calls for unauthorized access to backend systems.
• AI-powered phishing: AI-generated fake CAPTCHA challenges are used in phishing sites and emails to bypass security filters. Mimicking real verification prompts, these scams evade detection and trick users into revealing credentials.
• Operational stress underload: Traffic surges can expose misconfigured security point solutions and broken failover paths, amplifying the impact of attacks or outages during Cyber 5.

Securing AI Without Adding Security Debt

Retailers can adopt AI safely by modernizing their digital edge and enforcing consistent, cloud-native security policies. A secure access service edge (SASE) architecture is key to this transformation, converging networking and security into a unified platform that supports secure AI adoption across distributed environments.

1. Converge networking and security. Replace fragmented tools with a SASE platform to ensure high-performance connectivity across stores, data centers, and cloud apps.
2. Enforce data protection at the edge. Use security capabilities like data loss prevention (DLP), cloud access security broker (CASB), and secure web gateway (SWG) to block unauthorized uploads and monitor AI tool usage. These tools are critical for mitigating shadow AI risks.
3. Apply zero trust to AI interfaces. SASE enables identity-based access controls and segmentation for API calls to LLMs and model endpoints, enforcing least-privilege access.
4. Segment traffic. Application-aware routing and traffic prioritization isolates and manages AI workloads without disrupting core commerce operations for resilience during peak loads.
5. Automate threat detection and response. Real-time analytics and threat intelligence detect AI-driven fraud and model misuse.
6. Establish governance. Define approved AI tools, enforce usage policies, and educate employees on risks. Run drills to validate failover and incident response plans.

AI is reshaping retail from personalized experiences to automated fraud detection, but its benefits come with new risks. As Cyber 5 approaches, retailers must secure their digital edge. A SASE-powered approach offers the convergence, visibility, and control necessary to scale AI-driven experiences while keeping holiday risk under control.

Etay Maor is the chief security strategist at Cato Networks, which provides a world-leading single-vendor SASE platform.