Retail has reached a stage where customers desire and demand personalization. In the present world, people expect to get products and services that are relevant to them based on their preferences, past purchases and internet activity. This desire for individualized care opens up new opportunities for multichannel retailers to enhance customer loyalty and sales but, at the same time, presents new and growing data security risks in 2025.
The Personalization Paradox
The more personalized the shopping experience, the more data retailers have to collect, analyze and store about their customers. Recent industry surveys reveal that retailers that have adopted personalization strategies can achieve 40% higher conversion rates; however, they also assume a higher burden of protecting customer data.
This leads to the “personalization paradox”: the data that helps retailers offer great customer experiences is also the data that represents the highest risk to their security and liability. In 2025, solving this paradox will become a crucial competitive advantage.
The Growing Data Landscape
Think about how this occurs behind the scenes when a retailer is trying to provide a personalized experience: Every purchase is tracked and analyzed, website behavior is logged, customers’ locations may be captured via mobile apps or in-store WiFi and their communication history with support teams is stored. Each interaction is valuable and creates data that must be protected.
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For the multichannel retailer, the data collection and management challenge escalates across the physical stores, ecommerce sites, mobile applications and social media platforms. The result is a data asset that is growing and is more difficult to protect using conventional approaches.
Emerging Threats to Retail Data
With personalization as the primary driver of data collection, retailers are facing several threats that are increasing year by year:
Shadow data proliferation: Customer information is now found in places you don’t expect – backup copies, test environments, analytical tools and third-party applications. Many retailers are unaware of all the locations where their customer data is stored, leaving them blind.
AI-powered attacks: Cybercriminals can quickly identify and exploit weaknesses in retail systems with the help of advanced AI technologies. Payment card information remains the most attractive target, and attacks are more targeted and challenging to detect.
Supply chain vulnerabilities: As customers’ data is shared with suppliers, logistics partners and service providers, the attack surface expands exponentially. Each connection is a potential entry point.
Building a Retail-Focused Data Security Strategy
Retailers that want to achieve the optimal balance between personalization and data protection should consider the following four approaches for 2025.
1. Implement Comprehensive Data Discovery and Classification
First, you need to know where your customer’s data is before you can protect it. Put data discovery and classification tools into place that can identify sensitive data across the entire retail environment, from the point-of-sale system to the marketing databases.
New AI-based classification models can effectively identify sensitive data, even in unstructured text such as customer service notes or social media posts.
2. Adopt Privacy-Enhancing Technologies
New technologies are being developed to help organizations analyze customer data without revealing the actual customer information. For instance, federated learning allows retailers to train personalization models without that data ever being sent from the customer’s device to the cloud. Similarly, homomorphic encryption enables the analysis of encrypted data without requiring decryption. Synthetic data can be used to build and test new applications without exposing real customer info.
Although still in the developmental stage, these technologies will become available to mid-market retailers in 2025.
3. Develop Incident Response Plans for Data Breaches
Despite all efforts, breaches do happen. Customer data is a special kind of data that requires specific incident response plans for retailers. These plans should include methods of communication to inform affected customers and pre-approved message templates that can be easily modified. Plans also should detail actions to avoid the same incident from occurring again as well as procedures for compliance with different regulations.
4. Create Customer Data Perimeters and Incorporate Security into the Personalization Process.
Data perimeters should be defined to control the flow of customer data within your organization. Thus, by identifying sensitive data, you can avoid transferring it to inappropriate environments. This approach enables the personalization engines to receive the required data while ensuring that the data is properly secured and not over-permissioned or shared inappropriately. Many privacy regulations also require this.
Rather than seeing security as a separate function, building security into the personalization process is possible. This security-by-design approach involves considering data protection at each stage of the process. By embedding security into the process, retailers can deliver personalized experiences while safeguarding customer data.
The Future of Secure Personalization
In the future, the successful retailers will not look at data security as a threat to personalization but as a way to gain customers’ trust. Some forward-thinking retailers already are incorporating security and privacy into their personalization strategies at the foundational level.
As data volumes continue to expand and personalization becomes more sophisticated, retailers that can offer secure customer experiences with rock-solid data protection will thrive. By adopting robust data security strategies now, retailers can be ready for the future of highly personalized yet secure shopping experiences.
Ron Reiter is a Co-founder and CTO at Sentra, a cloud data security company. He is an experienced entrepreneur who sold his company to Oracle in 2016 and went on to invest in over a dozen new startups. After serving in Unit 8200, Reiter spent 15 years in various managing positions in data engineering, cybersecurity and cloud infrastructure.
