The adoption of AI has already been widely embraced among customer experience professionals. With its ability to revolutionize customer interactions and satisfaction metrics, AI continues to reveal new possibilities in the CX space. A recent McKinsey study highlights this trend, showing that customer service departments are second only to IT in their rapid integration of AI technologies. As AI-enhanced customer experiences become increasingly standard, organizations are scrambling to either maintain their competitive edge or close the gap with early adopters.
Also Read: TrueData Introduces Low-Latency Identity API That Fits Into Any Data Workflow
However, this technological transformation presents a crucial challenge: Companies must carefully balance their use of customer data to create personalized, meaningful experiences while maintaining strict standards of privacy and trust. As AI-powered personalization becomes the norm in customer experience, organizations must urgently address the growing gap between employee AI usage and data security protocols to prevent unauthorized exposure of sensitive customer information while maintaining competitive service delivery.
Security risks to watch out for
The foundation of AI-driven customer success relies heavily on consumer data, which is essential for crafting meaningful personalized interactions, but the security of that data is still a top priority for consumers. In fact, recent research has shown that 40% of customers rank data protection as either the most important or “very or extremely important.”
This creates a fundamental challenge for businesses: How can they harness customer data to deliver exceptional experiences while maintaining robust security measures that earn and preserve customer trust? To address this challenge effectively, organizations must first understand and mitigate several key security risks.
- Data storage and collection concerns. Consumer privacy expectations clash with concerning employee behavior patterns. Recent studies by the National Cybersecurity Alliance reveal a troubling trend where 38% of employees regularly input confidential data into AI tools without proper authorization. Of particular concern is employees’ casual attitude toward data security — 25% see no issue with sharing sensitive personal information through AI platforms. This risk is compounded by the fact that many AI services store submitted data for model training purposes, creating potential vulnerabilities for data breaches or unauthorized access to sensitive information. The gap between proper data handling protocols and actual employee practices presents a significant security challenge that organizations must address.
- Baked-in biases. AI is trained on and learns from whatever data it’s provided, and much of that information comes from the public internet. Because of this, many cases of AI outputting prejudiced responses have arisen from the biases present in a model’s training data. Advanced AI technology can only succeed when its underlying data is free from prejudice — no matter how sophisticated the system, it will fail to create an inclusive customer experience if trained on biased datasets.
- Miscommunications and incorrect information. A lot of AI is unable to handle the nuances of language — see X (formerly Twitter)’s AI Grok wrongly accusing an NBA player of vandalism and property damage because it misunderstood what “shooting bricks” meant in the context of basketball (for the record, it means missing shots). Until AI is adapted to a specific domain, language, context, brand and more, it will struggle to parse idioms and other complex language.
Prioritizing a privacy-first CX strategy
So, now that we know some of the security threats to look out for, how can we strategically balance the need for personalization with the prioritization of security and ethical concerns? The key is to see AI for what it really is — a double-edged sword. One side is a powerful tool that can improve customer experiences while the other is a burgeoning risk for data privacy. Add in the quickly changing regulatory environment and the need for proactive privacy strategies becomes even more apparent. Here are some actionable tips for how to best meet expectations for both experience and security:
- Work toward error and bias elimination. Context and accuracy are paramount in AI implementation. Domain-specific training and customized terminology are essential to ensure AI systems can distinguish between industry-specific concepts and their common language counterparts. For example, specialized training helps AI differentiate between technical terms and their everyday meanings.Building truly equitable AI requires more than technical solutions — it demands diverse development teams and innovative approaches that challenge conventional thinking. This comprehensive strategy helps organizations develop AI systems that minimize biases and maintain high accuracy standards.
- Enable encryption. Encrypt your data to protect yourself from cyberattacks and gaps in your security infrastructure. Opt for a platform that can detect and mask PII automatically.
- Require zero data retention. As soon as you’re done using information, make sure it is securely deleted from all processors, including those of partners or vendors you work with.
Also Read: Secure with Simplicity: Why IT Teams Need Better Backup Processes
Today’s customers have heightened awareness of data privacy concerns, and their confidence in businesses is directly tied to robust data protection practices. Building and maintaining customer trust requires clear communication about how data is handled and protected. Organizations must implement comprehensive security oversight that extends to all stakeholders in their privacy framework, enabling them to identify and address vulnerabilities regardless of their source and keep customer data secure.
