EDITOR ’ S QUESTION

CX teams have a duty to protect customer data , most importantly their financial details , and so must keep critical systems such as CRMs under careful observation to safeguard against AI cybersecurity threats . The best way for enterprises to keep sensitive data secure and maintain excellent customer experience is to leverage the same technology with AI-powered threat detection .

AI attacks are especially pervasive due to their ability to analyse massive datasets too quickly and evasively for a human agent to detect . AI systems can automate the same processes to quickly locate patterns within datasets that suggest a cybersecurity threat , such as by establishing baselines for normal system behaviour to measure abnormal activity and detect sophisticated cyberattacks before they can cause harm .

CX officers should ensure that there are clear governance policies in place for data protection and retention , including regular audits of customer information and data entry training for CX teams . It ’ s important to remember that no AI model is free from bias , and so should be regularly assessed to guarantee that all customer data is treated equally .

For businesses using cloud-based unified communications tools to engage with customers , cloud-specific ransomware and fraud protection tools are key to countering threats . While APIs can provide additional security , such as authentication and rate limiting , teams should primarily be focused on implementing stringent security testing and management practices .

THE BEST WAY FOR ENTERPRISES TO KEEP SENSITIVE DATA SECURE AND MAINTAIN EXCELLENT CUSTOMER EXPERIENCE IS TO LEVERAGE THE SAME TECHNOLOGY WITH AI-POWERED THREAT DETECTION .

Organisations must maintain good data hygiene habits to ensure that their AI models are effective ; they ’ re only as good as the data they are trained on . To start with , essential data protection practices should comply with regulatory frameworks , such as GDPR and PCI DSS . These require data encryption , firewall installation , strong access controls and regular network monitoring , alongside other practices for safeguarding sensitive information , especially payment data . Data masking techniques can also protect sensitive information when training AI models by replacing real data with fictitious but structurally similar data to avoid compromising sensitive information .

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