Trust or Bust: How to Secure Contact Center AI
Contact center AI holds great promise for improving customer support, but it also carries significant risks. Bots that provide misleading or unsafe responses can damage customer trust and harm brand reputation. Addressing these challenges requires focused testing and validation tools designed specifically for AI in customer service.
Origins Rooted in Risk
Many early AI deployments in contact centers stumbled when virtual agents went off-script—sometimes swearing at customers, responding offensively, or even encouraging illegal activity. These incidents highlight the need for rigorous safeguards.
To combat these risks, Cyara developed the AI Trust testing suite. This solution includes modules that identify unique dangers posed by generative AI. The AI Trust Misuse module flags inappropriate or off-brand behavior during development, while the AI Trust FactCheck module detects factual inaccuracies and hallucinations common in large language models (LLMs).
As Christoph Börner, VP of Engineering at Cyara, puts it: “Trust is the main currency for AI-driven customer engagements.” He adds that as AI reshapes contact centers, new challenges will arise, and testing approaches must evolve accordingly.
FactCheck: Validating AI Responses Against Real Data
FactCheck works by verifying AI-generated responses against a reliable “source of truth,” such as a product knowledge base or policy manual. It highlights factual errors and partial matches using color-coded feedback, helping teams refine their models before deployment.
This module frequently uncovers issues like fabricated product specs, outdated policies, and incorrect procedures—problems that, if left unchecked, can lead to customer frustration or misinformation.
Bridging the Proof-of-Concept Gap
Despite heavy investment, about 70% of AI-powered CX projects stall in pilot or testing phases. The AI Trust suite helps close this gap by uncovering hidden risks early, giving teams the confidence to move forward.
Börner points out a key challenge: “One of the biggest problems for our clients is the ‘what to do next’ question. Testing AI language models can reveal thousands of issues, which can be overwhelming.” The AI Trust Misuse module assists by detecting hate speech, fraud, and other restricted content in customer interactions, enabling contact centers to prevent harmful incidents before they reach customers.
Speed vs. Assurance: No Longer a Trade-Off
Generative AI requires fast iteration, but speed shouldn’t compromise accuracy or safety. Cyara integrates testing into the development lifecycle, allowing teams to quickly improve AI performance while maintaining strict governance.
Börner sums it up: “We build these tools based on real client challenges, not just because AI is the next big thing.”
For customer support professionals looking to build reliable AI systems, adopting comprehensive testing like the AI Trust suite is essential. It ensures AI-driven interactions are safe, factual, and aligned with brand values—helping maintain trust and deliver better customer experiences.
To learn more about practical AI applications in customer support, explore relevant courses at Complete AI Training.
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