Enterprise contact centers are where the quiet tug-of-war between security and customer experience plays out in milliseconds. On one side, fraud teams are tightening controls as account takeover rates climb and social engineering gets more convincing. On the other, CX leaders are under pressure to cut friction, shorten handle times, and keep satisfaction scores moving up.
In the middle sits the customer, more impatient than ever, more frequently targeted, and mostly unaware of how much is happening behind the scenes before an agent even answers a call.
Globally, approximately 7.7 percent of annual revenue was lost on average to fraud, representing $534BN, according to TransUnion's 2025 Global Fraud Trends Report, driven by scams, synthetic identities, and account takeovers.
At the same time, Salesforce's State of the Connected Customer report found that 80 percent of customers say the experience a company provides matters as much as its products. And 75 percent of consumers say poor customer service changes their purchasing behaviors, according to a Zoom consumer study.
Organizations can't afford to lose on either front. Can real-time intelligence make that balance achievable?
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What Real-Time Risk Looks Like
In a contact center, fraud signals surface almost instantly. Real-time risk evaluation now means moving authentication to the "edge" of the network, using deterministic, invisible signals.
Before an agent even says hello, modern systems are pinging carrier networks to verify if the device a customer is using is familiar, jailbroken, or the IP address has appeared in recent fraud clusters, alongside behavior such as how they type, pause, or navigate a call or chat. Multiple login attempts, rapid-fire requests, or unusual spikes in activity across channels in the past 24 or 72 hours can raise flags. And then there’s interaction history—including past calls, recent changes to account details, prior disputes—layered with agents’ notes.
Taken together, these form a live snapshot of intent, helping teams decide in the moment whether they’re dealing with a genuine customer or something that needs a closer look.
But that decision is becoming more challenging as AI agents proliferate.
Reliance on knowledge-based authentication (KBA), asking callers to provide a mother’s maiden name, a first pet, or a recent transaction, is no longer reliable account defense in an era of massive data breaches and sophisticated social engineering.
Trust frameworks need to take new approaches to determine who is at the other end of an interaction, Mary Ann Miller, VP Evangelist & Fraud Executive Advisor at Prove told CX Today in a recent interview.
“It's understanding if it's a human or not, understanding if it's an agent and if that agent is authorized, understanding if there are other ways that the AI can be, at the other end of that interaction, learning and adjusting to its attack vector. So we're really in a different situation now.”
Fraudulent actors are already using AI voice cloning and deepfake videos to bypass selfie and identification verification. And AI agents can take this a step further by acting autonomously to run full-scale scams. They can probe for weak points, test verification flows, and refine their approach with each interaction, at speed and scale.
“We can use AI to look at a document to determine if that document is genuine or not. But we can also use AI to generate a fake document as well. So we’re really looking at an environment [where it’s] machine versus machine. And that puts us in a position to need to really look at controls and risk assess those almost on a weekly basis to see what’s working and what’s not,” Miller said.
The combination of context from customer relationship management (CRM) and customer data platforms (CDPs) with security telemetry separates modern risk-aware CX from basic authentication. It’s also what makes governance and architecture more complex.