How deepfakes, phishing bots, and agentic AI are forcing online retailers to rethink digital security for 2026. 

The New Face of Fraud: Machines Imitating Humans

E-commerce security teams have spent the past decade learning to spot stolen cards, scripted bot attacks, and crude phishing messages. In 2024 and 2025, the battlefield changed again. Instead of sloppy spelling and generic messages, merchants are facing AI-generated phishing emails that mirror their own brand voice, deepfake phone calls that convincingly mimic executives, and automated bots that probe storefronts and apps around the clock.

Law enforcement agencies have warned that cybercriminals are now routinely using generative AI to craft persuasive messages and scripts that defeat the classic “this looks suspicious” instincts consumers once relied on.Federal Bureau of Investigation+1 At the same time, security researchers are documenting real-world deepfake fraud cases in which cloned voices or synthetic videos are used to trick staff into transferring millions of dollars or disclosing access credentials.Incode+1

For e-commerce, this is more than a headline. Chatbots, SMS alerts, and email notifications increasingly mediate digital storefronts, customer service apps, and payment flows. When attackers can use AI to copy those channels precisely, traditional user education is no longer enough. Security has to move inside the app, continuously scoring risk and detecting anomalies in real time.

Inside the New AI Fraud Toolkit

Today’s fraud rings are assembling full AI “toolchains” to attack retailers. Models help identify promising targets by scanning breach data and social networks for high-value accounts. Generative models produce tailored phishing emails referencing real orders or loyalty accounts. Voice synthesis recreates the tone and cadence of customer support agents or company executives. Video deepfakes turn ordinary refund claims or account disputes into convincing “proof” for manual review teams.Federal Bureau of Investigation

Security applications have to respond in kind. Rather than relying on static rules, modern fraud and security platforms are embedding their own AI to:

Detect micro-anomalies in language, such as subtle changes in phrasing that point to AI-generated content. Analyze the acoustics of voice interactions to flag synthetic speech patterns. Correlate behavioral signals across device, browser, and network to distinguish a genuine customer from an automated agent.

In practice, this looks like security apps that sit between the commerce platform and every digital touchpoint. They monitor login flows, password resets, customer service chats, support tickets, and payment authorization steps. When the signals do not match a customer’s historical profile or human interaction patterns, these apps can step up authentication, redirect the interaction to a specialist team, or block it outright.

Security Apps as AI Threat Hunters

Generative AI not only makes fraud easier; it also gives defenders new tools. Vendors are now training dedicated security models on years of fraud patterns, chat transcripts, and behavioral telemetry from thousands of merchants. These models are embedded directly inside digital security apps that plug into commerce platforms, CRMs, and payment gateways.

Instead of queuing suspicious orders for manual review, these apps can automatically generate a structured risk explanation: why the order looks suspicious, which signals contributed most to the risk score, and what additional verification might resolve the uncertainty. At the same time, they can automatically search for look-alike accounts, promo abuse rings, or related devices that point to an organized campaign rather than a one-off incident.Signifyd

A second wave of innovation is emerging around AI-powered “fraud co-pilots” for security analysts. Rather than sifting through dashboards and raw logs, analysts can ask natural-language questions, such as which campaigns are generating the most chargebacks this week or whether there is a new cluster of account takeover attempts tied to a specific set of IP ranges. The co-pilot translates those questions into complex queries across telemetry, making advanced investigation techniques accessible to smaller fraud teams.

Confronting AI Abuse Without Smothering the Customer Experience

Security leaders face a dilemma. Every extra verification step, from multi-factor challenges to document checks, adds friction that can drive away legitimate customers. Yet AI makes it easier than ever for criminals to bypass simple login screens and knowledge-based questions.

The most effective digital security apps are shifting from blanket controls to adaptive, risk-based experiences. Customers with a consistent history, familiar devices, and low-risk behavior can glide through frictionless one-click checkout. Transactions that exhibit anomalies, such as new locations, devices, or unusual order combinations, quietly trigger more stringent checks.

Crucially, these decisions are increasingly made at the edge of the experience. Authentication SDKs embedded in mobile apps, JavaScript tags in web checkouts, and risk APIs in payment flows work together to deliver a personalized journey. This not only protects against AI-augmented attackers but also lays the foundation for privacy-preserving security, where as little personal data as possible is exposed to centralized systems.

Closing Thoughts and Looking Forward

Over the next two years, the race between AI-enabled fraud and AI-powered defense will define digital security for e-commerce. Merchants that treat AI as a bolt-on fraud filter will struggle, as attackers adapt faster than static rules and legacy machine-learning pipelines. Those that redesign their digital security apps as AI-native platforms, ingesting signals from across channels and responding in real time, will be better positioned to maintain trust as commerce continues to accelerate.

The long-term outcome is clear. Every major retailer will run an AI security stack alongside its AI marketing, merchandising, and logistics stacks. The question is whether they can deploy it quickly enough to keep up with adversaries who now have access to the same generative tools.

References

“FBI Warns of Increasing Threat of Cyber Criminals Utilizing Artificial Intelligence.” Federal Bureau of Investigation. https://www.fbi.gov/contact-us/field-offices/sanfrancisco/news/fbi-warns-of-increasing-threat-of-cyber-criminals-utilizing-artificial-intelligence Federal Bureau of Investigation

“Criminals Use Generative Artificial Intelligence to Facilitate Fraud.” FBI Internet Crime Complaint Center (IC3). https://www.ic3.gov/PSA/2024/PSA241203 Internet Crime Complaint Center

“The Anatomy of a Deepfake Voice Phishing Attack.” Group-IB. https://www.group-ib.com/blog/voice-deepfake-scams/ Group-IB

“Top 5 Cases of AI Deepfake Fraud From 2024 Exposed.” Incode. https://incode.com/blog/top-5-cases-of-ai-deepfake-fraud-from-2024-exposed/ Incode

“AI-Powered Scams: How to Protect Yourself in 2024.” University of Wisconsin–Madison IT. https://it.wisc.edu/news/ai-powered-scams-how-to-protect-yourself-2024/ UW–⁠Madison Information Technology

Author: Claire Gauthier, Author – eCommerce Technologies, Montreal, Quebec
Co-Editor: Peter Jonathan Wilcheck – Co-Editor, Miami, Florida

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