Why “never trust, always verify” is becoming the new operating system of digital commerce

As eCommerce channels multiply—from mobile apps and social commerce to embedded checkouts and marketplaces—the underlying attack surface grows exponentially. APIs, microservices, third-party plugins, and distributed data stores create countless opportunities for misconfigurations and vulnerabilities. To cope, security teams are increasingly adopting Zero Trust architectures and big data analytics as foundations for fraud prevention.OceanoBe+2claemirates.com/

In a Zero Trust world, every request—whether from a customer, an employee, or a system—is treated as untrusted by default. Identity, device posture, context, and behavior all factor into whether access is granted, limited, or denied. Combined with multi-factor authentication and big data-driven risk scoring, this approach allows merchants to apply nuanced controls that evolve with threats.

Zero Trust as a Fraud Strategy, Not Just Cybersecurity

Zero Trust is often framed as an IT security framework, but its principles are highly relevant to fraud. Fraudsters frequently exploit overly trusted internal systems, flat networks, or under-protected admin tools to steal data, modify orders, or manipulate payouts.

A mature Zero Trust implementation in eCommerce might segment internal systems so that even if one area is compromised, attackers cannot pivot easily to payment systems or customer data. Identity and access management policies ensure that only specific services or personnel can initiate refunds, change bank details, or alter pricing at scale. Continuous verification means that anomalies—such as an admin logging in from an unexpected location or device—trigger additional checks.Entrust

Big Data Analytics and Unified Risk Views

Zero Trust architectures thrive on context, and that context comes from data. eCommerce merchants increasingly consolidate logs and event streams from web servers, payment gateways, IDV systems, customer support tools, and fulfillment platforms into centralized data lakes or real-time analytics layers.

Fraud teams, security operations centers, and data science groups work off a shared view of customer behavior and system events. This enables cross-domain insights: a spike in failed logins, followed by unusual access to loyalty balances, and then a wave of refund requests can be recognized as a coordinated attack rather than three separate anomalies.Feedzai

Machine learning models trained on this unified data can assign dynamic risk scores not just to transactions, but to sessions, accounts, devices, and even merchants in a marketplace.

Multi-Factor Authentication in a Zero Trust World

In Zero Trust architectures, MFA becomes a core enforcement tool. Rather than using MFA as a blunt instrument, merchants deploy it adaptively based on risk. For example, a customer logging in from a known device and network might not be challenged, while a high-value purchase from a new device in a different country could trigger strong MFA or even temporary blocking.

Modern implementations lean on phishing-resistant methods such as FIDO2 security keys, WebAuthn-based passkeys, or in-app push approvals rather than SMS codes. These methods are harder to intercept and can be more user-friendly when integrated well.Entrust

Shared Signals Between Security and Fraud Teams

Historically, cybersecurity and fraud teams have operated in silos: one focusing on infrastructure and data breaches, the other on payment losses and chargebacks. In a Zero Trust, data-driven environment, these boundaries blur.

Security tools that detect credential stuffing or bot attacks feed signals to fraud engines, which can then adjust risk scores or enforce step-up authentication. Fraud tools that identify suspicious sessions can share identifiers with security systems to block IP ranges, update firewalls, or tune bot mitigation.

This convergence is particularly important as attackers blend technical exploits with social engineering, using one breach to fuel another. Joint playbooks and shared data models ensure that fraud and security defenses reinforce each other rather than duplicating effort.Entrust

Regulatory and Compliance Dimensions

Regulators increasingly expect organizations to adopt robust controls around identity, access, and data protection. Frameworks that encourage Zero Trust thinking and strong authentication are reflected in financial regulations, privacy laws, and industry standards.ScienceDirect

For eCommerce merchants handling cross-border payments and customer data, this means that investments in Zero Trust and big data security analytics do double duty: they reduce fraud and support compliance with evolving regulatory expectations.

Closing Thoughts and Looking Forward

Zero Trust, big data analytics, and adaptive MFA are not silver bullets, but together they form a powerful foundation for fraud resilience in eCommerce. By treating every request as untrusted, unifying data across systems, and calibrating authentication to risk, merchants can stay ahead of increasingly automated and sophisticated attacks.

Over the next few years, this architectural mindset will likely become the default for large retailers and platforms. Mid-market merchants will access similar capabilities via cloud-native platforms and managed services, allowing them to benefit from global threat intelligence and best practices without building everything in-house.

As these approaches mature, customers may never see the complexity beneath the surface. What they will experience is what matters: fast, seamless shopping that quietly keeps their accounts, identities, and payments safe.

References

Zero Trust Architecture in Payment Systems: Principles, Patterns & Practices – Oceanobe – https://oceanobe.com/news/zero-trust-architecture-in-payment-systems/1654 OceanoBe

Fraud Risk: Why You Need a “Zero Trust” Strategy – CLA Emirates – https://www.claemirates.com/fraud-risk-why-you-need-a-zero-trust-strategy/ claemirates.com/

Criminals and Financial Fraud Schemes: Financial Fraud and Zero Trust – Entersekt – https://www.entersekt.com/resources/blog/tpost/y9mx96n6h1-criminals-and-financial-fraud-schemes-st Entersekt

RegTech Universe 2024 – Deloitte – https://www.deloitte.com/lu/en/industries/technology/analysis/regtech-companies-compliance.html Deloitte

4 Ways RegTech Will Transform AML/KYC Controls in 2024 – AML Watcher – https://amlwatcher.com/blog/4-ways-regtech-will-transform-aml-kyc-controls-in-2024/ AML Watcher

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

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