Riskified Cyber Security Framework: A Comprehensive Guide

What Is the Riskified Cyber Security Framework?

When most businesses think about cybersecurity, they picture firewalls, intrusion detection systems, and endpoint protection. But for ecommerce merchants, the most dangerous threats are not hackers breaking into servers—they are fraudsters exploiting the checkout process, the returns flow, and the trust systems built into every customer-facing interaction.

Riskified has built a cyber security framework specifically designed for this reality. It is not a generic compliance checklist or a perimeter defense tool. It is a full-stack approach to ecommerce risk that combines machine learning, behavioral analysis, a global merchant network, and now, emerging protections for AI-driven shopping agents. Understanding how it works can help any merchant build a more resilient, fraud-resistant business.

The Core Problem: Traditional Security Misses Ecommerce Fraud

Traditional cybersecurity tools are built to stop unauthorized access. They are excellent at keeping attackers out of your systems. But ecommerce fraud does not require unauthorized access—fraudsters exploit the authorized channels your customers use every day.

A fraudster who has obtained stolen credit card data does not need to breach your database. They simply place an order like any other customer. A fraud ring running a returns abuse scheme does not need to hack your systems. They exploit your legitimate returns policy at scale. An AI agent performing automated reseller arbitrage does not need to bypass your firewall. It simply purchases faster and smarter than a human can.

This is why Riskified's approach is fundamentally different. Instead of focusing on who is trying to get into your systems, it focuses on whether the person placing an order—or the AI agent acting on their behalf—is who they claim to be, and whether their behavior is consistent with legitimate intent.

The Five Pillars of the Riskified Cyber Security Framework

1. Machine Learning–Driven Transaction Decisioning

At the heart of Riskified's framework is an automated decisioning engine that analyzes every transaction in real time. The system assesses hundreds of data attributes for each order—device fingerprints, behavioral signals, IP characteristics, email account age, shipping address history, and dozens more—to produce a fraud probability score.

What distinguishes Riskified's machine learning models from simpler rule-based systems is their ability to recognize complex, non-obvious patterns. Traditional fraud rules look for specific signals: mismatched billing and shipping addresses, unusual transaction amounts, or orders from high-risk geographies. Riskified's models look for clusters of signals that, taken together, indicate fraud even when no single signal is conclusive.

Critically, these models are trained on data from Riskified's global Merchant Network—a consortium of ecommerce businesses whose transaction data collectively forms one of the largest fraud intelligence datasets in the world. Insights derived from fraud patterns at one merchant immediately benefit all others on the network.

2. Smart Linking and Identity Resolution

One of the most powerful capabilities in Riskified's framework is what the company calls Smart Linking: the ability to connect disparate data points across orders and customers to build a comprehensive picture of identity.

A fraudster may use a different name, email, and device for each fraudulent order. But they may share a phone number, a shipping address, or a device fingerprint with a previous fraudulent transaction. Smart Linking detects these cross-order connections, allowing Riskified to identify suspected fraud rings and flag associated orders even when no single order appears suspicious in isolation.

This identity resolution capability extends across Riskified's entire merchant network. Behavioral intelligence from over one billion historical transactions is continuously refined and applied to new orders, making the system progressively more accurate as the network grows.

3. Policy Protect: Defending Against Abuse Beyond Fraud

Classic payment fraud—using stolen card data to make unauthorized purchases—is only one dimension of the ecommerce threat landscape. Increasingly, sophisticated actors exploit merchant policies themselves: filing false return claims, abusing promotional codes, orchestrating reseller arbitrage, and manipulating loyalty programs at scale.

Riskified's Policy Protect solution addresses these threats directly. Using the same identity clustering technology that powers its fraud detection, Policy Protect evaluates refund requests, return claims, and promotional redemptions to identify patterns of abuse. Merchants gain real-time visibility into which customers and orders are generating legitimate returns versus those exploiting policy weaknesses for financial gain.

Policy Protect is particularly valuable because policy abuse is difficult to catch with traditional fraud tools. A return filed by a legitimate-sounding customer with a valid order number does not look like fraud—until you see that the same identity has filed eighteen similar returns across six different accounts over the past month.

4. Chargeback Guarantee: Financial Risk Transfer

A defining characteristic of Riskified's framework is its economic model: for approved orders, Riskified provides a financial guarantee. If an order that Riskified approves results in a chargeback due to fraud, Riskified covers the loss.

This guarantee is more than a commercial differentiator—it is a structural alignment of incentives. Riskified is financially motivated to make accurate approval decisions, not just to decline suspicious orders. An overly conservative system that declines too many legitimate orders is costly to Riskified because it fails merchants and drives customers away. An overly permissive system that approves too much fraud is costly because Riskified absorbs the losses.

This creates a powerful feedback mechanism: the guarantee pushes Riskified's models toward precision—approving as many legitimate orders as possible while minimizing fraud losses—which is precisely what merchants need.

5. AI Agent Security: The Emerging Frontier

Perhaps the most forward-looking component of Riskified's framework is its response to AI-driven shopping agents. As large language models like ChatGPT, Claude, and Gemini gain the ability to research products, compare prices, and complete purchases autonomously on behalf of consumers, ecommerce merchants face a new category of risk.

Riskified has identified that LLM-referred traffic can be significantly riskier than traditional sources. In documented cases, LLM-referred transactions at a ticketing merchant were 2.3 times more likely to be fraudulent than Google search traffic. An electronics merchant recorded a 1.8-times higher risk rate from AI-agent traffic. Automated reseller arbitrage—AI agents rapidly purchasing products to resell at inflated prices—is already occurring at scale.

In response, Riskified has launched a suite of AI agent security tools in partnership with cybersecurity firm HUMAN Security. The AI Agent Approve solution, available as an MCP Server Package on AWS Marketplace, allows merchants and LLMs to communicate with Riskified's platform APIs to get real-time fraud decisions on AI-agent-initiated orders. The AI Agent Intelligence dashboard provides visibility into orders originating from AI shopping agents. The AI Agent Policy Builder enables merchants to configure and enforce policy controls specifically for agentic commerce.

The Role of Human Intelligence in an AI-Driven Framework

Despite the depth of automation in Riskified's framework, human expertise remains central to its operation. Dedicated risk analysts monitor performance around the clock, adjusting model thresholds to protect each merchant's unique risk profile. Fraud analysts and customer service agents are given tools and dashboards that allow them to interact with customers more intelligently, with full context on why a given order was approved or flagged.

This human-in-the-loop design serves a critical function. Fraud patterns evolve constantly—new schemes emerge, fraud rings adapt their methods, and macroeconomic conditions shift the risk landscape. Human analysts are positioned to detect emerging threats that have not yet accumulated enough data for the models to recognize independently, and to adapt the system's response accordingly.

Why the Network Effect Makes This Framework Unique

No other component of the Riskified framework is as strategically important as the Merchant Network itself. When fraud occurs at one merchant on the network, that intelligence is immediately incorporated into the models that protect every other merchant. The network is self-reinforcing: every new merchant that joins adds new transaction data, which improves detection accuracy for all existing merchants.

This creates a compounding advantage over time. An in-house fraud team at a single merchant, no matter how capable, simply cannot accumulate the breadth and depth of cross-merchant fraud intelligence that a network of global ecommerce leaders produces collectively.

Implementing the Riskified Framework in Your Business

For merchants evaluating Riskified's framework, implementation begins with a straightforward integration—a storefront Beacon that collects device and behavioral signals, plus API connections to Riskified's decisioning engine. The platform integrates natively with major ecommerce stacks including Shopify, and Riskified offers a seamless plug-and-play integration through the Shopify App Store.

Once live, the framework operates in the background of every transaction. Merchants access performance data and behavioral trends through the Riskified Control Center, a real-time dashboard that provides decision-level transparency: not just whether an order was approved or declined, but why. This context empowers fraud teams to learn from the system's decisions and make more informed overrides when necessary.

Conclusion: A Cybersecurity Framework Built for Modern Commerce

Ecommerce security is not a perimeter problem—it is a trust problem. Every transaction is a moment of truth: a decision about whether the person on the other side of the screen is who they say they are, and whether their intent is legitimate. Riskified's framework addresses this challenge at every layer, from real-time ML decisioning and identity resolution to policy abuse protection and emerging AI agent security.

For merchants operating at scale, the calculus is clear. Fraud losses, chargeback costs, and policy abuse drain margin. Overly aggressive fraud rejection alienates legitimate customers. The Riskified framework is designed to resolve this tension—maximizing legitimate order approvals while minimizing fraud exposure—with a financial guarantee that aligns Riskified's success directly with the merchant's own.

As the ecommerce threat landscape evolves—particularly with the rise of AI-driven shopping agents—the businesses that invest in purpose-built, network-powered cyber security frameworks will be the ones that grow safely and sustainably. That is the promise Riskified is built to keep.