As more consumers tap into artificial intelligence to enhance their online shopping experiences, new risks are being created for e-commerce merchants. To address those risks, a pair of e-commerce security companies is partnering to offer a new unified framework that they say will enable merchants to safely reap the benefits of AI shopping.
The framework utilizes technologies from Riskified, a global e-commerce fraud prevention and chargeback protection company, and Human Security, which protects digital experiences against bots, fraud, and digital abuse, to protect merchants from revenue loss, inventory manipulation, and reputational damage from AI agent misuse.
Human Security’s Chief Strategy Officer, John Searby, explained in a statement that his company will provide the framework’s trust layer and visibility to identify and govern AI shopping agent interactions, empowering merchants to set and enforce “trust or not” policies, while Riskified will contribute its expertise in e-commerce fraud prevention, chargebacks, and policy abuse.
“Together, we enable merchants to approve more legitimate AI-driven orders, reduce false declines and protect margins, setting the standard for how agentic commerce can grow safely and profitably,” he said.
Riskified CTO and Co-Founder Assaf Feldman added, “In a world where AI agents transact on behalf of individuals, resolving identity and trust becomes more complex. By working with Human and developing new agentic tools and capabilities, we give merchants a way to safely embrace this shift, turning what could be a threat into a new, profitable digital channel.”
AI Agents Evade Fraud Checks
While the companies acknowledge that fully autonomous shopping agents have yet to reach mainstream adoption, they note that consumers increasingly use large language models to research products, compare prices, and find deals, creating both opportunities and risks as technology advances. For merchants, early adoption of AI-driven shopping offers the chance to win new customers and boost conversion rates.
Still, they continued, rules-based fraud management can fail when an AI agent transacts, removing key behavioral signals and leading to more false declines or undetected fraud.
One way AI agents can evade rules-based fraud management is through adaptive probing. “Agents learn thresholds such as velocity, coupon limits, and IP ranges to route around static rules,” explained Ashu Dubey, CEO of Alhena AI, a San Francisco company that specializes in AI-powered customer experience solutions for e-commerce.
He added that agents are also good mimics. “They are very good…
