Trust Infrastructure: How Digital Trust Becomes a Growth Engine in the Age of AI Agents

The gap between looking legitimate and actually being who you claim – with the authority you claim – isn't new. Fraudsters have exploited it for years. 

Last year, US consumers reported $15.9 billion in fraud losses to the Federal Trade Commission. Impostor scams generated more than a million reports. Online, a seller, a company joining a platform, or an AI agent making a purchase can appear legitimate before anyone has established who is behind them or what they are authorized to do. 

The uncertainty runs both ways. Customers need confidence that a business will protect their money and data. Platforms need to verify individuals and companies, understand who owns a business, and recognize when an account's control or an agent’s authority changes. Those checks must respond to risk without making every interaction difficult.

Sumsub’s Trust Infrastructure supports this ongoing, risk-based approach by connecting identity and business verification, AML and transaction monitoring, fraud signals, risk scoring, and case management across the customer lifecycle. For compliance teams, this means carrying information from onboarding into later checks and investigations, with a traceable record of decisions. 

What is Trust Infrastructure?

Trust Infrastructure is the connected system a business uses to decide who it can trust, reassess those decisions as circumstances change, and document and justify them. It brings identity and business verification (KYC and KYB), AML and transaction monitoring, fraud signals, risk scoring, and case management into one layer, so every decision draws on the same data and leaves the same audit trail.

The word that matters is infrastructure. Most compliance stacks are built one tool at a time, with one vendor for onboarding, another for monitoring, and a third for investigations. Each tool does its job, but context gets lost as information passes from one system to the next. Alerts are harder to review, evidence is harder to trace, and analysts spend more time gathering information than acting on it. As a business adds users, entities, and markets, that fragmentation leads to missed risk, slower investigations, and more manual work.

Trust Infrastructure connects these systems so context carries through. A check completed at onboarding informs monitoring later. What monitoring finds updates the customer's risk profile. When a case is opened, the analyst sees identity, fraud, AML, and transaction data together, and every rule, action, and outcome stays traceable for an audit.

The peer-to-peer trust problem

On a platform that connects strangers, verifying someone’s identity cannot guarantee they will keep their side of a deal. A verified seller might still send a faulty item; in peer-to-peer lending, a verified borrower can still default. Marketplace trust and safety goes beyond identity checks. Reviews help people judge past behavior, while payment protections and dispute processes address what happens when a transaction goes wrong.

How reputation and rating systems build digital trust

Reviews make one person’s experience useful to the next stranger. Soon after eBay began connecting buyers and sellers, its founder introduced a Feedback Forum for public praise and complaints. A reputation system turns individual experiences into a shared record: buyers can judge sellers from previous orders, just as guests use earlier stays to assess a host. 

Reviews are only useful if the feedback is genuine. While platforms typically prohibit feedback manipulation, it remains a risk – one that AI makes far easier to scale.

Ratings also need context. Five stars from two sales tell buyers less than five stars from hundreds of recent orders, while a reliable new seller may have no reviews at all. Ratings offer evidence of past conduct, not a promise about the next sale. Even an established seller’s history becomes misleading if someone later takes over the account. 

Escrow, verification, and dispute resolution as trust primitives

Consider a buyer purchasing a secondhand camera from a seller with good reviews. Those reviews help the buyer choose, but they do not ensure this sale will go smoothly. Verification helps establish who the seller is, and an escrow service can hold the buyer’s money until the camera arrives and the agreed inspection period ends. The seller knows the funds have been committed, and the buyer can check the camera before payment is released.

If the camera arrives damaged, a dispute process sets out how the buyer raises the problem, what evidence the seller can provide, and who decides whether the money is released or returned. Together, they identify the participant, protect the payment, and provide a route to resolution – none of which a good reputation can do on its own.

Suggested read: E-Commerce Fraud Prevention: A Complete Guide for Online Merchants 2026

Trust as a competitive differentiator

Consumer trust begins before a first purchase, when someone decides whether to believe a business’s claims. Customer trust is tested after payment, when the product arrives, and support or a refund may be needed. Brand trust grows when those experiences consistently match what the company promised. In a crowded category, that record can influence which of two similar offers a buyer chooses.

Before purchasing, buyers can check whether reviews come from completed orders, what a “verified seller” badge means, the total price, and the conditions for a refund. These details serve as trust signals when they are easy to find and verify.

Trust signals that convert (reviews, certifications, transparency)

In Baymard Institute’s breakdown of cart abandonment, after setting aside shoppers who were only browsing, 19% of surveyed US online shoppers cited distrust of a site with their credit card information.

The most useful trust signals address a buyer’s immediate doubt. On a product page, reviews tied to completed orders speak to quality, while a certification with a named issuer and scope supports a specific claim about testing or security. At checkout, the total price, payment information, and clear return terms provide details that a generic badge cannot.

Case comparison: high-trust vs. low-trust brands in the same category

Take two refurbished-phone marketplaces listing the same model at the same price. One links reviews to completed sales, explains what its “verified seller” label covers, publishes a minimum battery-health standard, and states the return process beside the listing. The other has glowing testimonials and an unexplained verification icon, but no stated battery standard and return terms that are hard to find. Both describe the phone as “tested,” but only the first tells buyers what that means.

If the battery proves faulty, the first buyer can point to a published standard and a clear route to a refund. The second buyer has to work out what “tested” means and how to raise a claim. The difference is visible before checkout and becomes more consequential if something goes wrong. The first marketplace gives customers a reason to choose it and a reason to come back if it handles the problem fairly.

Trust as a growth engine

In a trust economy, one completed transaction can make the next one easier. A buyer’s honest review gives the next shopper evidence about a seller. Fair handling of complaints can bring buyers back. A seller who can challenge inaccurate claims can protect their reputation. Those experiences can attract new buyers and keep existing ones active. Public reviews can also influence local search visibility and AI recommendations that draw on customer feedback, reaching people who have yet to visit the business. 

Social proof mechanics and conversion lift

Social proof is the psychological tendency to look to others’ behavior for guidance when uncertain. Online reviews apply that tendency to shopping: a review from a verified buyer that names the product variant and describes delivery gives the next shopper more to go on than a star average. Dates, review volume, and responses to criticism help shoppers decide how relevant those experiences are.

The Medill Spiegel Research Center found that purchase likelihood for products with five reviews was 270% greater than for products with none in the data it studied. The benefit of additional reviews diminished after the first five, and purchase likelihood typically peaked below a perfect five-star rating.

Reputation management as an SEO/GEO asset (reviews feeding AI answer engines)

Online reputation management covers correcting inaccurate information, responding to complaints, and fixing the problems behind recurring criticism. Google says reviews and positive ratings can help local search rankings. Its Ask Maps feature also draws on community reviews to answer questions about places. What customers say can affect both a business's visibility in traditional search and what AI-powered tools recommend.

Generative engine optimization (GEO) concerns whether and how a business appears in AI-generated search answers. Google’s AI Overviews and AI Mode, for instance, can link to indexed pages and current business details, clear policies, and accounts of actual customer experiences to give people and answer engines sources they can check. 

The same route, however, can be abused through AI recommendation poisoning – hidden instructions in content an assistant reads that can bias its later suggestions.

The identity & verification layer

A buyer can read a seller’s reviews, but the marketplace needs to know who will receive the money if a sale goes ahead. Identity verification helps establish who an individual seller is. When the seller is a company, KYB checks establish whether it exists, who owns or controls it, and who is authorized to act for it. Both create a record the platform can consult if a sale is disputed or the seller’s account changes.

A digital identity links those verified details to the account and the credentials used to access it. A login is not permanent proof of who is acting: credentials can be stolen, and an authorized representative can leave a company. The initial verification has to support decisions made later in the relationship.

KYC/AML as Trust Infrastructure, not just compliance

Banks and other businesses covered by anti-money laundering rules use Know Your Customer verification to verify who their customers are and understand how they use the service.

The same identity record helps these businesses respond when an account’s behavior changes. In Sumsub’s internal data, 76% of fraud attempts occurred after onboarding, including during logins, profile changes, and transactions. 

A familiar monthly payment need not prompt another document check, but a large transfer to a new recipient could call for confirmation with the verified account holder. This means the initial KYC check also helps protect the customer without adding the same friction to every payment.

Verifiable credentials and decentralized identity

When a bank opens an account, it needs to verify who its customer is. A site selling age-restricted products may need only to know whether a buyer meets the minimum age. Verifiable credentials could let the buyer present an age claim from an issuer the site trusts, without handing over the document used to establish their age. 

In some decentralized identity models, the customer holds that credential and chooses when to present it to another service. This could avoid another document upload, provided the service accepts the issuer’s checks, confirms the credential is still valid, and checks that the person presenting it is entitled to use it.

Suggested read: KYC and AML Explained: Key Differences, Regulations, and Best Practices

The security layer

Verifying an account holder is only part of keeping an account safe. A stolen login or an employee account with excessive access can expose customer data or enable fraud long after signup. Fraud prevention depends on controlling what each person or system can do as well as checking who they are. 

Zero trust architecture explained

Traditional network security trusts anyone already inside the company network. Once someone logs in, they can often reach far more systems and data than their job requires. Zero trust architecture removes that assumption. Each request to access a resource, such as an application, a database, or a customer record, is checked separately, and access is granted only to what that request needs. NIST's guidance says network location and device ownership should confer no implicit trust. Instead, the user's identity and the device's status are verified before each session with a resource begins. If an attacker steals one set of credentials, they reach only what those credentials allow, not the whole network.

Under zero trust, a support worker resolving a delivery complaint might be able to view an order and its delivery status, while access to identity documents or the ability to issue a refund requires separate permission. If the worker’s credentials are stolen, these limits contain what the intruder can reach. 

Data privacy as a trust signal, not just a legal requirement

Customers encounter data privacy in the questions a service asks and the choices it gives them. A site offering birthday discounts could ask for the day and month, without collecting a full date of birth. An account verification step may need more sensitive information. Explaining why it is required at that point gives customers a way to judge the request and decide whether to trust.

Some businesses have legal obligations to retain identity records for AML purposes, and records may also be needed to investigate fraud. Stating how long they are kept, who can access them, and whether a provider receives them makes the privacy promise more concrete. Limited collection and access also reduce the amount of information exposed if a system is breached. When a service asks only for information it needs and explains what will happen to it, privacy becomes a trust signal customers can assess before sharing sensitive details.

Suggested challenge: Test: How Well You Protect Your Data

Can criminals catch you out? Put your habits to the test.

Trusting AI agents

Questions about who may access a customer’s information become more complicated when software acts on their behalf. Agentic AI can complete forms, pass details between services, and make purchases without a person reviewing every step. The business receiving a request needs to establish who authorized the agent and what it may do, while the customer needs a way to limit and review those actions.

Why AI agents need their own trust infrastructure

An agent might read a product page, email, or document containing instructions planted by someone other than its user. If it treats those instructions as part of the task, it could order the wrong item or disclose account information through a tool it is allowed to use. Risks include agent goal hijacking, tool misuse, and identity and privilege abuse.

That creates a problem on both sides of a transaction. A retailer needs to distinguish a customer’s authorized agent from an impersonator, and the customer needs assurance that the agent still follows their request. Recognizing the agent alone cannot establish that a particular order reflects the customer’s instructions.

Agent identity, authentication, and permissioning

AI agent security involves three linked checks: which software is making the request, which person or organization it represents, and what that person or organization has authorized it to do. Credentials can authenticate an agent, while permissions specify the accounts and actions available to it. An agent asked to find a phone under $500 does not automatically have permission to buy one. 

A trust layer applies those permissions when an agent tries to act. It can check the agent’s credentials, its delegated authority, spending limits, and whether approval is still valid. The action can be recorded, access revoked, and extra confirmation requested if the amount or circumstances fall outside the agreed limits. In a Know Your Agent approach, the agent’s activity can also be linked to a verified person, whose involvement is checked when a higher-risk action warrants it.

How humans verify AI-agent-originated actions

AI trust depends on a person being able to check an agent’s proposed action independently of the agent’s own description. Before a purchase, an approval screen opened in the retailer’s or payment provider’s own app could show the seller, item, total cost, and delivery address. A check that confirms the person’s identity is useful, but they also need to know what they are approving.

Continued trust in AI also depends on what happens after an action. Customers need a record of what the agent did and a way to withdraw its permissions or dispute an unauthorized transaction. An AI travel-booking agent could compare flights within a set budget without interrupting the customer, then show the itinerary, total price, and cancellation terms before booking. If the flight exceeds the budget or goes to a different destination, the agent would need fresh approval. The customer can delegate the search without giving up control of the purchase.

Suggested read: From AI Agents to Know Your Agent: Why KYA Is Critical for Secure Autonomous AI

Reputation & trust scoring systems

A trust score brings together signals such as account history, complaints, and unusual activity to help decide when closer review is needed. While a reputation system such as those found on Amazon or eBay describes past conduct, a risk score estimates whether a particular action needs review. 

Let’s say a seller with years of positive reviews changes their bank details just before a large payout. The reviews describe earlier transactions, but cannot establish who controls the account now. Dynamic risk scoring can flag the change so the platform can confirm it with the seller before releasing the money. When the change is legitimate, resolving the hold quickly is important too, as a delayed payout can cost the platform a trusted seller.

Scores need updating as company ownership changes and reputable accounts can be compromised. Dynamic risk scoring can incorporate new identity and activity signals, while analysts should still be able to explain what changed and why it affects a decision.

Suggested read: Precise, Confident, and Wrong: The Trouble With KYB Risk Scores

Building a trust infrastructure strategy

Strategies for building trust need to consider the customer journey. How do people discover a service? Do they need to sign up and share information? How do they complete transactions and ask for help? 

At each stage, businesses should establish what they promise, what they actually deliver, and what happens if they fall short. If a marketplace calls a seller “verified,” buyers should be able to find out what was checked and how to seek a refund if a sale goes wrong. If it lets an AI agent buy on a customer's behalf, the customer should see the agent's spending limit and be able to revoke its access. 

A digital trust framework assigns responsibility for delivering on promises, like deciding when a seller needs further verification, which account changes warrant review, how customers approve or revoke an agent’s authority, and who can reverse a mistaken restriction. Information from signup, transactions, agent actions, and complaints should inform those decisions as the relationship develops. 

Suggested challenge: Is Your Memory Good Enough to Beat the Beast?

How Sumsub connects the customer lifecycle

Sumsub’s AI-powered Trust Infrastructure brings identity and business verification, AML and transaction monitoring, fraud prevention, and risk workflows into one connected platform. Its operational layer includes Workflow Builder, Risk Scoring, Case Management, and Summy AI Copilot, while integrations connect external data providers and existing systems.

Compliance teams can configure checks around their policies, review relevant evidence in a case, and retain a traceable investigation record. AI-assisted tools help summarize cases and surface signals for analysts to assess, with teams keeping control over decisions.

Explore Sumsub’s Trust Infrastructure to see how its solutions, products, workflows, and integrations can support your compliance operations.

Digital trust FAQ

• What is Trust Infrastructure?

  Trust Infrastructure is a connected system that helps businesses decide who to trust, update those decisions as risk changes, and document the reasoning behind them. It combines identity and business verification (KYC and KYB), AML and transaction monitoring, fraud signals, risk scoring, and case management in one layer. Every decision draws on the same data and leaves a consistent audit trail.

• How do you build trust with AI agents?

  Give each agent a verifiable identity, link it to an accountable person or organization, and restrict its permissions to the task at hand. Show users what the agent plans to do, require approval for consequential actions, and keep a record they can review.

• Why is trust a competitive advantage for businesses?

  Clear, trustworthy processes can help a new customer feel comfortable signing up, sharing information, or making a purchase. Reliable experiences also support repeat business, referrals, and a stronger reputation when competitors offer similar products.