In today’s world of generative AI-powered deepfakes and sophisticated fraud, organizations are grappling with a fundamental “trust gap,” in which automation outpaces governance and erodes confidence in digital interactions.
At the same time, adversaries are rapidly weaponizing AI, creating new forms of fraud, faster reconnaissance, and highly adaptive threat campaigns.
Convera’s global business-to-business (B2B) payments network is built on trust — trust in identities, trust in systems, and trust in the integrity of every transaction.
Redefining trust in the age of AI
No longer a single tool deployed by security teams, AI is becoming core infrastructure for companies to reestablish trust, mitigate risk, and maintain resilient commercial relationships.
AI is so prevalent that both businesses and end users of fintech solutions have come to appreciate its value. AI is enhancing confidence in an era that’s been maligned by bad actors.
AI is especially great at solving existing business problems, such as increasing efficiency, transparency, and resiliency. This transformation goes a long way toward closing the trust gap and rebuilding confidence in a wide array of business practices, particularly cross-border payments.
The new attack surface: Where AI creates risk
However, the speed of attackers is now far outpacing a business’s ability to build trust. Digital payments have already expanded the attack surface, and AI compounds this exposure by introducing new threat vectors:
- Prompt injection attacks exploit AI assistants embedded in workflows, manipulating the model’s logic to leak sensitive data or alter decisions.
- Credential reuse at scale makes AI weapons more affordable as adversaries use automated AI-powered attack frameworks.
- Synthetic identity abuse now includes AI-generated documentation, account histories, and digital fingerprints that closely resemble legitimate entities.
According to infrastructure analysis from IBM, AI systems — both defensive and adversarial — operate at machine speed, dramatically widening the aperture for potential vulnerabilities.
“Our attack surfaces are expanding faster than controls evolve,” as was mentioned on a recent Convergepodcast episode. “Traditional perimeter security is not designed for this landscape.”
Private threat-sharing and the integrity gap
A growing challenge in the security community is the widening integrity gap in private threat intelligence.
Threat feeds vary in quality. Some threats are AI-generated, some are human-curated, and many are a blend of incomplete signals. Meanwhile, regulatory barriers and fragmented practices limit cross-border sharing, even as adversaries coordinate globally. The result is uneven intelligence, slow signal propagation, and mismatched response capabilities.
AI infrastructure depends on broad, accurate, and timely data to remain effective. When threat intelligence is inconsistent, even the most advanced AI systems struggle to classify attacks correctly.
The way you get better at AI is by having more data. But in the current AI arms race, sharing data isn’t as easy. The question then is: Why should companies give data to others when they could use it as a competitive advantage?
Closing the integrity gap requires stronger collaboration between financial institutions, cloud platforms, government regulators, nonprofits, and security vendors. For cross-border payments, it’s the only path toward system-wide resilience.
From defense to proactive detection
The shift from reactive defense to proactive detection is where AI becomes indispensable.
Deloitte’s research emphasizes the importance of continuous oversight and AI systems that detect anomalies long before a human analyst could. For the financial services industry and global payments, this is essential. Fraud, credential misuse, or manipulation of a supplier record can happen in seconds.
When utilized properly, AI can detect threats before damage is incurred. It’s the kind of AI-enabled security posture that we identify as key for resilience in highly digitalized industries.
Trust by design: Building secure payment workflows
Embedding trust from the start is critical not just for cross-border payments, but for the financial services industry in general. This aligns with Deloitte’s view that trust must be engineered into AI systems through design choices, governance, and visibility.
Key trust safeguards can include:
- Verified and authenticated inputs at the moment a transaction or instruction enters the system
- Encrypted data flows across the payment lifecycle
- Adaptive authentication that escalates friction only when risk indicators appear
- Model-driven risk scoring before funds move
- Role-segmented approvals for sensitive changes or settlement modifications
- Immutable audit logging that supports regulatory and forensic requirements
This approach forces trust into the workflow itself. As companies expand their use of AI, they must integrate security and governance directly into their infrastructure, because B2B payments require exactly that level of baked-in resilience.
Who to trust vs. what to trust: Managing AI inputs
AI changes the nature of trust. Historically, we have focused on who to trust: the customer, employee, or supplier. Now we must also determine what to trust: the data, signals, and outputs generated by automated systems.
If organizations can’t verify the origin of data or how models interpret it, trust can deteriorate quickly.
With cross-border payments, this is even more critical. Transaction approvals, fraud scores, identity checks, and system recommendations all rely on input integrity. That’s where your business use case risk matters so much: How much risk can you take with the AI use case that you have? What if it hallucinates? What’s the impact of that on your business?
Future outlook: AI as both risk and defense
According to Convera experts, AI will continue to shape both the risks and defenses of cross-border payments, but the trajectory is ultimately positive.
AI infrastructure allows for secure transactions at speeds and scales the industry has never achieved with people providing oversight, ethical governance, and contextual judgment. Together, they create a hybrid security model that strengthens trust rather than erodes it.
The speed of AI adoption isn’t going to slow down. Businesses need to have open conversations about AI use, the risk, and their defenses. By starting with AI use cases that they understand, businesses can evolve talent around AI development inside their organizations, because it won’t just be a technology shift; it’ll be a business process shift as well.
Listen to this recent episode of the Converge podcast to learn more about building trust in the era of AI.
