Jul 2026
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Customer stories

How a U.S. Payments Company Adopted Generative AI While Strengthening PCI Data Protection and AI Governance

Generative AI is transforming software development, customer support, operations, and fraud analysis across the payments industry. However, employees frequently work with highly sensitive information, including payment card data, customer financial records, API credentials, and proprietary transaction data. This customer story explores how a U.S.-based payments company adopted generative AI securely by deploying Wald AI as its enterprise AI security layer. Through contextual data sanitization, prompt protection, centralized governance, and comprehensive audit logging, the company enabled employees to use AI confidently while reducing the risk of exposing regulated financial information.

Productivity Boost
Compliance with financial regulations
Iron Clad Security
Multi LLM Approach

The Challenge

As generative AI became part of everyday work, employees across engineering, customer support, operations, product, and compliance began using AI assistants to accelerate routine tasks.

Developers used AI to debug code.

Support teams summarized customer cases.

Operations teams analyzed payment failures.

Product managers drafted documentation.

While these use cases improved productivity, they also introduced an entirely new category of security risk.

Employees were unknowingly sharing sensitive information with public AI models, including payment card details, customer personally identifiable information (PII), API keys, transaction records, merchant information, and internal business data.

For a payments company, protecting this information is critical.

Leadership needed confidence that AI adoption aligned with the organization’s broader security program and supported controls expected under PCI DSS 4.0, the Gramm-Leach-Bliley Act (GLBA), and internal governance policies for handling financial data. AI usage also needed to be visible, auditable, and governed rather than relying on individual employee judgment.

The company faced several key challenges:

  • No visibility into what employees were sharing with AI.
  • No centralized controls over approved AI models.
  • No audit trail for AI interactions.
  • Risk of payment card data or customer PII being included in prompts.
  • Traditional DLP solutions were designed for email and file transfers, not conversational AI.

The company wanted employees to benefit from AI without increasing regulatory or operational risk.

Governance Objectives

Before expanding AI adoption, the company established four priorities.

Protect regulated payment and customer data before it reached external AI models.

Create centralized governance over AI usage across the organization.

Maintain comprehensive audit logs for security investigations, compliance reviews, and customer due diligence.

Reduce human error by automatically identifying and sanitizing sensitive information before AI processing.

The Solution

The company implemented Wald AI as its secure AI gateway, enabling employees to continue using leading AI models while automatically enforcing enterprise security policies.

Every prompt and uploaded document passed through Wald’s Context Intelligence engine before reaching the AI model.

Instead of relying solely on predefined keywords or regular expressions, Wald analyzed the context of each prompt to identify sensitive financial information, payment card data, customer identifiers, API credentials, authentication tokens, internal business information, and proprietary transaction data.

Sensitive information was automatically sanitized while preserving enough context for AI models to generate accurate responses.

At the same time, Wald introduced centralized governance across the organization’s AI ecosystem.

Security teams gained complete visibility into:

  • Which employees were using AI.
  • Which AI models were accessed.
  • What sensitive data was detected.
  • What information was sanitized.
  • Which governance policies were enforced.
  • Complete audit logs for every AI interaction.

Rather than blocking AI, the company established a secure framework that allowed innovation while strengthening oversight.

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Results

By implementing Wald AI, the payments company established a secure foundation for enterprise AI adoption.

Employees continued benefiting from generative AI while leadership gained the visibility and controls needed to manage organizational risk.

Key Outcomes

  • Secure enterprise-wide adoption of generative AI.
  • Automatic sanitization of payment card data, PII, and confidential financial information.
  • Reduced risk of sensitive information being exposed to external AI models.
  • Centralized governance across all AI interactions.
  • Comprehensive audit trails supporting security reviews and compliance activities.
  • Improved visibility into employee AI usage.
  • Consistent enforcement of AI security policies.
  • Greater confidence in expanding AI across engineering, operations, and customer support.

For payments companies, the challenge is not whether employees will use generative AI. The challenge is ensuring AI is used responsibly without exposing regulated financial information.

By combining contextual data sanitization, prompt protection, centralized governance, and comprehensive audit logging, Wald AI enabled this payments company to adopt AI while strengthening its overall security posture.

Instead of treating AI as an unmanaged risk, the company established a governed, enterprise-ready AI environment that supports innovation while helping protect payment data, customer information, and organizational trust.

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