⭐︎ Customer Stories
Wald.ai Safeguards Sensitive Payment Data for Leading Financial Institution
AI Security Transformation for a Leading US Payment Processor
A major US-based payment processing company with over 4,000 employees faced significant hurdles in protecting sensitive financial data while adopting AI technologies. Traditional Data Loss Prevention (DLP) systems were inadequate, posing compliance risks and reducing operational efficiency. By implementing Wald.ai, the company achieved:
50%
Productivity Boost
100%
Compliance with financial regulations without compromising security.
Client Overview
1. Industry: Financial Services
2. Company Size: Enterprise (4,000+ employees)
3. Location: United States
4. Primary Challenge: Secure AI adoption while protecting customer payment data and ensuring regulatory compliance
The Challenge: Securing AI in a High-Compliance Environment
Security Vulnerabilities
1. Outdated DLP Systems: Traditional tools couldn’t secure AI-generated outputs or protect unstructured data.
2. Customer Data Exposure: AI tools risked leaking sensitive customer information during support operations.
3. Intellectual Property Risks: Proprietary algorithms and strategic data were vulnerable to accidental exposure.
Regulatory Compliance Challenges
1. PCI-DSS Complexity: AI usage created complications in maintaining payment card industry standards.
2. Sensitive Data Governance: Difficulty in managing data classification and protection across AI workflows.
Operational Inefficiencies
1. Shadow IT: Employees used unapproved AI tools, increasing security and governance risks.
2. Manual Workflows: Security concerns hindered automation of routine, data-intensive tasks.
3. Delayed Responses: Limited AI use led to slower customer service and internal turnaround times.
The Wald.ai Solution: Context-Aware AI Security
Core Security Components
Context Intelligence Engine: Semantic understanding of data to detect nuanced threats and context.
Automated Data Classification: Real-time detection and categorization of sensitive data like PII and payment information.
Smart Redaction: Automatic removal of sensitive elements from AI inputs and outputs.
Role-Based Access Control (RBAC): Fine-grained control over who can access what type of data in AI workflows.
Implementation Highlights
Seamless SSO Integration: Plugged directly into existing workflows and identity systems.
Security Policy Customization: Defined granular access rules and classification protocols for AI use.
Secure Task Automation: Enabled AI-driven support for document reviews and customer interactions.
Employee Enablement: Hands-on training to help teams use AI safely and efficiently.
Measurable Impact: Security Without Sacrificing Speed
Security & Compliance
100% PCI-DSS Compliance: Aligned all AI interactions with industry and regulatory standards.
50,000+ Sensitive Data Points Protected: Including cardholder data, source code, and confidential documents.
Shadow IT Eliminated: Centralized control over all AI tools and workflows.
Operational Gains
50% Increase in Overall Productivity: Teams automated routine tasks with peace of mind.
35% More Customer Inquiries Resolved per Hour: Personalized AI responses improved service volume and quality.
60% Faster Response Times: Secure automation accelerated turnaround across departments.
Wald.ai helped us securely leverage AI while maintaining compliance. Our teams now use AI more efficiently, without security concerns. The platform has become an integral part of our operations.
⎯ CTO, Leading Payment Processing Company
 Before Wald.ai |  After Wald.ai |
|---|---|
Traditional DLP Limitations Traditional DLP systems couldn't handle GenAI usage, failing at data loss prevention in AI outputs. | Traditional DLP LimitationsContext Intelligence Engine with contextual understanding beyond static redaction. |
Customer Support Security GenAI assistants with sensitive data created severe leakage risks. | Customer Support SecuritySecure AI support with redaction and masking customer data prevents data leakage. |
Data Classification Difficult to secure various data types while enabling AI assistant access. | Data ClassificationAutomated classification isolates sensitive data for proper handling. |
Intellectual Property Protection Increased risk of exposing proprietary information in internal communications. | Intellectual Property ProtectionRobust IP protection for sensitive contracts, strategies, and code. |
Compliance Challenges handling sensitive data under PCI-DSS and other regulations. | ComplianceStreamlined regulatory compliance, reducing penalty risks. |
Employee Productivity Time wasted on data classification and low-value tasks reduced productivity. | Employee ProductivityAI automation improved efficiency, boosting productivity by 50%. |
Response Time Manual responses due to sensitive data restrictions caused slow service. | Response Time60% faster responses through secure data uploads and personalization. |
Security & Privacy Shadow IT and unsecured AI tools posed serious risks. | Security & PrivacyCentralized governance with access controls prevents unauthorized usage. |
Key Takeaways
Secure AI Integration is a Must: Financial firms can’t afford to adopt AI without robust, context-aware security.
1.
Traditional DLP Falls Short: AI security requires real-time semantic analysis—not static redaction.
2.
Compliance is Possible with the Right Tools: Even in high-stakes environments, AI adoption is feasible with built-in safeguards.
3.
Security Doesn’t Mean Sacrificing Productivity: With Wald.ai, efficiency and compliance go hand in hand.
Conclusion
By deploying Wald.ai, this enterprise successfully navigated the complexities of AI adoption in a high-compliance environment. The result: airtight security, full regulatory alignment, and significant productivity gains.
Wald.ai proves that secure AI isn’t just possible—it’s a business advantage.
Wald: Leverage the power of AI Assistants without compromising your privacy and trade secrets.
