Key Highlights

• The major AI security risks emerging across banking, financial services, and insurance
• How employee AI usage, AI applications, and Shadow AI create new financial data exposure paths
• The four key BFSI AI risk vectors: financial data leakage, prompt injection, Shadow AI proliferation, and model manipulation
• Why traditional DLP, SIEM, CASB, and endpoint controls are blind to natural-language AI interactions
• Real-world scenarios involving employee financial data leakage and prompt injection against financial AI applications
• How Guardia, Armor, and Vector secure employees, AI applications, agents, and MCP-connected workflows
• How Smart Redact and PCI Guard help prevent PAN, UPI, IFSC, account, cardholder, and other financial data from reaching external AI models
• How LangProtect supports PCI DSS 4.0, DORA, GDPR, RBI AI Framework, and SEBI compliance evidence and audit requirements
• How organizations can generate audit-ready AI security evidence while maintaining employee productivity and application performance