Securing AI Applications for Financial Services

Artificial Intelligence is transforming financial services—powering credit scoring, fraud detection, personalized banking, and algorithmic trading. But innovation brings risk. AI now powers core workflows and critical decisions, often through third-party APIs or informal channels that bypass traditional controls. Over 85% of financial institutions use or plan to use GenAI—mirroring earlier shifts like BYOD and cloud, but with far greater stakes.

This guide offers a structured approach to AI risk management—spanning discovery, testing, runtime defense, and governance. It aligns with modern AI stacks and the regulatory, security, and audit demands of financial services. Sections of the comprehensive guide include:

• Understanding the AI Security Stack
• Defining a New MLSecOps Pipeline
• What Are the Risks to AI?
• Step 1: Discover Your AI Footprint
• Step 2: Test AI Models and Assess Risks
• Step 3: Secure AI Development and Deployment
• Step 4: Prevent Data Leaks and Threats During Runtime
• Step 5: Protect the Full AI and Application Stack
• Step 6: Operationalize AI Security
• Conclusion: Building Resilience in the AI Era

Each section details:

• Key Questions You Should Ask
• Challenges
• AI Security Best Practices
• How PointGuard Meets these Challenges