AI Security Blueprint - Financial Services Edition

Overview

Financial services AI security protects high-value targets: trading algorithms worth millions in competitive advantage, fraud detection systems protecting billions in transactions, and credit models making decisions that regulators scrutinize. Attackers are sophisticated and well-resourced. Regulators expect rigorous security controls with documented evidence.

This comprehensive security blueprint is built specifically for financial services AI. It provides security architecture that protects proprietary models, secures trading and fraud systems, and satisfies regulatory requirements—while enabling the low-latency performance that financial systems demand.

What's Inside

• Financial AI Threat Landscape: Financial-specific threat analysis covering trading system attacks, fraud detection evasion, model theft for competitive advantage, market manipulation through AI compromise, and supply chain attacks on financial AI vendors
• Regulatory Security Requirements: Mapping security requirements from FFIEC, OCC, SEC, FINRA, and other financial regulators to AI systems, with specific attention to examination expectations
• Security Architecture: Reference architecture for financial AI including network segmentation for trading systems, DMZ design for market data, and secure connectivity patterns
• Data Protection: Protecting financial data in AI including customer data, trading data, market data, and proprietary model training data
• AI-Specific Security Controls: Financial-specific AI controls including model access controls, inference security for production models, and protection against model extraction attacks
• Trading System Security: Security architecture for AI-powered trading including latency-aware security controls, market data protection, and order management system security
• Fraud and AML AI Security: Protecting fraud detection and AML AI including adversarial robustness, model integrity, and secure integration with transaction systems
• Third-Party Risk Management: Security requirements for AI vendors including due diligence, ongoing monitoring, and fourth-party risk considerations
• Incident Response: Financial AI incident response including regulatory notification requirements, market impact assessment, and coordination with financial sector ISACs

Who This Is For

• Financial Services CISOs responsible for AI security
• Technology Risk Officers managing AI in risk frameworks
• Trading Technology leaders securing algorithmic trading
• Fraud/AML Technology teams protecting detection systems
• Compliance Officers ensuring regulatory security requirements

Why This Resource

Financial regulators expect documented security controls with evidence of implementation. This blueprint provides architecture and controls that satisfy examination requirements while protecting systems that attackers actively target for financial gain.

Every control accounts for financial context: latency requirements for trading systems, regulatory expectations for documentation, and the sophisticated threat actors that target financial institutions.

FAQ

Q: How do we secure trading AI without adding latency?

A: Trading system security specifically addresses latency-aware security: controls that provide protection without adding unacceptable latency, asynchronous security monitoring, and architecture patterns that separate security processing from critical trading paths.

Q: What do regulators expect for AI security?

A: The regulatory security section maps requirements from FFIEC, OCC, SEC, and other regulators to AI systems. It identifies specific examination questions, documentation requirements, and the evidence regulators expect to see for AI security controls.

Q: How do we protect proprietary trading models from theft?

A: Model security covers protection of proprietary algorithms: access controls for model code and weights, detection of model extraction attempts, watermarking for theft detection, and secure deployment practices that minimize exposure of model internals.