Building A Secure AI Foundation For Financial Services
In this whitepaper, you’ll find:

AI adoption is accelerating across banking, trading, and wealth management. But without proper governance, sensitive data such as PII, trading strategies, and proprietary models can be exposed through Shadow AI, prompt injection, or misconfigured access controls.

Traditional security tools like DLP and CASB focus on infrastructure and data movement but lack insight into AI intent and context. This creates blind spots around how data is used by AI systems, agents, and copilots.

Shadow AI refers to unsanctioned AI tools, models, or agents used by employees without security oversight. These tools may access regulated financial data, increasing the risk of data leakage, compliance violations, and model misuse.

Yes. A unified governance layer decouples security policies from individual AI providers, allowing teams to adopt new models and agents quickly while maintaining consistent controls and risk management.

By mapping data access and AI usage to regulatory frameworks like GLBA, SOX, PCI DSS, and DORA, organizations can maintain continuous compliance and generate audit-ready evidence automatically.