From Data Exposure to Trusted AI: Building Resilient Data Security in Financial Services

PII, PAN, PCI, KYC, AML, and trading records are the top examples of financial data that are considered the most sensitive and highly-regulated data across diverse industries. However, when this data is moved across systems or environments, fed to AI models, shared across borders, or replicated in test environments, it creates data exposure pathways in its wake.

The average cost of a data breach in the financial sector is $6.08 million. However, the greatest loss is trust, from partners, customers, and regulators.

The solution is automated discovery and visibility of sensitive data across all environments, controlling who can access it, and regulating its use by AI agents and copilots.

We’ve put together this whitepaper to help CISOs, CDOs, and BFSI security leaders understand how they can reduce risk, simplify audits, and enable accelerated AI innovation in finance by considering some of the industry’s best-practices.