The Privacy Layer for Banking

Identifying and minimizing PII, PHI, PCI, and CCI (Confidential Company Information) are critical parts of most internal compliance programs. However, these programs are driven not only by a plethora of regulations, such as PCI DSS, GDPR, Law 25, and the GLBA but also by the different needs of each segment. For example, the data protection needs in Retail banking are vastly different from those in Investment Banking.

‍

Doing so is no easy feat in banking, where data can live hidden within free text fields in databases, embedded files, storage buckets and network drives with handwritten content on PDF scans, Word documents and images. The volumes, variety, and complexity of the data involved mean banks are dealing with which contain messy, disfluent data, OCR errors, and multilingual and multinational information.

Generative AI has the potential to transform the banking industry, but security and regulatory compliance are key concerns when using such systems. This has resulted in cases of multi-million dollar Generative AI investments held up at the POC stage. Private AI unblocks such projects by removing sensitive information before it enters the Generative AI system.

By anonymizing sensitive data, Private AI also reduces the time and effort required by Data Science teams to get access to internal data sources, allowing projects to be accelerated by months.

Legacy DLP and compliance software have traditionally failed to detect unstructured data contained in network drives and storage buckets. Such PII detection systems are based on specific rules, which are difficult to configure and require extensive maintenance.

‍

Private AI’s proprietary technology scans PDFs, Word, PowerPoint, and other document formats within your own environments at a far higher level of accuracy while significantly reducing False Positives. And that’s all out of the box—no configuration or rule creation required, and no sharing data with a third party!

‍

Private AI can even scan databases and data lakes, which often contain large amounts of text such as agent notes and even files, enabling accurate reporting of PII for Privacy Impact Assessments (PIAs), incident response, and risk assessments.