Enigmata Sentinel is a data protection solution that helps safeguard sensitive information when employees use AI tools. It is built on Cipher, Enigmata’s proprietary data protection technology. Cipher also enables institutions to analyze sensitive data and train models while keeping the underlying values encrypted.
For financial institutions, the opportunity and the risk often sit in the same workflow. AI can help employees draft communications, summarize documents and prepare notes, but a copied customer record or an attachment containing account details can expose information unintentionally. At the same time, sensitive institutional data may hold patterns that could improve fraud detection and other analytics, yet distributing readable copies across data teams and processing environments can exceed an institution’s appetite for risk.
“Community banks and credit unions see opportunities to save employees time and learn more from their data, but protecting customer information has to remain central. Enigmata addresses two practical challenges: reducing accidental exposure when employees use AI and making sensitive data usable for analysis. We’re pleased to give our marketplace customers and institutions a way to explore those capabilities,” said Coy Ogle, Head of Partnerships at Finovifi.
Adding protection to employee AI workflows
Built on Enigmata Cipher technology, Sentinel helps protect sensitive information when employees use ChatGPT, Claude, Gemini, Grok and Copilot in supported web-based workflows. Employees continue using familiar chat applications and their existing provider accounts.
Behind the scenes, Sentinel detects sensitive information and replaces it with protected placeholders before those values reach third-party AI providers. Cipher protects the original values so Sentinel can restore the information the user is authorized to see when the AI returns a response.
Sentinel adds another line of defense alongside policies, training and existing security controls, with protection scoped to information types and workflows validated during evaluation. It also records protection and reveal activity.
Putting protected data to work
Cipher technology is also available now for institutional analytics. Normally, sensitive data must be made readable before teams can use it to train a model. Cipher enables teams to build models while the data remains encrypted, helping them learn from valuable information while reducing exposure of the original customer details.
Cipher can also enable faster model training and more accurate predictions. Its protection runs on standard computing infrastructure, without requiring specialized hardware or cloud environments.
For banks and credit unions, potential applications include identifying patterns in transactions and improving fraud detection.
This opens opportunities to use valuable data in projects previously constrained by the risk of exposing sensitive information.
“We built Cipher to help organizations use sensitive information while reducing its exposure. Sentinel brings that technology into employees’ everyday AI workflows, while Cipher’s analytics capabilities help data teams learn from information, they may previously have been reluctant to use. The common foundation is protecting sensitive data while keeping it useful,” said Peter Baer, Co-Founder and CMO of Enigmata.
