BigID announced the industry’s first access control for sensitive data in AI conversations. With these new prompt protection capabilities, organizations can stop data leaks at the source – preventing sensitive information from being exposed through copilots, chatbots, and AI assistants.
AI adoption has transformed how employees interact with data. Sensitive PII, financial records, and regulated information are no longer just stored or shared – they’re flowing through prompts and responses. The result: a new frontier of risk that legacy DLP and security tools were never built to handle. Without visibility or controls, enterprises face growing threats of data exfiltration, insider misuse, compliance violations, and reputational harm.
BigID closes this gap by pioneering new controls for sensitive data in AI interactions, giving enterprises unified visibility, enforcement, and protection across every stage of the data lifecycle. Organizations can enforce privilege rights in AI conversations, redact or mask sensitive values on the fly, and accelerate investigations with full visibility into violations — all while keeping AI tools functional and trusted.
Key Takeaways
- Reduce data leakage risk: Prevent sensitive data exfiltration with redaction and masking policies that preserve context while protecting underlying information.
- Gain visibility into AI conversations: Detect and highlight violations involving PII, financial data, and regulated content across prompts and responses.
- Extend access control to AI apps: Enforce privilege rights and prevent unauthorized users from viewing or sharing sensitive data in prompts or responses.
- Accelerate investigations: Leverage alerts, conversation timelines, and user attribution to speed up incident response.
“AI introduces a new challenge: what happens when sensitive data like employee payroll ends up in a model and employees without privileges try to access it?” said Dimitri Sirota, CEO of BigID. “With expanded access control, we can stop that data from being exposed at the inference stage, enforce privilege rights, and apply safe-AI labeling so AI models only consume approved data. No one else in the market is tackling this problem the way we are, and it’s critical to making AI adoption safe and trusted.”
