BigID, a leader in data security, privacy, compliance, and AI governance, has unveiled the industry’s first access control for sensitive data in AI conversations. The platform now offers prompt protection capabilities that prevent sensitive information from being exposed via AI copilots, chatbots, and other AI assistants.
As AI adoption grows, employees are no longer just accessing stored data—they’re interacting with it directly through prompts. Sensitive PII, financial records, and regulated content are now flowing through AI interfaces, creating a new frontier of risk that legacy DLP and security tools were never built to manage.
“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.”
How It Works
BigID’s new controls allow enterprises to enforce privilege rights, redact sensitive data, and monitor AI interactions across the data lifecycle. Key features include:
- Reduce Data Leakage Risk: Redact or mask sensitive values while preserving context, preventing exfiltration in prompts and responses.
- Gain Visibility: Detect violations involving PII, financial data, and regulated content across all AI interactions.
- Extend Access Control: Prevent unauthorized users from accessing sensitive data in AI applications.
- Accelerate Investigations: Use alerts, conversation timelines, and user attribution to speed up incident response and compliance audits.
By introducing these AI-native security controls, BigID aims to make enterprise AI adoption safe, compliant, and trustworthy without compromising productivity or model functionality.
This move positions BigID as the first vendor addressing prompt-level data governance, a growing concern as organizations deploy AI at scale. With sensitive data increasingly entering AI workflows, the company’s approach could set a new standard for protecting enterprise information in generative AI environments.
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