BigID, the leading platform for data security, privacy, compliance, and AI governance, announced Data Cleansing for AI, a new capability designed to help organizations reduce AI risk by removing high-risk and sensitive data before it enters generative AI tools and large language models (LLMs). This enables security teams to build safer, more trustworthy AI pipelines without slowing innovation.
Data Cleansing for AI empowers organizations to redact or tokenize sensitive information at scale across both structured and unstructured data. This helps prevent confidential data from being embedded into model outputs, leaked in prompts, or misused in downstream AI applications.
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With native support for emails, PDFs, collaboration files, and databases, BigID gives teams the control they need to secure data before it ever reaches AI. Security teams can take proactive action, apply policy-based controls, and continuously reduce exposure across their AI initiatives.
Key Takeaways
- Automatically remove or tokenize sensitive and high-risk data before it’s used in AI
- Strengthen GenAI pipeline security with pre-cleansed, policy-compliant datasets
- Reduce exposure to data leakage, prompt injection, and unauthorized use
- Support structured and unstructured formats, including PDFs, emails, and SaaS files
- Part of BigID’s Secure Data Pipeline solution with GenAI Catalog, Search, and Safe-for-AI Labeling
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“Every AI pipeline is only as secure as the data behind it,” said Nimrod Vax, Chief Product Officer and Co-Founder at BigID. “With Data Cleansing for AI, we’re giving security teams the power to take action so they can protect sensitive data, reduce risk, and drive AI initiatives with confidence.”
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