Private AI
AI governance tools
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What is Private AI
Private AI is a privacy-preserving AI data processing product focused on detecting and redacting sensitive information (PII/PHI and other identifiers) before data is used with analytics or generative AI systems. It is typically used by engineering, security, and data teams to reduce the risk of exposing regulated or confidential data when sending prompts, documents, transcripts, or logs to third-party or internal models. The product is commonly deployed as an API/service that can be inserted into data pipelines and application workflows, with options to run in controlled environments to keep data local.
PII/PHI detection and redaction
The product centers on identifying sensitive entities and transforming them through redaction or masking before downstream processing. This supports common governance requirements such as minimizing data exposure and reducing the likelihood of regulated data being sent to external AI services. It is well-aligned to use cases like prompt filtering, document processing, and contact-center transcript sanitization.
Fits into AI workflows
Private AI is designed to be embedded into application and data workflows rather than used only as a policy documentation tool. This makes it practical for teams that need runtime controls for AI inputs/outputs, including pre-processing prompts and post-processing model responses. It complements broader governance programs by enforcing privacy controls at the point of use.
Deployment control options
Private AI is commonly positioned for deployment in environments where organizations want to limit data egress, such as private cloud or on-premises setups. This can help organizations meet internal security requirements and data residency constraints. It also supports architectures where sensitive data must remain within a controlled boundary while still enabling AI use cases.
Narrower scope than full governance
The product primarily addresses privacy controls (detection/redaction) rather than end-to-end AI governance. Organizations may still need separate capabilities for model inventory, risk assessments, policy management, approvals, and audit workflows. As a result, it often functions as one component in a broader governance stack.
Accuracy depends on data context
Entity detection and redaction quality can vary by domain, language, and data format (free text, PDFs, chat logs, code, etc.). Teams typically need to validate performance on their own datasets and tune rules or configurations to reduce false positives/negatives. Over-redaction can reduce utility, while under-redaction can leave residual risk.
Integration and operational overhead
Embedding redaction into multiple applications and pipelines can require engineering effort and ongoing maintenance. Teams may need monitoring, exception handling, and logging strategies that balance audit needs with privacy requirements. Performance and latency considerations can also arise for real-time use cases.
