Elza Hayyat, Relativity: Hidden to Handled: Detecting Confidential Business Information in aiR for Review

Key Takeaways

• Relativity launches CBI analysis type in aiR for Review
• Generative AI scans attachments for nonpublic contracts, policies
• Reduces manual review time by up to 70%
• Ensures consistent detection across massive data volumes
• Helps firms meet compliance and data‑privacy regulations

Pulse Analysis

The explosion of digital evidence has turned confidential business information (CBI) into a hidden liability for many organizations. Traditional e‑discovery platforms rely on keyword searches and human reviewers, which struggle to flag nuanced, unlabelled data embedded in multi‑page contracts, policy drafts, or even casual email remarks. Missed CBI can trigger regulatory penalties, competitive harm, or costly litigation, making accurate detection a top priority for legal and compliance teams.

Relativity’s new CBI analysis type in aiR for Review addresses this gap by embedding generative AI directly into the review workflow. The model parses entire document sets, understands context, and flags sensitive clauses without requiring pre‑defined tags. Early testing suggests the AI can cut manual review effort by as much as 70%, delivering faster turnaround times and freeing attorneys to focus on strategy rather than rote scanning. Consistency is another benefit: the AI applies the same criteria across all files, reducing human error and ensuring that no critical information slips through the cracks.

Beyond operational efficiency, the feature aligns with tightening data‑privacy regulations such as the GDPR, CCPA, and sector‑specific rules. Companies that can demonstrate robust CBI controls are better positioned to avoid fines and maintain stakeholder trust. As AI‑driven review tools become mainstream, firms that adopt Relativity’s solution now may set a new standard for proactive risk management, gaining a measurable advantage in both cost containment and regulatory compliance.