Using AI to flag privileged documents in large litigation

When a matter involves terabytes of email, manual privilege review becomes the slowest and most expensive part of discovery. AI-assisted privilege review is designed to narrow that burden by predicting which documents are most likely privileged, so attorneys focus their time where it counts.

In practice, several techniques work together:

• Models trained on attorney decisions to rank documents by likelihood of privilege

• Detection of lawyer names, law-firm domains, and legal-advice language to surface candidates

• Communication and thread analysis to catch privilege across email chains and attachments

• Consistency checks that flag documents coded differently from similar ones

No tool should make the final privilege call on its own. The defensible pattern is AI for prioritization and quality control, with qualified reviewers confirming designations and building the privilege log. Accuracy also depends on good inputs - clean data processing, sensible search terms, and a validation step such as sampling to measure precision and recall before relying on the results.

Because vendor capabilities and accuracy claims vary by data set and change over time, validate any platform on a representative sample of your own documents rather than on marketing figures.

For more on running AI review at this scale, see AI eDiscovery Review for Cases Over 100,000 Documents.

Claira is an AI eDiscovery platform that applies classification and language models to help teams prioritize privilege review and reduce manual hours while keeping human reviewers in control. See how it works on your data.