AI, data privacy, and regulation, minus the noise.Get the insights that matter, straight from Limina.
Discover why de-identified data actually enables AI model accuracy. Explore peer-reviewed research on MIMIC, GatorTron, and clinical NLP utility.
Under the HIPAA Privacy Rule, the Expert Determination method allows organizations to share data while retaining greater utility than the rigid Safe Harbor standard. However, because the regulation doesn't mandate specific degrees or licenses, many organizations struggle to identify who is truly qualified to serve as their expert.
Private AI has officially rebranded as Limina, a name derived from the Latin word for "threshold." This change signals the company's evolving role as the essential boundary between raw, sensitive information and data that is ready for secure use and analysis.
In regulated industries like healthcare and finance, the most valuable data for training AI—such as clinical notes or claim records—is also the most sensitive. Organizations often feel forced to choose between using synthetic data, which lacks real-world richness, or public datasets, which rarely fit specific use cases.
Navigating data privacy requires more than just removing names. Understanding the technical and legal boundaries between de-identification, anonymization, and pseudonymization is critical for compliance with HIPAA and GDPR. This guide clarifies these often-confused terms and provides a framework for choosing the right method based on your specific use case.
This guide explores the critical process of data de-identification, a regulatory necessity for organizations handling sensitive PII, PHI, and PCI data. It details the methodologies required to transform identifiable datasets into compliant, low-risk information suitable for research, AI training, and analytics.
As AI adoption accelerates, privacy can no longer be an afterthought. This post explores the strategic importance of advanced PII detection—showing why regex, open-source, and general-purpose solutions fail, and how specialized AI like Private AI provides comprehensive coverage across PII, PHI, and PCI. Discover why privacy must be the foundation of both compliance and trust in AI-driven businesses.
Contact centers, chat logs, and email systems carry some of the most sensitive customer data a business handles. This article breaks down the unique PII detection challenges across each channel, shares benchmark performance data, and explains why purpose-built AI is the only reliable path to compliance and customer trust.
Healthcare records contain some of the most sensitive and structurally complex personal information in existence. This article examines why medical data is the ultimate stress test for PII detection systems, breaks down benchmark performance across major providers, and explains how Limina's purpose-built approach handles free-form clinical notes, PHI entity coverage, and HIPAA compliance where general-purpose tools fall short.
Choosing the wrong PII detection vendor can expose your organization to serious compliance and security risks. This post outlines Limina's research-based benchmarking methodology, covering datasets, precision and recall metrics, and real-world testing, so you can evaluate solutions with confidence.
Traditional methods like regex fall short when it comes to detecting hidden PII in real-world contexts such as call transcripts and unstructured data. This post explores the risks of missed PII, why open-source solutions struggle, and how AI-driven approaches provide the accuracy and scalability needed to protect sensitive information and maintain trust.
New technologies like ambient listening, wearables, and telemedicine are generating more patient data than ever before. Here is what that means for healthcare organizations and why turning that data into secure, actionable insights is now a strategic imperative.
Healthcare data is messy, ambiguous, and deeply human. That's why de-identifying it correctly requires more than pattern-matching AI. It requires linguistics. Learn why Limina's linguistics-first approach produces more accurate, context-aware results for healthcare organizations working with sensitive unstructured data.
Healthcare data has the potential to transform patient care—but most strategies never get off the ground. Here's why unstructured data, siloed systems, and outdated manual processes are killing your data strategy before it starts, and what to do about it.
Limina is proud to announce its integration with NVIDIA NeMo Guardrails to bring advanced privacy and compliance capabilities to enterprises leveraging large language models (LLMs).
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