Private AI (now Limina AI)
Limina AI (rebranded from Private AI) builds PII/PHI de-identification technology that strips personal data out of text, images, audio and documents before it reaches LLMs, training pipelines or analytics tools.
Visit Website ↗ + Add to CompareOverview
Founded in 2019 by University of Toronto PhD candidate Patricia Thaine, Private AI built a reputation as a specialist PII/PHI detection-and-redaction engine for unstructured data, later becoming a common building block other AI-security and data-governance vendors integrated to keep personal data out of prompts, training sets, and logs. The company has since rebranded to Limina AI.
The platform detects and redacts (or generates synthetic replacements for) personal data across text, images, audio and documents, targeting regulated verticals including pharma and life sciences, healthcare, financial services, contact centers and insurance. It carries ISO and AICPA-aligned certifications aimed at compliance-sensitive buyers.
The company raised roughly $8 million-plus with participation from Microsoft’s M12 and BDC Capital, and is available through channels including AWS Marketplace.
Innovation Matrix Assessment
Has iterated its core redaction technology across modalities (text, image, audio, document) over several years, including a recent full rebrand as Limina AI.
Provides a clear, embeddable control for a specific and common AI data-leakage risk (PII/PHI exposure to LLMs), useful as a building block in a broader security stack.
Funding and named-customer visibility are modest relative to other entrants, and the recent rebrand adds some uncertainty about current market messaging and continuity.
De-identification technology is a well-established data-privacy category; applying it to AI/LLM pipelines is a useful adaptation rather than a new security model.
Backing from Microsoft's M12 lends some credibility, but independent, published accuracy benchmarks were not located.
PII/PHI leakage into AI systems remains a persistent compliance and security concern likely to stay relevant as AI adoption in regulated industries grows.
Why CISOs Should Care
Any organization feeding customer or employee data into LLMs needs a reliable way to strip PII/PHI before it leaves the perimeter — Limina AI's redaction engine addresses that specific, high-consequence data-leakage vector.
What Makes It Different
Rather than acting as a general LLM firewall, Limina AI focuses narrowly and deeply on PII/PHI detection and redaction/synthesis, a specialization that has made it a common embedded component inside other vendors' AI-security stacks.
The Matrix Verdict
50/100 — INCREMENTAL INNOVATOR
A mature, narrowly-focused de-identification specialist with credible enterprise backers (Microsoft's M12) and real regulated-industry use cases; the recent rebrand to Limina AI introduces some continuity uncertainty for buyers tracking the company under its former name.
Editorial Note: Claims vs. Verified Findings
Redaction accuracy and industry-fit claims are vendor-stated; independent benchmarking of detection accuracy against alternative PII-redaction tools was not found.
Sources
Alternatives to Private AI (now Limina AI)
Adaptive Security
AI-driven platform that simulates deepfake, voice, and multichannel social-engineering attacks to train and test organizations against next-generation phishing.
Quilr
Early-stage agentic AI security startup building a 'Service-as-Software' platform to guard against human-related breaches and secure AI agent…
Zenity
Governance and security platform for AI agents and low-code/no-code development, securing agent identity, permissions and behavior across the…
Tenzai
An agentic AI penetration testing startup building autonomous 'AI hackers' to find and validate exploitable vulnerabilities at a…
Reco
Reco secures the "agentic ecosystem" — mapping what AI agents can access across SaaS and enterprise apps, detecting…
Charm Security
Agentic AI workforce that investigates and intervenes on scams and fraud in real time, reading manipulation and intent…