How Private AI can help the Public Sector to Comply with the Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024

Ontario’s Bill 194, formally known as the Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024, represents a crucial legislative shift, aiming to fortify digital security and elevate trust within public sector entities. This act is significant not only for its broad coverage, which includes institutions under the Freedom of Information and … Read more

A Comparison of the Approaches to Generative AI in Japan and China

In the rapidly evolving landscape of generative AI, distinct regulatory and ethical approaches have emerged, reflecting the values, ambitions, and constraints of various global players. We previously delved into the contrasting strategies of the United States and the European Union, two titans in the realm of artificial intelligence. Today, we broaden our lens to encompass … Read more

Updated OECD AI Principles to keep up with novel and increased risks from general purpose and generative AI

On May 3, 2024, the OECD released updated AI Principles that build upon the 2019 version with some notable differences that respond to risks emerging from latest technological developments such as general purpose and generative AI systems. This article contains a summary of the changes as well as a line-by-line comparison of the old and … Read more

Is Consent Required for Processing Personal Data via LLMs?

Under the General Data Protection Regulation (GDPR), consent is only one of the legal bases that can be used to process personal data, including when using Large Language Models (LLMs) to boost the efficiency of processing personal information. While obtaining consent has its advantages, for example the possibility of a clear audit trail if consent … Read more

The evolving landscape of data privacy legislation in healthcare in Germany

The healthcare sector has witnessed a remarkable evolution in data privacy legislation from the 1970s to the present, mirroring the technological innovations of the time. The journey we trace here using Germany as an example illustrates how laws have struggled to adapt to protect and make usable sensitive health information against the backdrop of digital … Read more

The CIO’s and CISO’s Guide for Proactive Reporting and DLP with Private AI and Elastic

Being able to manage the data and information within a company’s infrastructure is critical for properly assessing when sensitive information is either being mismanaged or to report an “all clear” when company policies are being followed as intended. As you may be already aware, Private AI provides PII detection and redaction services to enable companies … Read more

The Evolving Landscape of Health Data Protection Laws in the United States

The healthcare sector in the United States has seen a profound transformation in its approach to data privacy, paralleling significant technological advancements, in particular the electronic health record (EHR). This article explores the trajectory of health data protection legislation in the U.S., highlighting key developments in EHR development and adoption and ongoing challenges in balancing … Read more

How to Safely Redact PII from Segment Events using Destination Insert Functions and Private AI API

Twilio Segment is a great centralized platform for storing customer data. However, it’s unlikely Segment will be your only application that needs the data. Most of us will need to download customer information from Segment, store it in the data lake, propagate the data to reporting software, and populate a few backend databases. All of … Read more

WHO’s AI Ethics and Governance Guidance for Large Multi-Modal Models operating in the Health Sector – Data Protection Considerations

The WHO’s publication of its Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models from January 18, 2024 is an almost 100 page document, including over 20 pages of footnotes, which takes a deep dive into the risks and challenges of Large Multi-Modal Models (LMMs) developed, provided, and/or deployed for health … Read more

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Language Packs

Expand the categories below to see which languages are included within each language pack.
Note: English capabilities are automatically included within the Enterprise pricing tier. 

French
Spanish
Portuguese

Arabic
Hebrew
Persian (Farsi)
Swahili

French
German
Italian
Portuguese
Russian
Spanish
Ukrainian
Belarusian
Bulgarian
Catalan
Croatian
Czech
Danish
Dutch
Estonian
Finnish
Greek
Hungarian
Icelandic
Latvian
Lithuanian
Luxembourgish
Polish
Romanian
Slovak
Slovenian
Swedish
Turkish

Hindi
Korean
Tagalog
Bengali
Burmese
Indonesian
Khmer
Japanese
Malay
Moldovan
Norwegian (Bokmål)
Punjabi
Tamil
Thai
Vietnamese
Mandarin (simplified)

Arabic
Belarusian
Bengali
Bulgarian
Burmese
Catalan
Croatian
Czech
Danish
Dutch
Estonian
Finnish
French
German
Greek
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Italian
Japanese
Khmer
Korean
Latvian
Lithuanian
Luxembourgish
Malay
Mandarin (simplified)
Moldovan
Norwegian (Bokmål)
Persian (Farsi)
Polish
Portuguese
Punjabi
Romanian
Russian
Slovak
Slovenian
Spanish
Swahili
Swedish
Tagalog
Tamil
Thai
Turkish
Ukrainian
Vietnamese

Rappel

Testé sur un ensemble de données composé de données conversationnelles désordonnées contenant des informations de santé sensibles. Téléchargez notre livre blanc pour plus de détails, ainsi que nos performances en termes d’exactitude et de score F1, ou contactez-nous pour obtenir une copie du code d’évaluation.

99.5%+ Accuracy

Number quoted is the number of PII words missed as a fraction of total number of words. Computed on a 268 thousand word internal test dataset, comprising data from over 50 different sources, including web scrapes, emails and ASR transcripts.

Please contact us for a copy of the code used to compute these metrics, try it yourself here, or download our whitepaper.