LGPD do Brasil: Anonimização, Pseudonimização e Solicitações de Acesso à Informação

lgpd brazil

Traduzido por Patrícia Graciano. Read in English here A Lei Geral de Proteção de Dados (LGPD), é o posicionamento do Brasil à privacidade de dados. Ela  determina as regras que as organizações que lidam com dados pessoais – e que não anonimizam esses dados – devem seguir. Nesse post, analisaremos a anonimização de dados, a … Read more

Brazil’s LGPD: Anonymization, Pseudonymization, and Access Requests

lgpd brazil

Leia em português aqui The Lei Geral de Proteção de Dados (LGPD), Brazil’s answer to data privacy, determines the rules organizations that handle personal data have to follow, unless they anonymize the data. This article delves into data anonymization, pseudonymization and what that means for data processing activities, as well as the LGPD’s stringent response … Read more

Canada’s Principles for Responsible, Trustworthy and Privacy-Protective Generative AI Technologies and How to Comply Using Private AI

Canada generative AI

The realm of generative AI, encompassing technologies that generate content like text, images, and videos, have seen a significant surge in usage and development. In response, the Office of the Privacy Commissioner of Canada (OPC) has introduced key privacy principles tailored to generative AI technologies on December 7, 2023. This framework is crucial for organizations … Read more

Data Integrity, Data Security, and the New NIST Cybersecurity Framework

Data Integrity Security

Data security and integrity are two critical concepts in the world of cybersecurity. While they are often discussed together, they have distinct definitions and implications. Data security encompasses the processes and technical safeguards established to preserve data integrity, as well as confidentiality and availability of data. In this article, we will dive into the details … Read more

Safeguarding Privacy with Commercial LLMs

Privacy LLM

In the era of digital transformation, Large Language Models (LLMs) have emerged as powerful tools for businesses, enabling them to automate tasks, generate insights, and improve decision-making. However, the use of these models also brings forth significant privacy challenges. Despite the inherent privacy protections in enterprise solutions like Microsoft Azure OpenAI Services, residual privacy issues … Read more

Cybersecurity in the Public Sector: Protecting Vital Services

Cybersecurity in the public sector

67% of government agencies have increased their financial commitment to digital transformation. Long lines and endless paper documents no longer suffice  – citizens now expect public services with less hassle and technology seamlessly embedded. However, this increased reliance on technology makes government agencies and institutions prime targets for cyberattacks – 30% of public sector agencies struggle … Read more

Privacy Impact Assessment (PIA) Requirements under Law25

PIA Law25

Quebec’s commitment to modernizing its data protection measures is evident in the provisions of Law25, the most important provisions of which came into effect on September 22, 2023. A significant component of this new legislation is the requirement for private companies to conduct Privacy Impact Assessments (PIAs). While already mandatory in certain circumstances for public … Read more

Elevate Your Experience with Version 3.5

Version 3.5 features

Hello, dear community! We are thrilled to announce the release of Version 3.5. Packed with new features, improvements, and fixes that are crafted based on your feedback and our commitment to enhancing your experience and productivity. Let’s dive in and explore what’s new and enhanced in this version! Now Available on Azure Marketplace Great news for … Read more

Fine-Tuning LLMs with a Focus on Privacy

fine tuning llms

This blog has an accompanying Jupyter Notebook! Access the notebook Large Language Models (LLMs) like Azure’s OpenAI service have become pivotal technology, enabling machines to understand and generate human-like replies to questions posed in a chat format. For organizations looking to augment those models with domain specific knowledge or for traditional ML applications such as … Read more

GDPR in Germany: Challenges of German Data Privacy (Part 2)

GDPR in Germany

In the first part of this blog series, we discussed data privacy in Germany and the various obstacles associated with redacting Personally Identifiable Information (PII) in the German language. Now, in the second installment, we further explore the multifaceted landscape of German data privacy, shedding light on challenges that emerge not just from linguistic intricacies, … 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.