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

Comply with US Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence using Private AI

biden trustworthy AI

The Biden-Harris Administration recently enacted a sweeping Executive Order to forge America’s path in responsible AI development, encouraging both innovation and risk mitigation. The Executive Order spells out a multi-faceted plan touching upon AI safety and security, privacy, equity, civil rights, and more, with profound implications for organizations that are already embedded in the AI … Read more

How to Comply with EU AI Act using PrivateGPT

eu ai act

The recently amended EU AI Act proposal we introduced in this blog post, would regulate “foundational models,” defined in Art. 3(1c) as “an AI model that is trained on broad data at scale, is designed for generality of output, and can be adapted to a wide range of distinctive tasks.” This blog post sets out … Read more

Navigating the Privacy Paradox: A Guide to Ethical Fine-Tuning of Large Language Models

fine tuning llm

In the field of artificial intelligence, Large Language Models (LLMs) such as GPT-4 stand out as a major innovation, proving useful in a range of areas including automated customer support and creative content generation. Nonetheless, there exists a notable challenge in leveraging the capabilities of these models while also maintaining data privacy. This blog aims … 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.