How Private AI Can Help Financial Institutions Comply with OSFI Guidelines

The Office of the Superintendent of Financial Institutions (OSFI) has set forth guidelines to ensure that Federally Regulated Financial Institutions (FIs) maintain robust data security, risk management, and operational resilience. Private AI’s advanced machine learning and natural language processing technologies provide helpful tools that can help FRFIs meet these requirements efficiently and effectively. Since the … Read more

The American Privacy Rights Act – The Next Generation of Privacy Laws

For the longest time, the US was one notable outlier in the global trend of developing federal-level comprehensive privacy laws. The nation, (in)famous for its patchworked approach to privacy with many sector-specific and 15 (soon to be 17) states laws that cover privacy protection comprehensively, is now (once again) close to joining the other 137 … Read more

How Private AI Can Help with Compliance under China’s Personal Information Protection Law (PIPL)

China’s Personal Information Protection Law (PIPL) that come into force November 1, 2021 sets out stringent requirements for the handling, processing, and protection of personal information. Organizations operating under this law must navigate a complex landscape of obligations, including limiting data collection, ensuring data security, managing sensitive information, and responding to data breaches. Private AI’s … Read more

PII Redaction for Reviews Data: Ensuring Privacy Compliance when Using Review APIs

In This Blog Understanding PII in Review Data Privacy Legislation Impacting Review Data The Role of PII Redaction in Review APIs Implementing PII Redaction in Review Data Pipelines Why Privacy Matters in Leveraging Review Data User reviews have become a crucial element for businesses seeking to understand consumer sentiment, improve products, and build trust with … Read more

Independent Review Certifies Private AI’s PII Identification Model as Secure and Reliable

An independent evaluation has confirmed that Private AI’s Personal Identifying Information (PII) identification model is both highly effective and secure, now backed by Armilla AI’s Guaranteed Warranty and insurers Swiss Re, Greenlight Re and Chaucer. This validation addresses crucial concerns regarding the reliability and fairness of AI-driven privacy tools, providing assurance to organizations that their … Read more

News from NIST: Dioptra, AI Risk Management Framework (AI RMF) Generative AI Profile, and How PII Identification and Redaction can Support Suggested Best Practices

Acting on its obligations flowing from a 2023 Executive Order, the US Department of Commerce’s National Institute of Standards and Technology (NIST) has recently released two new tools to aid companies developing Generative AI models (GenAI) do so responsibly and securely. Dioptra The first tool is geared towards the GenAI system developers themselves, instead of … Read more

Leveraging Private AI to Meet the EDPB’s AI Audit Checklist for GDPR-Compliant AI Systems

As the European Union continues to strengthen its data protection and artificial intelligence (AI) regulations, organizations are seeking innovative ways to ensure compliance. Private AI, a cutting-edge approach to machine learning that prioritizes data privacy, has emerged as a powerful tool in this landscape. This article explores how Private AI can help organizations adhere to … Read more

Handling Personal Information by Financial Institutions in Japan – The Strict Requirements of the FSA Guidelines

Under the APPI, businesses must adhere to strict rules regarding the processing of personal information, in particular when it comes to the disclosure or transfer of such information. However, in the financial services industry, there are additional rules, the Comprehensive Guidelines for Supervision of Financial Instruments Business operators, etc. (the “Guidelines”),  that increase the bar … Read more

Who is Responsible for Protecting PII?

Protetcing PII

Personally identifiable information (PII) is any data that can be used to identify an individual directly or indirectly, such as their name, social security number, date of birth, gender, ZIP code, and more. If this information falls into the wrong hands, it can lead to identity theft, financial fraud, and other forms of harm to … Read more

Download the Free Report

Request an API Key

Fill out the form below and we’ll send you a free API key for 500 calls (approx. 50k words). No commitment, no credit card required!

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.