Private AI 4.0 – Checksum

Introducing Private AI’s Checksum Validation: Protecting Sensitive Data with Precision

At Private AI, we believe in enabling organizations to harness the full potential of their data while upholding the highest standards of privacy. The new Checksum Validation feature in our 4.0 release enhances the secure handling of sensitive information, ensuring accurate detection and management of Credit Card Numbers (PCI) within unstructured datasets. This powerful tool helps maintain data integrity and supports compliance efforts across healthcare and clinical research workflows.

Why Checksum Validation Is Essential for Data Privacy

In healthcare and clinical research, protecting sensitive patient data is paramount. Traditional detection methods often fall short, especially when data appears without context or in incorrect formats. Checksum Validation provides a robust solution to these challenges, ensuring that credit card numbers and other sensitive financial data are accurately identified and securely handled.

With Checksum Validation, you can:

  • • Accurately Identify Sensitive Data: Uses advanced validation algorithms like the Luhn check to ensure that detected credit card numbers are legitimate.
  • • Ensure Context-Aware Detection: Identifies valid credit card numbers even when they lack surrounding context or are embedded in mislabeled fields.
  • • Strengthen Patient Privacy: Reduces the risk of exposing sensitive patient data, supporting your commitment to privacy in research and healthcare.

Designed for Healthcare and Research Data Workflows

Whether you’re working with unstructured patient data, processing large datasets, or conducting compliance audits, Checksum Validation ensures the accurate detection and secure management of sensitive information. This feature is designed to help healthcare, clinical research, and patient-driven organizations to:

  • Enhance Data Privacy: Safeguard sensitive financial information like credit card numbers across your data ecosystem.
  •  
  • Support Compliance: Streamline your compliance with regulations like HIPAA and PCI DSS by ensuring sensitive data is correctly identified and handled.
  •  
  • Improve Data Governance: Gain better control over your sensitive data assets by accurately identifying and managing their location within your systems.

How Checksum Validation Works

Validates Detected Numbers: When sequences resembling credit card numbers are identified, Checksum Validation applies algorithms like the Luhn check to confirm their validity and authenticity.

Effortless Integration: Easily integrate Checksum Validation into your existing data workflows and privacy tools, ensuring seamless operation without disruption.

Benefits of Using Checksum Validation

Reliable Detection

Achieve precise identification of valid credit card numbers, ensuring sensitive financial data is accurately recognized.

Enhanced Privacy Protection

Minimize the risk of accidental exposure of sensitive information, bolstering your data security and compliance efforts.

Facilitates Compliance

Ensure adherence to regulatory standards such as PCI DSS and HIPAA, safeguarding patient data and maintaining trust.

Ready to Protect Your Sensitive Data with Confidence?

Checksum Validation gives you the power to confidently manage sensitive data, ensuring it is accurately detected, validated, and securely handled. By incorporating this feature into your data privacy strategy, you can confidently safeguard patient information and ensure compliance with critical privacy regulations.

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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.