Sensitive Data
By demonstrating robust security measures, our businesses can earn customer trust, attract partnerships, and mitigate potential financial and reputational risks from data breaches or non-compliance with regulations. Sensitive Data, an umbrella term for information requiring strict protection, includes PII, PHI, PCI, and IP. Businesses safeguarding sensitive data maintain privacy, prevent fraud, gain a competitive advantage, and enhance business value.
Personally Identifiable Information (PII) refers to data that can identify or locate an individual, either alone or when combined with other information. This includes names, addresses, phone numbers, email addresses, Social Security numbers, and more. Safeguarding PII is crucial to protect privacy, prevent identity theft, and comply with regulations.
Protected Health Information (PHI) is a subset of PII that specifically pertains to an individual’s physical or mental health, healthcare services, or payment information. This can include diagnoses, medical histories, insurance details, and more. Ensuring PHI privacy and security is vital under laws like HIPAA to safeguard patients’ sensitive data.
Payment Card Industry (PCI) data refers to sensitive information associated with credit and debit card transactions. This includes cardholder names, account numbers, expiration dates, and security codes. Organizations handling such data must adhere to the Payment Card Industry Data Security Standard (PCI DSS) to ensure secure processing, storage, and transmission to prevent fraud.
Protecting Intellectual Property (IP) presents unique challenges, including identifying valuable IP assets, preventing internal and external threats, and safeguarding IP in global markets. Organizations must navigate complex legal frameworks, employ robust cybersecurity measures, and foster a culture of IP protection. Successfully securing IP offers value by preserving competitive advantage, fostering innovation, attracting investments, and promoting a company’s reputation for safeguarding proprietary knowledge and creative works.
Industry Example
Pseudonymization
Protecting PII presents challenges including evolving cyber threats, human error, and balancing data accessibility. Pseudonymization is data protection method recommended by the European Data Protection Board that delivers key benefits for businesses:
- Pseudonymized datasets can be compliantly moved outside of legal jurisdiction. (e.g., European Union)
- It delivers Zero Trust principles directly to data (e.g., email field) while preserving data type, length, and format.
- The protected data set maintains referential integrity enabling analytics on protected data.
*DISCLAIMER: This is a dramatized visual example of what your PII data will look like under Protegrity’s protection.
Industry Example
Anonymization
Protecting PHI poses unique challenges due to its sensitivity, value to cybercriminals, and involvement of numerous stakeholders. Healthcare providers must navigate strict regulations like HIPAA, ensure secure storage, and control access to data. Businesses that wish to aggregate and analyze PHI for trends like vaccine efficacy can face challenges anonymization can solve:
- Anonymized datasets permanently remove the ability to identify a data subject contained within it allowing aggregation and industry sharing.
- These datasets offer high-value training sets for AI/ML models that improve patient outcomes while maintaining and individual’s privacy.
*DISCLAIMER: This is a dramatized visual example of what your PII data will look like under Protegrity’s protection.
Industry Example
Synthetic Data
Protecting PCI data involves maintaining robust encryption and/or tokenization, restricting data access, and performing regular security audits. Businesses are often challenged to build innovative customer applications and experiences while remaining compliant with PCI-DSS security controls. Synthetic datasets offer the ability to create new customer experiences and train analytical models while avoiding the risks and audit costs associated with using Cardholder Data.
- Synthetic data are machine generated datasets that measurably maintains the original datasets characteristics using artificial data subjects.
- Machine generated datasets for testing applications and training analytical models accelerates innovation.
- Synthetic datasets can be shared with third-party partners and vendors as a source of new business value.
*DISCLAIMER: This is a dramatized visual example of what your PII data will look like under Protegrity’s protection.
Industry Example
Encryption
Protecting IP is Use Case dependent and often is based on the storage medium. IP stored in databases can be protected through a variety of fine-grained methods like pseudonymization or FPE. When IP is stored as a file or an image, we deploy robust encryption to protect it. The benefits are:
- Encryption is a tried-and-true data security method that offers excellent data protection.
- Using Protegrity’s File Protector, sensitive data policy remains centralized for protecting PII, PHI, PCI, and IP even when stored in files.
*DISCLAIMER: This is a dramatized visual example of what your PII data will look like under Protegrity’s protection.
The Benefits of Protegrity
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Our gateways, integrations, and solutions are regularly updated, allowing Protegrity products to evolve with PCI DSS, GDPR, and other data security compliance regulations. Whether your organization needs to protect data for sharing, analytics, or use, Protegrity has a comprehensive solution.