Healthcare Cybersecurity: 8 Data Protection Best Practices

This blog was originally published by BigID here.

Written by Kimberly Steele, BigID.

Healthcare organizations need to look beyond compliance to reduce risk, implement robust data management, and achieve a data protection program that balances safeguarding patient data with providing the highest quality of patient care.

Healthcare Data Security Challenges

Patient data — or protected health information (PHI) under HIPAA (the Health Insurance Portability and Accountability Act) — is not only some of the most sensitive data out there, but it’s also some of the most targeted by malicious attackers.

Organizations need to mitigate risk across their entire organization, properly protect patient information, and unlock value from their data — while complying with a soaring volume of complex and overlapping regulatory requirements, like HIPAA, HITECH, CCPA, GDPR, U.S. state laws, and many more.

Data Security Best Practices

Using automated data intelligence platforms, deep machine learning, and extensible app frameworks, healthcare companies can establish, operationalize, and enforce best practices to secure and manage their data.

1. Discover All Your Data — Patient Data, Dark Data, Regulated Data, and More

A deep discovery foundation is the first step that all healthcare organizations need to take to set up robust programs for privacy, security, and governance across the enterprise. You can’t protect what you don’t know you have, so discover all of your data — across all types, in any language, at petabyte scale, in the data center or the cloud.

2. Leverage Next-Gen Data Classification

Automatically classify PHI beyond pattern matching and regular expressions (RegEx). Take an ML-based approach to automatically classify and tag all sensitive, regulated, and high-risk data — by regulation, document type, policy, attributes, person, and more.

3. Define Policies to Retain or Discard Data

Apply internal and external policies for data retention rules and regulations. Automate workflows to act on data aging, tag what data to keep, define how long to keep it, and mark over-retained data for deletion.

4. Protect Critical Data

Proactively identify and safeguard patient and critical data; delete redundant, obsolete, or trivial (ROT) data to minimize risk; and identify data with legal holds to comply with regulatory requirements.

5. Remediate High-Risk Data and Manage Remediation Workflows

Remediate sensitive, critical, and regulated data – and leverage remediation workflows to delegate decisions to the right people. Review findings and violations across all your data sources and across structured and unstructured data. Prioritize and assign findings to the individuals most qualified to make decisions on that data, and take actions to remediate high-risk data.

6. Monitor File Access

Get high-level permission analysis around targeted datasets based on category and type, and identify users with access to large, sensitive datasets for further investigation. Identify and remediate overexposed data to reduce risk and protect personal information across your enterprise data stores.

7. Simplify Incident Response

Accurately determine impacted users following a data breach and operationalize your incident response plan. Understand whose data was impacted by a data incident — loss, theft, or misuse — and identify which personal and critical data sets have been affected.

8. Assess and Score Risk

Proactively reduce risk on your most sensitive data. Take a risk-centric view of personal data to be proactive in reducing risk. Score risk based on a variety of data parameters like data type, location, and residency.