The Essential DLP Checklist for Digital Health and Life Sciences

Security leaders in the life sciences and health technology fields know how important it is to safeguard sensitive data like protected health information (PHI), personally identifiable information (PII), and confidential research data. They also know what’s at stake with a security breach or data exfiltration event. But what’s not always clear is how to find the right solution to keep all that data safe. 

Selecting the right DLP solution can be challenging, with many options that offer different features and subsequent strengths and weaknesses. Organizations should carefully evaluate their specific needs and requirements before making a decision. 

To simplify the decision-making process, we’ve created this comprehensive checklist highlighting key considerations to keep your data secure without compromising productivity. From comprehensive coverage to seamless end user experiences, this guide will ensure your organization remains protected while operating efficiently.

1. Comprehensive Coverage

Ensure your DLP platform offers end-to-end protection across all channels:

• SaaS and AI applications: Protect sensitive data across productivity and collaboration apps like Slack, Google Drive, GitHub, Salesforce, Atlassian, and Zendesk, with coverage across managed and unmanaged devices.
• Endpoints: Track data movement and prevent exfiltration on endpoint devices.
• Gen AI: Catch and block sensitive data before it’s shared into Gen AI chatbots like ChatGPT—and Google Gemini and Microsoft Copilot coming soon from Nightfall AI.
• Browsers: Monitor data uploads and downloads within browser activity and prevent secrets from leaving your environments.

2. Ease of Use

A DLP solution should empower your security team to act swiftly and effectively. Prioritize these features:

• Unified console: Manage violations, detection rules, and remediate actions from a single dashboard.
• Real-time alerts: Receive notifications via Slack channels, email, SIEM, Jira tickets, or a violations API.
• Full visibility: Access detailed context around incidents via data lineage for rapid and informed decision-making.
• Automated remediation: Take immediate action with automated workflows to block, redact, delete, quarantine, notify users, or adjust file permissions.
• Efficient triage: Streamline responses to incidents with built-in violation management workflows.

‍3. Intelligent Detection

Accurate detection minimizes false positives and reduces the burden on security teams. Seek out these aspects:

ML-powered detection: Models trained on millions of data samples enhance data detection precision and recall, compared to solutions relying on regex, heuristics, rules, or validators alone.

• HIPAA compliant PHI detection: Specialized algorithms should be in place to identify sensitive patient information and medical data.
• Adaptive detection engine: Detection models should be continuously trained through human-annotated findings to create more accurate results and reduce false positives.
• Customized detectors: Security teams should have the ability to create custom regexes or dictionaries to identify proprietary information and specialized use cases.
• Proper file type support: Nightfall AI supports detection for a broad range of file types to help security teams track and manage sensitive data across multiple environments.

‍4. Relevant Use Cases

A strong DLP solution should support common and complex threats alike:

Prevent data exfiltration: Block uploads to unauthorized domains or personal cloud storage.

• Secure collaboration: Detect unauthorized user access and automatically flag changes to file permissions.
• Enable safe data sharing: Encrypt sensitive information in all environments to allow for secure transmission throughout the organization.
• Monitor email communications: Track and block outbound emails containing sensitive data like PHI, PII, and API keys.

‍5. End-User Experience

Maintain a positive user experience while ensuring compliance with:

Self-remediation: Allow users to resolve issues, report false positives, or communicate their actions and intent with the security team.

• Real-time notifications: Provide clear guidance to end users on how to handle sensitive data incidents.
• Ongoing education for end users: Implement customized notifications and security tips to ensure best practices.
• Minimal disruption: Empower users to remain productive while maintaining strong data protection.
• Invisible deployment: Opt for API integrations, lightweight agents, and seamless email scanning.

‍DLP is Not One Size Fits All

An effective DLP solution is the gatekeeper that prevents unauthorized access, use, or exfiltration of sensitive data. It should monitor and control data movement and detect and block potential data breaches in real time.

We hope this checklist helps your security team make informed decisions on which DLP solution fits your specific needs. First principles of data visibility, full control over all systems, and confidence that data is safe at all times will be the guiding light towards the right choice.

Talk to us about your data protection needs, or schedule a demo to see how Nightfall’s AI-native DLP could be the solution you’re looking for.