Compliance Use Cases

Organizational compliance is one of the leading drivers that require DLP tooling such as Nightfall. These are the recommended configurations for each compliance framework.

Compliance	Configuration	Considerations
HIPAA Compliance	Use the Protected Health Information (PHI) detector Set Minimum Confidence level to Likely Set alert to trigger on Any Detectors	Depending on the type of healthcare organization, disclosure of personal information may disclose PHI (e.g., a sufficiently uniquely named person going to a health provider like an AIDS clinic would likely disclose the person’s PHI).
PCI Compliance - Text	Use the Credit Card Number Set Minimum Confidence level to Likely Set alert to trigger on Any Detectors	For greater rigor, set on each of your locale’s detection rules alongside the Person Name detector configured to trigger with All Detectors, per:
PCI/PII Compliance - Images	Use the Drivers License Image, Passport Image, US Social Security Image, Credit Card Image detectors Set Minimum Confidence level to Very Likely Set alert to trigger on Any Detectors	These detectors analyze the layout and formatting of content within images, accurately identifying government-issued ID documents from any nation and payment cards from any institution.
ACH Compliance	Use the US Bank Routing and Person Name detectors Set Minimum Confidence level to Likely Set alert to trigger on All Detectors
GLBA Compliance	Use the SWIFT and US Bank Routing detectors Set Minimum Confidence level to Likely Set alert to trigger on Any Detectors
ISO 27001 Compliance for v2022	Enable all Secrets detectors: API key Cryptographic key Database Connection String GCP credentials Password in code Set Minimum Confidence level to Likely Set alert to trigger on Any Detectors

Other detectors that exist are not recommended for use for the above compliance frameworks. For all use cases, Nightfall further recommends:

• Tune and amend Minimum Confidence over time in accordance with your violations and data set

• Scoping should cover all locations where the sensitive data should not be disclosed

• Using Exclusion Rules to reduce false positives and fine-tune alerts

• Reporting false positives for machine learning training to [e-mail]