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.
Last updated
Organizational compliance is one of the leading drivers that require DLP tooling such as Nightfall. These are the recommended configurations for each compliance framework.
Last updated
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 support@nightfall.ai
