Operationalizing DLP
Detection Engine
Nightfall for Slack
Nightfall for GitHub
NIGHTFALL FOR GOOGLE DRIVE
Nightfall for Confluence
Nightfall for Jira
Account Management
Resources
Detectors
Nightfall offers core machine learning-powered detectors that you can leverage out of the box. These detectors cover a range of sensitive information types, such as:
- Standard PII
- Health
- Finance
- Network
- Secrets and Credentials
For the complete, up-to-date list of Nightfall detectors, please visit the Detectors page within the Nightfall Dashboard.
Accuracy & testing: On the whole, our detectors are trained via machine learning, which yields higher accuracy than traditional approaches, and works well on unstructured data like files and messages. Our machine learning detectors are geared towards false-positive reduction. It's important to note that when you are testing these detectors, you may explicitly know what you're looking for the detector to flag on, but in real-world situations, these detectors need to explore context and other factors to make determinations. Not to mention, true positives are significantly rarer in practice in proportion to their frequency in a test environment. That being said, accuracy rates you experience in testing may not necessarily reflect what you experience when these detectors are enabled in the wild.
Context Rules: You can further tune a detector by applying context rules, or "hotwords" that can indicate higher likelihoods of a match on an individual detector basis. For example, if you are looking for Social Security Numbers "489-36-8350" but have customer account IDs similar in length and validity, you can also choose to match on context occurring before and/or after indicating higher likelihoods. This could be defined as "SSN: 489-36-8350".
Exclusion Rules: You can further tune a detector by whitelisting certain patterns, regexes, and dictionaries on an individual detector basis. For example, if your corporate domain is "example.com" and you have the Email Address detector enabled, you may want to avoid flagging "[email protected]" email addresses. This could be defined as a regex like "*@example.com" or a dictionary such as ["[email protected]", "[email protected]", etc.].
Detector Glossary
Custom Detectors
If you don’t see what you need in our core detector library, you can create custom detectors or regexes. While Nightfall's pre-built detectors listed above are trained via machine learning, Nightfall also supports RE2 regexes and word lists for any custom detectors that may be of interest to you. Over time, we've aggregated the following regex library, which you're welcome to select from to save you some time. Please note that a regular expression is an established yet limited method that searches for pre-defined patterns, so your mileage may vary. You can test regular expressions here. You can input custom detectors directly in the Nightfall console at app.nightfall.ai by navigating to Detectors → New Detector → Regular expression.
Additional Detectors
We can work with you to apply a range of other detectors. We continue to build a broad library of both machine learning and regex-based detectors spanning different industries, geographies, and compliance regimes, and our data science team continues to innovate upon new detectors on an ongoing basis. For additional beta detectors you can enable in your account, please e-mail us at [email protected].
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