Welcome to the amazing world of the Nightfall Developer APIs (formerly known as Firewall for AI). Here you can find all the information about Nightfall's APIs, and SDKs, and also usage examples of these APIs and SDKs.

The Nightfall API uses API keys to authenticate requests. You can create and view your API keys in the Nightfall app on the Manage API Keys page.

Your API keys carry many privileges, so be sure to keep them secure. Do not share your secret API keys in publicly accessible areas such as GitHub, client-side code, or anywhere else that would compromise their secrecy. If you believe one of your API Keys has been compromised, you should delete it through the Dashboard.

All API requests must be made over HTTPS.

Calls made over plain HTTP will fail.

API requests without authentication will fail.

Welcome to Nightfall's Firewall for AI Developers Scan and Workflow APIs documentation. This documentation helps developers leverage Nightfall AI's industry-leading detection engine to identify and protect sensitive customer and corporate data anywhere. It prevents unauthorized access and data breaches and allows you to focus on innovation.

Scan APIs

Scan prompts, text, documents, spreadsheets, logs, zips, JSON, images, etc., for PII, PHI, PCI, banking information, API keys, passwords, and network information with the highest accuracy and lightning-fast response times. Redact sensitive findings with customizable formatting.

Workflow APIs

Leverage the full potential of the Nightfall console application through our Workflow APIs. Customize your SIEM workflows and reporting, take actions, update support tickets, alert users, search violations, annotate findings, create reports, and more.

Key Features

• AI-Powered Identification: Utilize advanced AI models to detect and prevent security threats in real-time.

• Comprehensive Sensitive Data Detection: Identify PII, PHI, PCI, banking information, API keys, passwords, and network information across various formats including text, documents, spreadsheets, logs, zips, and images.

• Customizable Redaction: Tailor data protection to your needs with fully customizable redaction for each sensitive entity type.

Customizable and Built-in Machine Learning-based Detectors

You can leverage Nightfall’s machine learning-based detectors or create your own detectors with customized logic to scan third-party apps, internal services, and data silos to identify instances of potentially sensitive types of data such as:

• Personally Identifiable Information (PII) including Social Security Numbers, passport numbers, email addresses, or date of birth

• Protected Health Information (PHI) such as insurance claim numbers or ICD10 codes

• Financial information like credit card numbers or bank routing numbers

A Flexible Data Security Solution

Key features of Nightfall’s detection engine include:

• Defining minimum confidence thresholds and minimum finding counts on detectors to reduce the chance of false positives.

• Specifying and on detectors to fine-tune their accuracy to better suit your use cases.

• Choosing which detectors are triggered for each policy.

Using the API

The Nightfall API consumes arbitrary data as input either as or as and allows you to use any combination of detectors to return a collection of “findings" objects.

The detectors may be or defined as part of the .

The findings display the relevant detector, the likelihood of a match, and the location within the given data where the matched token occurred (not only in terms bytes — there is support for tabular and JSON data as well).

You can take protective action on sensitive text by , substituting, or encrypting it with the API. You may also set up to receive asynchronous notifications when findings are detected.

The Nightfall API is RESTful and uses JSON for its payloads. Our API is designed to have predictable, resource-oriented URLs for each endpoint and uses to indicate any API errors.

You may test out the API through the

Where to Go From Here

The following guide will walk you through getting started and describe the API functionality in more detail. If you want to execute an API call immediately, see our guide to see how to obtain an API Key and make a simple scan request.

After that, you can learn about Nightfall with our Key Concepts section, which will also help you get set up with Nightfall.

If you’re looking for more ideas about best to leverage Nightfall’s functionality, see our guide.

We have created numerous that demonstrate how to implement DLP for a variety of platforms (including OpenAI, LangChang, Amazon, Datadog, and Elasticsearch) and handle various scenarios (such as detecting sensitive data in GenAI prompts or detecting PII on your machine in real-time).

We also have several language-specific to get you up and running in Java, Python, Go, Node.js, and Ruby.

You can also quickly test out Nightfall detectors or your custom Detection Rules in the . Please also consult our Detector to see the variety of built-in detectors that Nightfall offers.

The page allows you to create API keys and manage Detectors and Detection Rules through a straightforward user interface. Log in here to access the Dashboard, or sign up to create a free account.

For frequently asked questions, feedback, and other help, please contact Nightfall support at . We also host on Wednesdays at 12pm PT to help answer questions, talk through any ideas, and chat about data security. We would love to see you there!

Nightfall offers many useful features beyond its detectors, including:

The ability to use and to narrow the scope of matches.

The ability to create in a way that is highly configurable so that sensitive data is appropriately obfuscated.

The ability to create that determine how leaks of sensitive information should be mitigated (i.e. through alerts sent to email or Slack).

Absolutely! In addition to the pre-built detectors, Firewall for AI allows you to create custom detectors tailored to your specific requirements. You can either fine-tune one of our pre-configured detection rules or build your own detector from scratch using our intuitive API. Nightfall supports many traditional detector types such as regular expressions, exact data matching, and word list/dictionaries. Check out our dedicated guide on creating custom detectors for more information.

The Nightfall Developer Playground (playground.nightfall.ai) is a sample app that you may use to test out API functionality before writing any code.

Our playground environment allows you to:

• Test Detectors and Detection Rules. Here are some sample datasets.

• Generate sample data for DLP testing.

• Explore a sample app built on our APIs

Firewall for AI provides a flexible and extensible API that allows you to scan a wide variety of data types, including plain text, structured and unstructured files, and even images. Our API can handle data in various formats such as JSON, XML, CSV, and more. Visit our detector glossary at docs.nightfall.ai/docs/detector-glossary to explore the comprehensive list of supported data types and file formats

Firewall for AI offers a rich set of pre-built detectors that can identify many different types of sensitive data, including personally identifiable information (PII), payment card industry data (PCI), protected health information (PHI), secrets, and credentials. These detectors are powered by advanced machine learning models and can be easily integrated into your application with just a few lines of code. Refer to our detector glossary at docs.nightfall.ai/docs/detector-glossary for a complete list of available detectors.

At Nightfall, data security and privacy are our top priorities. We have implemented stringent security measures to protect your sensitive data at every stage of the scanning process. All data transmitted to our API is encrypted in transit using industry-standard protocols. We adhere to best practices for secure coding, undergo regular security audits, and maintain compliance with relevant security standards. Visit our security and compliance page at nightfall.ai/security for more details on our commitment to data protection.

This section consists of use case tutorials for various scenarios of Firewall for AI. The tutorials explained in this section are as follows.

• LangChain Prompt Sanitization

• Deploy a File Scanner for Sensitive Data in 40 Lines of Code

Getting Help

If you encounter issues not covered in this guide:

• Email Support:

The API expects an API Key to be passed via the Authorization: Bearer <key> HTTP header.

To create and manage API keys:

1. Log in to Nightfall.

2. Click Overview

The Nightfall API supports the ability to send asynchronous notifications when findings are detected as part of a scan request.

The supported destinations for these notifications include external platforms, such as Slack, email, or url to a SIEM log collector as well as to a webhook server.

Nightfall issues notifications under the following scenarios:

• to notify a client about the results of a . File scans themselves are always performed asynchronously because of complexity relating to text extraction and data volume.

The scan endpoint allows you to apply Policies and Detection Rules to a list of text strings provided as a payload.

You may use or

Text scanning supports the use of,, and as well as other .

For scanning files, see .

Note that you must generate anto send requests to the Nightfall API.

Nightfall’s file scan API allows a user to upload a file in chunks, then to scan it with Detection Rules once the upload is complete.

The scan will then be processed asynchronously before sending the results to the webhook URL that is provided along with your Detection Rules.

The following sequence diagram illustrates the full process for scanning a binary file with Nightfall.

For a detailed walkthrough of the API calls necessary to upload and scan a file and full script that shows the entire process, see

Protected health information (PHI), also referred to as personal health information, describes a patient's medical history — including ailments, various treatments, and outcomes. PHI may include:

• demographic information

• test and laboratory results

Firewall for AI DLP APIs enables developers to write custom code to sanitize data anywhere–RAG data sets, analytics data stores, data pipelines, and unsupported SaaS applications.

In order to accept requests from Nightfall, a Webhook server must use a signing key to verify requests.

To access or generate your Webhook signing key, start by logging in to the Nightfall .

Select the Developer Platform > Manage API Keys using the navigation bar on the left side of the page. You will see the Webhook signing section:

Unlike the API Key, it is possible to reveal the signature via the "eye" icon furtherest to the left of the three icons displayed.

You may copy the current value to your clipboard with the "copy" icon in the center of the three icons displayed.

You may also regenerate the key with the circular arrow icon furthest to the right.

Don't hesitate to get in touch with us directly via email at or through the c on our website.

We host on Wednesdays at 12 pm PT to help answer questions, talk through any ideas, and chat about data security. We would love to see you there!

Leverage our software development kits (SDKs) to enable easier, faster, and more stable engagement with the Nightfall APIs. Nightfall has a growing library of language specific SDKs including for:

In two ways:

• Nightfall’s out of the box detectors can be modified with context rules and exclusion rules.

• Nightfall also supports inputting custom regular expressions or word lists (i.e. dictionaries) as detectors in the RE2 standard as documented here.

Yes, you can test out the detection engine, including 70+ pre-built detectors without writing any code or having to sign up in our Playground.

Firewall for AI is a powerful API that acts as a middleware layer or client wrapper to protect your AI models from consuming sensitive data. By integrating Firewall for AI into your application via API calls, you can proactively prevent data leaks and maintain compliance without disrupting your existing workflows or model updates.

Policies allow customers to create templates for their most common workflows by unifying a set of Detection Rules with the actions to be taken when those rules are triggered, including:

• automated actions such as redaction of findings

• alerting through webhooks

Once defined, a Policy may be used in requests to the Nightfall API, such as calls to scan file uploads, though automated redactions are not available for uploaded files at this time.

To create a policy:

1. Log in to Nightfall.

2. Click Policies under the Configuration section.

3. Click + New Policy.

1. Click Next.

2. (Optional) Enable the Redact Message toggle switch. This is an automated action that is triggered when sensitive data is found. The action automatically redacts sensitive data.

3. Click Next.

Configuring Webhook Alerts

To configure Webhook as an alert channel:

1. Enable the Webhook Alert notification channel.

2. In the Configure Webhook URL field, enter the URL of the Webhook to which you wish to send notifications.

3. (Optional) Click Add Headers to add header key value pairs.

Similarly, you can also use HTTP alerts channel.

You can use the posture management APIs to search posture events, fetch posture events and also event details. Additionally, you can also view details of the user (actor) whose actions triggered an event, and details of the asset that triggered an event.

While using Nightfall's Scan API, you may encounter some of the common errors outlined below. Try following the provided troubleshooting steps.

If problems persist, please contact Nightfall Support for further assistance.

HTTP Error Codes

The following error codes are returned as part of a standard HTTP response.

Schedule a Demo

You can schedule a demo or a meeting with our sales/solutions engineering team directly via Calendly here. If you don't see a suitable time, please email us at [email protected].

Email Us

For support inquiries, please email us at .

For sales inquiries, please email us at .

Using regex to identify long patterns in images can be challenging because OCR systems. In such cases, even Nightfall may not achieve 100% character-by-character accuracy. To improve results, you must introduce higher levels of flexibility into your regex patterns to accommodate common OCR inconsistencies. Here are some typical OCR challenges to keep in mind:

• Spell-check noise: Spell-checking tools can add artifacts like red underlines, which may interfere with text recognition.

• Character ambiguity:

  • The digit 0 may be misinterpreted as the letter O (or vice versa), depending on the font.

  • The character l (lowercase L) may be read as the digit 1.

  • The letter B may appear as the digit 8.

• Underscore handling: An underscore (_) is sometimes interpreted as a space, particularly when spell-check artifacts are present.

• Line wrapping: OCR may introduce unexpected newlines when text wraps across multiple lines.

• Periods and punctuation: Spell-check artifacts or font issues may result in extraneous periods (.) or other punctuation being added to the output. En dash (–) and hyphens (-) may be interchanged.

For reference, OCR tools like Tesseract typically achieve 85-98% character accuracy for similar input, and our system operates within a similar range. Given this, tuning your regex to be more forgiving (e.g., allowing for optional characters or slight variations) can significantly improve detection rates.

Example Regex (original and loosened)

original: ATATT3xFfGF0[A-Za-z0-9=_\-]*[=A-Za-z0-9]{9}

loosened: ATATT[A-Za-z0-9_\-– @.\n=]*[A-Za-z0-9_\- @.\n]{7,11}

• shortened the literal match prefix

• excluded the the literal zero (0) from the prefix

• added period (.) and newline () chars

The native SaaS app APIs can be utilized by customers using Nightfall’s SaaS apps, supported natively, to fetch violations, search violations by app meta-data attributes, and fetch findings within violations. These DLP APIs do not provide access to violations for apps scanned via the developer platform. These APIs require you to create an API key as outlined in the Getting Started with the Developer Platform section. However, to use these APIs, you need not create any detectors, detection rules, and policies in the developer platform.

If you are using Nightfall SaaS apps, you can use APIs to fetch violations, search through the violations, and fetch specific findings within the Violations. To scan data in any custom apps or cloud infrastructure services like AWS S3, you must use the APIs in the DLP APIs - Firewall for AI Platform section.

Nightfall provides you the flexibility to easily integrate into applications using programming languages. The supported languages are as follows.

• Python

• Ruby

APIs to monitor and manager integrations

You can use the exfiltration APIs to search exfiltration events, fetch exfiltration events and also event details. Additionally, you can also view details of the user (actor) whose actions triggered an event, and details of the asset that triggered an event.

This section describes the terms you will need to know when using the API.

Detectors

Detectors provide the logic to find potentially sensitive pieces of data.

When this logic detects such data, the Detector is considered "triggered."

Nightfall's has numerous pre-built Detectors that are trained via machine learning. Detectors may also be defined with regular expressions or dictionaries. Their accuracy may be further refined with exclusion rules and context rules. Whether a Detector is triggered may be controlled by a minimum confidence threshold per Detector and minimum number of findings per Detector as set on a Detection Rule.

These examples demonstrate the flexibility of natural language queries. You don't need to memorize syntax - just describe what you need.

Basic Searches

• "Show me today's violations"

Here are real-world scenarios where the Nightfall MCP server delivers immediate value:

Security Investigations

Example: "Show me all active high-risk violations from GitHub in the last week"

The AI automatically uses search_violations with appropriate filters and returns results in seconds. You can then ask follow-up questions like "Which repository has the most violations?" or "Show me the sensitive data found in violation abc-123" without manually constructing queries.

Before you use the scan endpoint, there are a number of actions to do within the Nightfall dashboard to get your environment set up properly.

• See to see how to create the necessary Authentication token for making API calls.

• See for how to define your own custom logic for detecting sensitive data

The following sample datasets can be used to test Nightfall's advanced AI-based detection capabilities.

This data has been fully de-identified and can be used to test any data loss prevention (DLP) platform.

You can start scanning for sensitive data in just a few minutes. Our developer-friendly API and comprehensive documentation make it easy to integrate Firewall for AI into your application. Follow our Quickstart guide at for step-by-step instructions on setting up the API, configuring detectors, and making your first API call.

We offer a free tier that allows you to sign up and start using Firewall for AI with zero upfront costs or commitments. This tier provides a generous data scanning capacity and access to all the core features.

We offer enterprise pricing plans for advanced requirements such as higher data volumes, custom rate limits, and dedicated support.

Contact our team at [email protected] or via the contact form on our website to discuss your specific needs and get a tailored pricing quote.

The Nightfall MCP (Model Context Protocol) server enables AI assistants like Claude, ChatGPT, and Cursor to interact directly with your Nightfall security data. Instead of navigating dashboards or running API queries manually, you can use natural language to investigate violations, search for exfiltration events, review security posture, and take remediation actions.

Key Benefits

• Natural Language Interface: Ask questions in plain English instead of learning complex query syntax

• Faster Investigations: Get instant answers to security questions without switching between tools

• Workflow Integration: Access Nightfall data directly in your development and collaboration environments

• Automated Analysis: Let AI help you identify patterns, summarize findings, and recommend actions

An Exclusion Rule allows you to refine a Detector to make sure false positives are not surfaced by Nightfall.

For instance you may want to detect whether credit card numbers are being shared inappropriately in your organization. However, there may be cases where members of your QA are sharing test credit card numbers, which should not be considered a violation and should be ignored by Nightfall.

In the following example, we define a Detector with a regular expression to match credit cards.

We then add an exclusion for some known test credit cards.

curl --location --request POST 'https://api.nightfall.ai/v3/scan' \
--header 'Accept: application/json' \
--header 'Authorization: Bearer NF-rEpLaCeM3w1ThYoUrNiGhTfAlLKeY123' \
--header 'Content-Type: application/json' \
--data-raw '{
    "policy": {
        "detectionRules": [
            {
                "detectors": [
                    {
                        "regex": {
                            "pattern": "(?:(4[0-9]{12}(?:[0-9]{3})?)|(5[1-5][0-9]{14})|(6(?:011|5[0-9]{2})[0-9]{12})|(3[47][0-9]{13})|(3(?:0[0-5]|[68][0-9])[0-9]{11})|((?:2131|1800|35[0-9]{3})[0-9]{11}))",
                            "isCaseSensitive": false
                        },
                        "exclusionRules": [
                            {
                                "wordList": {
                                    "values": [
                                        "4111111111111111",
                                        "5105105105105100"
                                    ]
                                },
                                "exclusionType": "WORD_LIST",
                                "matchType": "FULL"
                            }
                        ],
                        "minNumFindings": 1,
                        "minConfidence": "POSSIBLE",
                        "displayName": "Credit Card Reg Ex",
                        "detectorType": "REGEX"
                    }
                ],
                "name": "Credit Card Detection Rule",
                "logicalOp": "ALL"
            }
        ]
    },
    "payload": [
        "5105105105105100",
        "4111111111111111",
        "4012888888881881"
    ]
}'

As the resulting payload shows, only the 3rd provided Credit Card number matches because the first two items in the payload are included in our ExclusionRules word list.

Say you have a number of files containing customer or patient data and you are not sure which of them are ok to share in a less secure manner. By leveraging Nightfall’s API you can easily verify whether a file contains sensitive PII, PHI, or PCI.

To make a request to the Nightfall API you will need:

• A Nightfall API key

• A list of data types you wish to scan for

• Data to scan. Note that the API interprets data as plaintext, so you may pass it in any structured or unstructured format.

You can read more about or about our in the linked reference guides.

To run the following API call, we will be using Python's standard json, os, and requests libraries.

First we define the endpoint we want to reach with our API call.

Next we define the headers of our API request. In this example, we have our API key set via an environment variable called "NIGHTFALL_API_KEY". Your API key should never be hard-coded directly into your script.

Next we define the detectors with which we wish to scan our data. The detectors must be formatted as a list of key-value pairs of format {‘name’:’DETECTOR_NAME’}.

Next, we build the request body, which contains the detectors from above, as well as the raw data that you wish to scan. In this example, we will read it from a file called sample_data.csv.

Here we assume that the file is under the 500 KB payload limit of the Scan API. If your file is larger than the limit, consider breaking it down into smaller pieces across multiple API requests.

Now we are ready to call the Nightfall API to check if there is any sensitive data in our file. If there are no sensitive findings in our file, the response will be "[[]]".

Leaked secrets, such as credentials needed to authenticate and authorize a cloud provider’s API request, expose company software, services, infrastructure, and data to hackers.

Nightfall has developed technology to detect secrets and label findings to speed SecOPs workflows from being clogged and eliminate false positive alerts.

Overall Coverage

Nightfall uses machine learning models trained on a large (millions of lines of code) diverse dataset (including all programming languages and application types) to ensure best-in-class secret detection accuracy and coverage.

Explicit Labeling and Endpoint Validation for Popular Services

For a growing set of the most popular services, Nightfall will:

• label detected secrets by vendor and service type (returned the kind field of the response)

• label detected secrets as active risks by validating supported credential types with their associated service endpoints (returned as the status of the service)

Our current solution supports the following vendors covering a diverse set of use cases, including cloud storage/infrastructure, communication, social networks, software development, banking, observability, and payment processing.

This list is not static and will continue to grow as we add support for detecting API keys from additional services. If you want to detect API keys from a service not listed below, please .

Key Detection Example

Below is an example of how an AWS Key would be shown in a finding.

The following values are returned for the status field:

• ACTIVE

• EXPIRED

• UNVERIFIED

This value will be based on what information is returned by the corresponding service when attempting the validate the key. If no data is returned fro the service, it will be considered UNVERIFIED.

To use this functionality, you use our existing built-in API_KEY detector to scan a data source such as . Below is an example using a detection rule defined in line for a text scan.

Nightfall supports Detectors that will scan for file names, file types, and file finger prints.

Detecting File Names

In addition to scanning the content of files, you may configure the Detectors to scan file names as well.

This is done through the “scope” attribute of a Detector.

The scope attribute allows you to scan either within file contents, the file name, or both the file contents and file name.

File extensions can be scanned for by creating a Regular Expression type custom Detector with a scope to scan only file names ("File") or both the content and file name ("ContentAndFile"), as shown in the example request below.

In addition to scanning based on file name, you may also use a which allows you to scan for files based on their mime-type.

Note that confidence sensitivity does not apply to file names. Sensitive findings will always be reported on.

Detecting File Types

Nightfall’s File Type detection allows you to implement compliance policies that detect and alert you when particular file types that are not allowed in a given location are discovered.

This functionality is implemented by creating a specific Detector called a “File Type Detector”

To create a File Type Detector, select “Detectors” from the left hand navigation and click the button labeled “+New Detector” in the upper right hand corner. From there a drop down list of Detector types will be displayed which will include the “File Type” Detector type.

You will then select one or more file types for which to scan by selecting from a list of

You can either scroll through the list of mime-types in the select box or you may type in a portion of the mime-type and the contents of the select box will be filtered to match your input.

Nightfall supports detection for a wide variety of mime-types. See the Internet Assigned Numbers Authority’s (IANA) website for a definitive list of . Note however that Nightfall does not support the detection of audio and video related mime-types.

Detection of file types is done based on the file contents, not its extension. However, you can create by setting the scope attribute.

File Type Detectors vary from other Nightfall Detectors in that the attributes of scope and confidence are not relevant to File Type Detectors

Once you have added all the mime-types you wish to scan for, save your new Detector. You may then add your new Detector to and.

Detecting Files Through Fingerprinting

Nightfall allows you to discover the location of specific files that you have deemed sensitive and want to avoid sharing.

This discovery is done through document fingerprinting. Fingerprinting is the process of algorithmically creating a unique identifier for a file by mapping the data of the document to a signature that can be recalled quickly. This allows the file to be identified in a manner akin to how human fingerprints uniquely identify individual people.

This functionality is achieved in Nightfall by creating a specific Detector type called a File Fingerprint Detector.

The Fingerprint Detector allows you to create a fingerprint for one more files (a sort “handful” of fingerprints, if you would).

To create a Fingerprint Detector, select “Detectors” from the left hand navigation and click the button labeled “+New Detector” in the upper right hand corner. From there a drop down list of Detector types will be displayed which will include the “Fingerprint” Detector type.

When you create a File Fingerprint Detector you can upload up to 50 files that need to be fingerprinted. The file size limit is 25MB.

Once the fingerprint is generated, the actual content of the file is discarded so no sensitive content is stored on Nightfall’s system.

These Detectors may only be created through the console.

You may then treat the Fingerprint detector like any other and incorporate it into a using its unique Detector identifier.

You may incorporate these Detectors into that will alert you whenever files that match the fingerprint are detected.

To prevent misuse and ensure the stability of our platform, we enforce a rate limit on an API Key and endpoint basis, similar to the way many other APIs enforce rate limits.

When operating under our Free plan, accounts and their corresponding API Keys have a rate limit of 5 requests per second on average, with support for bursts of 15 requests per second. If you upgrade to a paid plan – the Enterprise plan – this rate increases to a limit of 10 requests per second on average and bursts of 50 requests per second.

The Nightfall API follows standard practices and conventions to signal when these rate limits have been exceeded.

Successful requests return a header X-Rate-Limit-Remaining with the integer number of requests remaining before errors will be returned to the client.

When your application exceeds the rate limit for a given API endpoint, the Nightfall API will return an HTTP "Too Many Requests.” If your use case requires increased rate limiting, please reach out to

Additionally, these unsuccessful requests return the number of seconds to wait before retrying the request in a Header.

Request Rate Limiting

Your Request Rate Limiting throttles how frequently you can make requests to the API. You can monitor your rate limit usage via the `X-Rate-Limit-Remaining` header, which tells you how many remaining requests you can make within the next second before being throttled.

Quotas

Your Quota limits how many requests you can make within a given period. Your current remaining quota and the end of your current quota period are denoted by the following response headers.

For the free plan, we allow 5 requests per second and 10000 requests in a day.

The Firewall for AI Platform differs from other solutions like Google DLP and Amazon Macie, as well as open source solutions like truffleHog, on a number of dimensions summarised below.

Accuracy

• While solutions like Google DLP have a broad set of detectors, many of them are rules or regex based, which means many of the detectors are not usable in practice. Likewise, detection has been found to be inconsistent in some cases, perhaps due to internal A/B testing.

• Because of the limitations of regex-based rules, instead of leveraging machine learning based detectors, OSS detection solutions tend to have a much higher rate of false positives compared to Nightfall.

• Detector configurability and ability to provide metrics at the token level makes Nightfall accurate and actionable to engineering & security teams.

Convenience

• Want to leave the last 4 digits of a credit card number visible, securely encrypt emails, and completely remove SSNs from your data? The Nightfall platform allows you to redact/replace, substitute, and/or encrypt sensitive data findings in the same API call as your inspection request.

Ease of use

• All inspection configuration in Google DLP is done as code, which makes it challenging to easily update, visualize, and modify detection rules and configuration. Nightfall allows for configuration as code, as well as the Nightfall for creating and updating detection rules, which makes it easier to collaborate.

• OSS secret detection tools tend to rely heavily on manual creation of regex-based detection compared to an ability to programmatically scan text and file inputs using 150+ detectors in Nightfall – e.g. truffleHog only enables you to scan for secrets like passwords and private keys whereas Nightfall scans for not only secrets and credentials, but also allows you to use our vast detector library to scan for PII, PCI, and PHI.

File parsing

• To parse files with Google DLP and Macie, each requires that they be in their respective cloud storage (Google Cloud Storage or S3, respectively). With the Nightfall Developer Platform, we take care of storage requirements for you. Uploaded assets are stored encrypted at rest with minimal access permissions, and are automatically deleted after 24 hours.

• Amazon’s file parsers are limited to around 20 file types. Most notably, Macie does not support images. Text extraction via machine-learning based OCR for images is a core component of Nightfall’s file scanning endpoint.

• Open source secrets detection solutions are limited in their detection capabilities. Namely, these projects do not support scanning binary files. Nightfall supports binary files and the ability to scan diff files.

Platform agnostic

• Each cloud provider's DLP products are geared towards protecting their own cloud services. For example, Google DLP’s native integrations are limited to Google Cloud offerings such as BigQuery. Similarly, Macie is primarily designed around scanning AWS S3 buckets. The interface is largely geared towards exploring sensitive data across S3 buckets. To scan content outside of S3, Amazon’s recommendation is to move or replicate the data into S3 to scan, which is impractical.

• OSS solutions are primarily designed around git repositories.

• Nightfall has native integrations with many cloud applications like Slack, Atlassian, GitHub, Google Drive, as well a broad set of tutorials and open source code so you can build integrations into any data silo with ease. For example, this includes services like Snowflake, Airtable, and more.

Support and documentation

• Google DLP and Macie are loosely supported products and with many cloud offerings, support is hard to come by. Nightfall is laser-focused on best-of-breed content inspection and we are ready to address your questions and use cases.

• Nightfall also has extensive documentation including SDKs for multiple languages including Python, Java, NodeJS, and Go - with more under consistent development.

Cost and scale

• Costs can balloon quickly with commercial services. They also have rate limits that don’t suit high data volumes.

• Open source solutions have high hidden costs in the form of TCO, maintenance, and opportunity cost.

• Nightfall offers a custom enterprise tier that can help you scale pricing based on your anticipated usage as well as custom rate limits.

Nightfall has the ability to send alerts when a violation is detected.

Policies for alerting may be configured through the Nightfall app user interface or they may be set up . Policies that are configured under Developer Platform > Overview > Policies may be used in the API by referencing their Policy UUID.

The way that an alert notification presents itself depends on the platform in question.

For example, notifications sent to Slack will appear as formatted messages sent by the Nightfall Alerts Bot. Other destinations such as email, SIEM url, and webhooks, will present the information as JSON objects.

In the case of webhooks, detailed information about the finding will be sent. For other destinations, sensitive information is redacted.

Technical Specifications

You can use the surrounding context of a match to help determine how likely it is that your potential match should actually be considered as a match by adjusting its confidence rating.

You can also tell the Detection Rule to return a portion of the surrounding context for manual review.

In the following example, in addition to providing a regular expression to match Social Security Numbers, we also look to see if someone has written the text “SSN” before and after the match, which might be a label indicating it is indeed a social security number. In which case, we change our confidence score to “VERY_LIKELY.” We then provide two possible matches in our payload, the first of which contains the string “SSN”.

In the results, you can see the confidence for the first finding in the payload has been set to VERY_LIKELY while the second item is only LIKELY.

In this example, we'll walk through making a request to the scan endpoint.

The endpoint inspects the data you provide via the request body and reports any detected occurrences of the sensitive data types you are searching for.

Please refer to the API reference of the scan endpoint for more detailed information on the request and response schemas.

In this sample request, we provide two main fields:

1. a policy and its detection rules that we want to use when scanning the text payload

2. a list of text strings to scan

In the example below we will use a Detection Rule that has been configured in the by supplying its UUID.

The aggregate length of all strings in payload list must not exceed 500 KB, and the number of items in the payload may not exceed 50,000.

Alternatively you may define your policy in code by using a built in Nightfall detector from the as follows:

See for more information about how policies and detection rules may be defined through code.

Executing the curl request will yield a response as follows.

The API call returns a list, where the item at each index is a sublist of matches for the provided detector types.

The indices of the response list correspond directly to the indices of the list provided in the request payload.

In this example, the first item in the response list contains a finding because one credit card number was detected in the first string we provided. The second item in the response list is an empty list because there is no sensitive data in the second input string we provided. The third item in the returned list contains multiple findings as a result of multiple Detectors within the Detection Rule being triggered.

You can read further about the fields in the response object in the .

To prevent misuse and ensure the stability of our platform, we enforce a rate limit on an API Key and endpoint basis, similar to the way many other APIs enforce rate limits.

When operating under our Free plan, accounts and their corresponding API Keys have a rate limit of 5 requests per second on average, with support for bursts of 15 requests per second. If you upgrade to a paid plan – the Enterprise plan – this rate increases to a limit of 10 requests per second on average and bursts of 50 requests per second.

The Nightfall API follows standard practices and conventions to signal when these rate limits have been exceeded.

Successful requests return a header X-Rate-Limit-Remaining with the integer number of requests remaining before errors will be returned to the client.

When your application exceeds the rate limit for a given API endpoint, the Nightfall API will return an HTTP "Too Many Requests.” If your use case requires increased rate limiting, please reach out to

Additionally, these unsuccessful requests return the number of seconds to wait before retrying the request in a Header.

Request Rate Limiting

Your Request Rate Limiting throttles how frequently you can make requests to the API. You can monitor your rate limit usage via the `X-Rate-Limit-Remaining` header, which tells you how many remaining requests you can make within the next second before being throttled.

Quotas

Your Quota limits how many bytes of data you're permitted to scan within a given period. Your current remaining quota and the end of your current quota period are denoted by the following response headers.

This guide describes how to use Nightfall with the Python programming language.

The example below will demonstrate how to use Nightfall’s text scanning functionality to verify whether a string contains sensitive PII using the Nightfall Python SDK.

To request the Nightfall API you will need:

• A Nightfall API key

This guide describes how to use Nightfall with the Java programming language.

The example below will demonstrate how to use Nightfall’s text scanning functionality to verify whether a string contains sensitive PII using the Nightfall Java SDK.

In this tutorial, we will be downloading, setting up, and using the Java SDK provided by Nightfall.

To make a request to the Nightfall API you will need:

• A Nightfall API key

Spreadsheets and Tabular Data

File scans of Microsoft Office, Apache parquet, csv, and tab separated files will provide additional properties to locate findings within the document beyond the standard byteRange, codepointRange, and lineRange properties.

Findings will contain a columnRange

Learn how to set up a server to handle results of file scans and alerts sent based on policy alert configurations.

Webhook Challenges

Nightfall will send a POST request with a JSON payload with a single field challenge containing randomly-generated bytes when it sends a message to a user-provided webhook address. This is to ensure that the caller owns the server.

In order to authenticate your webhook server to Nightfall, you must reply with (1) a 200 HTTP Status Code, and (2) a plaintext request body containing only the value of the challenge key.

If Nightfall receives the expected value back, then the file scan operation will proceed; otherwise it will be aborted.

When a server responds successfully to a challenge request, the validity of that URL will be cached for up to 24 hours, after which it will need to be validated again.

If the webhook cannot be reached, you will receive an error with the code "40012" and the description "Webhook URL validation failed" when you initiate the scan.

If the webhook challenge fails, you will receive an error with the code "42201" and the description "Webhook returned incorrect challenge response" when you initiate the scan.

Webhook Signature Verification

When a customer signs up for the developer platform, Nightfall automatically generates a unique for them.

This secret is used to sign requests to the customer's configured webhook URL.

If you has any concerns that their signing secret may have leaked, you can request rotation at any time by reaching out to Nightfall Customer Success.

For security purposes, the webhook includes a signature header containing an HMAC-SHA256 digital signature that customers may use to authenticate the client.

In order to authenticate requests to the webhook URL, customers may use the following algorithm:

1. Check for the presence of the headers X-Nightfall-Signature and X-Nightfall-Timestamp. If these headers are not both present, discard the request.

2. Read the entire request body into a string body.

3. Verify that the value in the

The snippet below shows how you might implement this authentication validation in Python:

Example Webhook Server

An example implementation of a simple webhook server is below.

You can test your webhook with a tool such as which allows you expose a web server running on your local machine to the internet.

In the above example, the webhook server is running on port 8075. To route ngrok requests to this server, once you run the python script (having installed the necessary dependencies such getenv and Flask), you would run ngrok as follow:

./ngrok http 8075

See the section on for details about the json payloads for the different messages sent to webhook servers.

The Nightfall MCP server provides 16 specialized tools organized into four categories:

1. Detection & Response Incidents (6 tools)

Work with Data Loss Prevention policy violations detected across your integrations.

The following workflows represent best practices for common security tasks. These step-by-step patterns guide you through effective multi-tool investigations.

Workflow 1: Incident Investigation

Purpose: Conduct an end-to-end security incident investigation

Steps:

1. Identify the scope: Search for related events using search_violations, search_exfiltration_events, or search_posture_events with your search criteria

2. Gather details: For each relevant result, use get_violation, get_exfiltration_event, or get_posture_event to get full context including affected assets, actors, and risk levels

3. Review findings: For violations, use get_violation_findings to see exactly what sensitive data was detected

4. Check activity timeline: Use the appropriate activity tools to understand what remediation has already been attempted

5. Assess user behavior: If an actor is identified, use get_actor_activity to check their recent activity across all event types

6. Summarize and recommend: Provide a structured incident summary with scope, affected assets, actors involved, risk assessment, and specific remediation actions

Example Conversation:

• "Investigate the incident involving repository finance-api"

• "Show me the findings for violation abc-123"

• "What has user [email protected] been doing recently?"

Workflow 2: Violation Triage

Purpose: Prioritize and triage active DLP violations for remediation

Steps:

1. Fetch active violations: Use search_violations with query state:ACTIVE sorted by RISK_DESC to get highest-risk violations first

2. Categorize by severity: Group violations by risk_label (CRITICAL, HIGH, MEDIUM, LOW) and integration

Example Conversation:

• "Triage all active violations, prioritize by risk"

• "Show me details for the top 5 high-risk violations"

• "What are the findings for these violations?"

Workflow 3: Compliance Reporting

Purpose: Generate security compliance summary across time periods

Steps:

1. Gather violation statistics: Use list_violations with createdAfter timestamp for your reporting period

2. Gather exfiltration statistics: Use list_exfiltration_events with the same time range

Example Conversation:

• "Generate a compliance report for the last 90 days"

• "Break down violations by integration and severity"

• "Show me the top 10 incidents with details"