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.

• Flexible Detectors: Leverage Nightfall’s comprehensive list of machine learning-based detectors, customize them, or create your own with specialized logic.

• High Accuracy and Performance: Achieve precision and recall rates of 95% or higher, handle over 1K requests per second, and experience latency of less than 100 ms.

• Seamless Integration: Easily integrate with your existing AI development and data engineering tools for smooth and efficient operation.

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

• Secrets such as API and cryptographic Keys, database connection strings, passwords, etc.

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.

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

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’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

In order to utilize the File Scanning API you need the following:

• An active API Key authorized for file scanning passed via the header Authorization: Bearer <key>

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).

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.

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 help.nightfall.ai/docs/detector-glossary to explore the comprehensive list of supported data types and file formats

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.

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

You may use Pre-Configured Detection Rules or Create Inline Detection Rules

Text scanning supports the use of Exclusion Rules, Context Rules, and Redaction as well as other Scanning Features.

For scanning files, see Scanning Files.

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

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.

4. Select Developer APIs.

5. Select the Detection Rules to be included in the policy and click Next.

6. Configure a notification channel. Click to learn more about alert channel configuration. If you wish to setup a webhook alert channel, click for details.

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.

4. Enter a name for the policy.

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.

4. Click Validate.

Similarly, you can also use HTTP alerts channel.

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 Creating an API Key to see how to create the necessary Authentication token for making API calls.

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

• See Creating Detection Rules for how to aggregate Detectors for use in the scan endpoint

• See for how to set up common workflows that combine your Detection Rules with remediation actions such as alerting.

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.

• Protected Health Information (PHI)

• API Keys

• Passwords

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 scan request. File scans themselves are always performed asynchronously because of complexity relating to text extraction and data volume.

• to notify a client about results from a text scan request. Although results are already delivered synchronously in the response object, clients may configure the request to forward results to other platforms such a webhook, SIEM endpoint, or email through a policy

To create a webhook you will need to and then set up a

For more information on how webhooks and asynchronous notifications are used please see our guides on:

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 dashboard.

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.

Use this value as shown in the code examples that are used in the following sections.

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.

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.

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:

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

• mental health conditions

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 under the DEVELOPER APIS section.

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 for further assistance.

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

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

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.

Example: "What has user been doing in the last 30 days?"

Get a complete timeline of user activity including file downloads, permission changes, and policy violations. The AI summarizes patterns and flags anomalies automatically.

Example: "Resolve all pending Slack violations from the #general channel"

Instead of manually processing violations one by one, describe the action in natural language. The AI finds matching violations and executes the appropriate remediation action.

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 . 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 section.

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

If you encounter issues not covered in this guide:

• Email Support:

We continuously improve the MCP server based on customer feedback. Please share:

• Feature requests for new query capabilities

• Use cases we should highlight in documentation

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 sales@nightfall.ai or via the contact form on our website to discuss your specific needs and get a tailored pricing quote.

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

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.

APIs to monitor and manager integrations

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

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:

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 https://help.nightfall.ai/detection_platform/detection_glossary for a complete list of available detectors.

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.

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.

Don't hesitate to get in touch with us directly via email at support@nightfall.ai or through the contact form on our website.

We host Nightfall Developer Office Hours 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!

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 sales@nightfall.ai.

Email Us

For support inquiries, please email us at support@nightfall.ai.

For sales inquiries, please email us at sales@nightfall.ai.

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.

The built-in set of Detectors cover a number of different categories of data, including:

• Standard PII (e.g. social security number, driver's license number, ID card image)

• PCI (Credit Card Number, credit card image)

• Healthcare (e.g. PHI, US Medicare Beneficiary Number)

• Finance - Banking (e.g. SWIFT code, IBAN code, US bank routing number)

• Network (e.g. an IP Address)

The full set is enumerated in the .

Nightfall also supports and word lists for any custom detectors that you may want to implement.

Over time, we've aggregated the following , 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 .

You can input custom detectors in two ways: directly in the Nightfall by navigating to Detectors → New Detector → Regular expression, or

An exclusion rule is a regular expression or word list that will be used once a Detector is triggered by its primary expression or word list to eliminate false positives.

For instance, you may have a Detector designed to detect phone numbers. However, you may have a particular set of phone numbers that you use for testing purposes that are known not to be valid (e.g. they start with the prefix 555) and this should be ignored. Adding an exclusion rule would allow you to prevent those matches from being returned by the API.

See:

Context Rules are additional matching expressions for a Detector that may be used to adjust the confidence score of a match.

You may provide a regular expression and the number of leading or trailing characters within which a match of that expression must occur in order to adjust the confidence level to a particular level.

For instance, if you found a sequence that appeared to be a social security number based on its length or formatting, you might boost the confidence score if it was preceded by the text like “SSN” or “Social Security Number.”

You may request that a sequence of bytes of a given length be provided from before and after the text that triggers a Detection Rule.

This information can help you better understand whether or not something is an actual violation by observing the circumstances within which the detected text was found.

You are limited to a maximum of 40 bytes of this context text preceding and trailing the match for a total of 80 bytes overall.

See:

Detection Rules are aggregations of Detectors that are assigned a minimum confidence level. The identifiers of Detection Rules are used as a parameter to the API.

You may create Detection Rules as described in the section and use their identifier as part of API calls to scan content.

Alternatively you may specify Detection Rules in each API call, as described in the scan method documentation below.

A Detection Rule is composed of a list of Detectors with which you wish to scan each request payload, where any or all Detectors may be satisfied in order to trigger the rule. You can add up to 50 total Detectors with a limit of 30 regular expression type custom detectors.

Additionally, each Detector in the Detection Rule is assigned a “minimum confidence” level (see and a minimum number of findings to determine if the Detection Rule should be considered triggered.

Detection results will be returned with one of the following confidence values.

In practice, the API will only return detections assigned a POSSIBLE or higher confidence level.

• VERY_LIKELY (recommended)

• LIKELY

• POSSIBLE

• UNLIKELY

Learn more about what different confidence levels mean and how to choose the right minimum confidence level for your detection rule .

Policies allow you to create templates for the 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.

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 and a rowRange that will allow you to identify the specific row and column within the tabular data wherein the finding is present.

This functionality is applicable to the following mime types:

• text/csv

• text/tab-separated-values

• application/vnd.openxmlformats-officedocument.spreadsheetml.sheet

• application/vnd.ms-excel

data files are also accepted.

Below is a sample match of a spreadsheet containing dummy PII where a SSN was detected in the 2nd column and 55th row.

Nightfall provides special handling for archives of GitHub repositories.

Nightfall will scan the repository history to discover findings in particular checkin, returning the hash for the checkin.

In order to scan the repository, you will need to create a clone, i.e.

git clone https://github.com/nightfallai/nightfall-go-sdk.git

This creates a clone of the Nightfall go SDK.

You will then need to create an archive that can be uploaded using Nightfall's file scanning sequence.

zip -r directory.zip directory

Note that in order to work, the hidden directory .github must be included in the archive.

When you initiate the with this file, you will receive scan results that contain the commitHash property filled in.

Using the Nightfall go SDK archive created above, a simple example would be to scan for URLs (i.e. strings starting with http:// or https://), which will send results such as the following:

To retrieve the specific checkout, you will need to clone the repository, i.e.

git clone https://github.com/nightfallai/nightfall-go-sdk.git

You can then checkout the specific commit using the commit hash returned by Nightfall.

Note that you are in a when working with this sort of check out of a repository.

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"

• "List high-risk violations in the last 7 days"

• "Find violations in the engineering-team Slack channel"

• "What GitHub repositories have violations?"

• "Show violations by "

• "What files has accessed this week?"

• "Track user activity for "

• "Find all Salesforce violations in the Sales org"

• "Show Google Drive files shared externally"

• "List Confluence pages with public links"

• "Check Microsoft Teams for sensitive data sharing"

• "Compare violation trends month over month"

• "Identify users with the most violations"

• "What types of sensitive data are most commonly exposed?"

• "Summarize security posture improvements over the last quarter"

• "Mark these violations as resolved"

• "Block access to this file"

• "Delete violations in test channels"

• "Quarantine all high-risk violations from last month"

Connection Issues

Problem: "Unauthorized" or 401 errors

Solutions:

• Verify your API key is correctly pasted in the configuration file (no extra spaces)

• Check that the API key hasn't expired or been revoked in the Nightfall dashboard

• Ensure you're using "Bearer YOUR_KEY" format, not just "YOUR_KEY"

• Restart your AI client after updating the configuration

Problem: MCP server not appearing in client

Solutions:

• Verify the configuration file is in the correct location for your operating system

• Check JSON syntax is valid (use a JSON validator if needed)

• For Claude Desktop: ensure npx is installed (requires Node.js)

• Review client logs for specific error messages

Problem: No results returned

Solutions:

• Broaden your time range (data may be older than expected)

• Check filter criteria aren't too restrictive

• Verify the integration or resource name is spelled correctly

• Confirm you have violations/events matching your criteria in the Nightfall dashboard

Problem: Rate limit errors (429)

Solutions:

• Wait for the duration specified in the Retry-After header

• Reduce query frequency or batch similar requests

• Contact Nightfall support if hitting limits during normal usage

Problem: Slow query responses

Solutions:

• Use more specific filters to reduce result set size

• Limit date ranges to recent periods when possible

• Request paginated results for large datasets

• Complex aggregations may take longer—this is expected behavior

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

• Redacting Sensitive Data in 4 Lines of Code

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

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.

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

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.

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.

As part of submitting a file scan request, the request payload must contain a reference to a URL defined as part of a policy defined inline.

When Nightfall prepares a file scan operation, it will issue a challenge to the to verify its legitimacy.

After the file scan has been processed asynchronously, the results will be delivered to the webhook.

For a file scan, your webhook will receive a request body that will be a JSON payload containing:

• the upload UUID (uploadID)

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.

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.

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.

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.

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)

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.

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.

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 "[[]]".

Technical Specifications

Before connecting to the Nightfall MCP server, ensure you have:

1. An active Nightfall subscription with appropriate permissions

2. A Nightfall API key from the Developer Platform

3. An MCP-compatible AI client (Claude Desktop, Cursor, Windsurf, or custom)

1. Log in to the Nightfall Dashboard at

2. Navigate to Developer Platform > API Keys

3. Click Generate New Key and provide a descriptive name (e.g., "Claude MCP Integration")

4. Copy the generated key immediately - it will only be shown once

⚠️ Security Note: Your API key provides full access to your Nightfall data. Never commit it to version control or share it publicly.

The Nightfall MCP server supports any MCP-compatible client. Below are configuration examples for popular AI tools.

Add the following configuration to your Claude Desktop config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

json

Replace YOUR_NIGHTFALL_API_KEY with your actual API key, then restart Claude Desktop.

Open Cursor Settings > MCP and add:

json

Add to ~/.codeium/windsurf/mcp_config.json:

json

For custom MCP clients, connect using Streamable HTTP transport:

The server operates in stateless mode and returns application/json responses.

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.

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

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.

In order to use asynchronous notifications with Slack, you must install the Nightfall Alerts plugin from the Slack Marketplace.

See our end user documentation on installing for more details.

Once you have authenticated Nightfall to your Slack workspace, you can provide any public

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

The Document will guide you in making your first API request.

This page will get you up and running with the Nightfall API so you can start scanning for sensitive data.

Obtain an API key

The Nightfall API requires a valid API key to authenticate your API requests.

You can create API keys in the Dashboard.

Learn more about Authentication and Security.

Make an API Scan Request

Below is an example request to the scan endpoint.

To run this example yourself, replace the API key (NF-rEpLaCe...) with the one you created in the dashboard or set it as the environment variable NIGHTFALL_API_KEY as necessary.

The cURL example may be run from the command line without any additional installation. To run the Python example, you will need to download the corresponding SDK.

The Policy (policy) you define indicates what to scan for in your payload with a logical grouped (ANY or ALL) set of Detection Rules (detectionRules).

Detection Rules can be defined two ways:

• , as shown above

• in the Nightall app, which you will then .

Learn more about in the Nightfall app to create your own Detectors, Detection Rules, and Policies. See for an example as to how to execute queries using an existing Detection Rules UUID.

In the example above, two of Nightfall's native Detectors are being used: US_SOCIAL_SECURITY_NUMBER and CREDIT_CARD_NUMBER.

You can find a full list of native Detectors in the .

If you don't want to create your Detectors, Detection Rules, and Policies in the Nightfall app, but would prefer to do it in code, it is possible to define with your own regular expressions or word list as well as extend our native Detectors with and rules.

When defining a Detection Rule, you configure the minimum(minConfidence) and minimum number of times the match must be found (minNumFindings) for the rule to be triggered.

Another feature Nightfall offers is the ability to sensitive findings. Detectors may be configured (via redactionConfig) to replace the text that triggered them with a variety of customizable masks, including an encrypted version of the text.

In the payload body, you can see that we are submitting a list of three different strings to scan (payload). The first will trigger the U.S. Social Security Detector. The last will trigger the credit card Detector. The middle example will trigger neither.

The Nightfall API returns a response with an array (findings) with a length that corresponds to the length of the payload array. In this example, only the first and last items in the request payload triggered the Detectors, so the second element of the array is empty.

In the first element of the array, you can see details about which Detection Rule was triggered and the data that was found (finding). The response also provides a confidence level (confidence), as well as the location within the original text where the data was found either in terms of bytes (byteRange) or characters (codepointRange).

Congratulations! You have successfully completed the Nightfall Quickstart.

You can modify the Detectors or payload in the example request to get more practice with the Nightfall API.

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 jane@example.com been doing recently?"

• "Summarize the risk and recommend next steps"

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?"

• "Create a remediation plan for the critical violations"

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

3. Gather posture statistics: Use list_posture_events

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"

• "What trends are emerging in our security posture?"

Purpose: Assess security risk profile for a specific user

Steps:

1. Find violations: Use search_violations with query user_email:[email] to find all DLP violations

2. Find exfiltration events: Use search_exfiltration_events with query actor_email:[email]

3. Find posture events

Example Conversation:

• "Assess the risk profile for user john@company.com"

• "Show me all violations and events involving this user"

• "What patterns emerge from their behavior?"

• "Recommend appropriate actions based on their risk level"

Purpose: Execute remediation actions on specific violations

Steps:

1. Review violation details: Use get_violation to understand context and available actions

2. Check findings: Use get_violation_findings to see what data is at risk

3. Verify appropriate action: Confirm the remediation action matches the violation severity and context

Example Conversation:

• "Show me details for violation xyz-789"

• "What are the available remediation actions?"

• "Resolve this violation and document the action"

• "Confirm the remediation was successful"

• "Search for GitHub violations, get details on the top 3, and show me the sensitive findings"

• "Find all violations by jane@company.com, check her recent activity, and assess her risk profile"

• "List exfiltration events from last week, get details on any involving bulk downloads, and summarize the risk"

• "Search for high-risk Slack violations, review their findings, and recommend remediation actions"

The most effective investigations use multiple tools in sequence. Follow these patterns:

Basic Investigation Pattern:

1. Search → Get Details → Analyze → Recommend

Deep Investigation Pattern:

1. Search → Get Details → Get Findings → Check Activity → Assess Actor → Recommend

Remediation Pattern:

1. Search → Get Details → Verify Context → Take Action → Confirm

When asking complex questions, the AI will automatically chain tools in the optimal order. You can also explicitly request multi-step workflows: "Search for high-risk violations, review the top 5, and create a remediation plan."

• Be specific about time ranges: "last 7 days" is clearer than "recently"

• Use exact usernames or email addresses when investigating specific actors

• Ask follow-up questions to drill deeper: "Show me the findings" after reviewing a violation

• Request summaries for complex data: "Summarize these violations by severity"

• Store API keys in secure credential managers, never in code or configuration files committed to version control

• Rotate API keys quarterly or immediately if compromise is suspected

• Use dedicated API keys for each integration rather than sharing across systems

• Review API key permissions regularly and follow least-privilege principles

• Be specific about time ranges: "last 7 days" is clearer than "recently"

• Use exact usernames or email addresses when investigating specific actors

• Ask follow-up questions to drill deeper: "Show me the findings" after reviewing a violation

• Request summaries for complex data: "Summarize these violations by severity"

• Set up dedicated Slack/Teams channels for security alerts and use MCP to investigate directly from your collaboration tool

• Create saved prompts for common investigations to maintain consistency across your security team

• Document investigation procedures that leverage MCP for faster onboarding of new analysts

• Use MCP in conjunction with the Nightfall console for comprehensive security operations

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

• An existing Nightfall Detection Rule

• 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.

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

We recommend you first set up a virtual environment. You can learn more about that .

You can download the Nightfall SDK from PyPi like this:

We will be using the built-in os library to help run this sample API script. This will be used to help extract the API Key from the OS as an environment variable.

Next, we extract our API Key, and abstract a nightfall class from the SDK, for it. 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 Detection Rule with which we wish to scan our data. The Detection Rule can be and referenced by UUID.

In this example, we will use some example data in the payload List.

Payloads must be under 500 KB when using the Scan API. If your file is larger than the limit, consider using the file api, which is also available via the Python SDK.

We will ignore the second parameter as we do not have redaction configured for this request.

With the Nightfall API, you can redact and mask your findings. You can add a Redaction Config, as part of your Detection Rule. For more information on how to use redaction, and its specific options, please refer to the guide here.

Now we are ready to review the results from the Nightfall SDK to check if there is any sensitive data in our file. Since the results will be in a , we can use the built-in __repr__ functions to format the results in a user-friendly and readable manner.

All data and sample findings shown below are validated, non-sensitive, examples of sample data.

If there are no sensitive findings in our payload, the response will be as shown in the 'empty response' pane below:

And that's it 🎉

You are now ready to use the Python SDK for other scenarios.

The file scan API has first-class support for text extraction and scanning on all MIME types enumerated below.

Certain file types receive special handling, such as and , that results in more precise information about the location of findings within the source file.

• application/json

• application/x-ndjson

• application/x-php