Customer support tickets are a potential vector for leaking customer PII. By utilizing HubSpot's CRM tickets API in conjunction with Nightfall AI’s scan API you can discover, classify, and remediate sensitive data within your customer support system.
You will need a few things to follow along with this tutorial:
A HubSpot account and API key
A Nightfall API key
An existing Nightfall Detection Rule
A Python 3 environment (version 3.6 or later)
Most recent version of Python Nightfall SDK
To accomplish this, we will install the version required of the Nightfall SDK:
We will be using Python and importing the following libraries:
We've configured the HubSpot and Nightfall API keys as environment variables so they don't need to be committed directly into our code.
Next, we define the Detection Rule with which we wish to scan our data. The Detection Rule can be pre-made in the Nightfall web app and referenced by UUID.
Also, we abstract a nightfall class from the SDK, from our API key.
Here we'll define the headers and other request parameters that we will be using later to call the Hubspot API.
Let’s start by using HubSpot API to retrieve all support tickets in our account. As the HubSpot API takes a "page limit" parameter, we will query the tickets over multiple requests to the HubSpot API, checking for list completion on each call. We'll compile the tickets into a list called all_tickets.
The first row of our all_findings object will constitute our headers since we will dump this object to a CSV file later. We won't include the sensitive fragments themselves to avoid replicating PII unnecessarily, but we'll include a redacted copy with 3 characters exposed to help identify it during the review process.
'Properties' -> 'Content' is the only field where users can supply their data, so it is the only field we need to pass to the Nightfall API. We store the ticket IDs in a matching list so that we can put a location to our findings later.
We are now ready to call the Nightfall API to scan our HubSpot tickets. This tutorial assumes that the totality of your tickets falls under the payload limit of the Nightfall API. In practice, you may want to check the size of your payload using a method like sys.getsizeof() and chunk the payload across multiple requests if appropriate.
Now that we have a collection of all of our tickets, we will begin constructing an all_findings object to collect our results. The first row of our all_findings object will constitute our headers since we will dump this object to a CSV file later.
This example will include the full finding below. As the finding might be a piece of sensitive data, we would recommend using the Redaction feature of the Nightfall API to mask your data. More information can be seen in the 'Using Redaction to Mask Findings' section below.
For each finding in each ticket, we collect the required information from the Nightfall API to identify and locate the sensitive data, pairing them with the HubSpot ticket IDs we set aside earlier.
Finally, we export our results to a CSV so they can be easily reviewed.
That's it! You now have insight into all of the sensitive data inside your customer support tickets. As a next step, we could utilize HubSpot's API to add a comment to tickets with sensitive findings, and then trigger an email alert for the offending ticket owner.
To scan your support tickets on an ongoing basis, you may consider persisting your last ticket query's paging value and/or checking the last modified date of your tickets.
With the Nightfall API, you are also able to redact and mask your HubSpot 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.
The example above is specific for the Nightfall Text Scanning API. To scan files, we can use a similar process as we did the text scanning endpoint. The process is broken down in the sections below, as the file scanning process is more intensive.
To utilize the File Scanning API you need the following:
An active API Key authorized for file scanning passed via the header Authorization: Bearer — see Authentication and Security
A Nightfall Detection Policy associated with a webhook URL
A web server configured to listen for file scanning results (more information below)
Retrieve ticket data from Hubspot
Similar to the process in the beginning of this tutorial for the text scanning endpoint, we will now initialize our and retrieve the ticket data from Hubspot:
Now we go through write the logs to a .csv file.
Begin the file upload process to the Scan API, with the above written .csv file, as shown here.
Once the files have been uploaded, begin using the scan endpoint mentioned here. Note: As can be seen in the documentation, a webhook server is required for the scan endpoint, to which it will send the scanning results. An example webhook server setup can be seen here.
The scanning endpoint will work asynchronously for the files uploaded, so you can monitor the webhook server to see the API responses and file scan findings as they come in.
Customer support tickets are a potential vector for leaking customer PII. By utilizing ZenDesk’s API in conjunction with Nightfall’s scan SDK you can discover, classify, and remediate sensitive data within your customer support system.
You will need a few things to follow along with this tutorial:
A ZenDesk account and API key
A Nightfall API key
An existing Nightfall Detection Rule
A Python 3 environment
most recent version of the Nightfall Python SDK
To accomplish this, we will install the version required of the Nightfall SDK:
We will be using Python and importing the following libraries:
We've configured the ZenDesk user and API key, as well as the Nightfall API key as environment variables so they don't need to be committed directly into our code.
Here we'll define the headers and other request parameters that we will be using later to call both APIs. Next we extract our API Key, and abstract a nightfall class from the SDK, for it.
Next we define the Detection Rule with which we wish to scan our data. The Detection Rule can be pre-made in the Nightfall web app and referenced by UUID.
Let’s start by using ZenDesk’s API to retrieve all support tickets in our account. We'll set up an "all_findings" object to compile our findings as we go.
The first row of our all_findings object will constitute our headers, since we will dump this object to a CSV file later.
This example will include the full finding below. As the finding might be a piece of sensitive data, we would recommend using the Redaction feature of the Nightfall API to mask your data. More information can be seen in the 'Using Redaction to Mask Findings' section below.
Now that we have a collection of all of our tickets, we will retrieve the set of user comments made on each of those tickets.
Note: If you are scanning a high volume of tickets, you may run into either the ZenDesk API's rate limits, or the Nightfall API's rate limits. In this tutorial, we assume that you fall under these limits, but additional code may be required to ensure this.
Within the above for loop, we compile all of the comment bodies into a list so that we can scan the entire comment thread for a ticket with a single call to the Nightfall SDK.
For each set of results we receive, we can start to compile our findings into a csv format.
Finally, we export our results to a csv so they can be easily reviewed.
That's it! You now have insight into all of the sensitive data inside your customer support tickets. As a next step, we could use these findings as an input to ZenDesk's redact API in order to clean up the original comments. We could also use ZenDesk's API to add a comment to tickets with sensitive findings triggering an email alert for the offending ticket owner.
To scan your support tickets on an ongoing basis, you may consider taking advantage of ZenDesk's Incremental Exports functionality.
Putting everything together:
That's it! You should now be set up to start using the Zendesk integration for the Nightfall Text Scanning SDK.
With the Nightfall API, you are also able to redact and mask your Zendesk ticket 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.
The example above is specific for the Nightfall Text Scanning API. To scan files, we can use a similar process as we did the text scanning endpoint. The process is broken down in the sections below, as the file scanning process is more intensive.
To utilize the File Scanning API you need the following:
An active API Key authorized for file scanning passed via the header Authorization: Bearer — see Authentication and Security
A Nightfall Detection Policy associated with a webhook URL
A web server configured to listen for file scanning results (more information below)
Retrieve ticket data from Zendesk
Similar to the process at the beginning of this tutorial for the text scanning endpoint, we will now initialize our and retrieve ticket data from Zendesk.
Now we go through write the ticket data to a .csv file.
Begin the file upload process to the Scan API, with the above written .csv file, as shown here.
Once the files have been uploaded, begin using the scan endpoint mentioned here. Note: As can be seen in the documentation, a webhook server is required for the scan endpoint, to which it will send the scanning results. An example webhook server setup can be seen here.
The scanning endpoint will work asynchronously for the files uploaded, so you can monitor the webhook server to see the API responses and file scan findings as they come in.
This section consists of various documents that assist you in scanning various popular SaaS applications using Nightfall APIs.