A pretrained if private key is in source code classifier that sorts text into one of 2 categories. Use the if private key is in source code API immediately, no training required, then adapt it to your own data when you need more.
Drop in some text and get the prediction back. No signup, no setup.
A sample of the 2 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "The text you want to classify"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "The text you want to classify"},
)
print(response.json())Example response
{
"labelName": "Contains Private Key",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if private key is in source code categories, served on Nyckel's own infrastructure — your text snippet stays on Nyckel.
Send raw text to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
This use case involves scanning source code repositories for private keys that may inadvertently be exposed. By identifying these sensitive keys, organizations can mitigate the risk of unauthorized access and data breaches.
Integrating the true text classification function within CI/CD pipelines can ensure that no private keys are included in code commits. This proactive measure helps maintain secure software development practices and prevents leakage of sensitive information.
When developers contribute to open source projects, it's crucial to ensure that no private keys from their personal projects are included. This function can automatically scan contributions to maintain the integrity of open source code and protect user credentials.
In the event of a security incident, this function can be used to conduct a rapid assessment of source code to identify any leaked or compromised private keys. This allows security teams to respond quickly and effectively to mitigate potential damage.
Organizations must adhere to various regulations regarding data protection and security. By employing this true text classification function, companies can conduct regular checks on their code bases to ensure compliance with standards such as GDPR or CCPA, thus avoiding potential fines.
During the code review process, using this identifier can help developers focus on critical areas by flagging any private keys present in the code. This not only enhances code quality but also ensures that best security practices are maintained throughout the development lifecycle.
Incorporating this function into training programs for developers can help them understand the importance of securing sensitive information. By providing real-time feedback on their code practices, it empowers developers to adopt better habits and reduce the risk of accidentally exposing private keys.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of text samples in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This if private key is in source code classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.