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DATA LOSS PREVENTION
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DATA PRIVACY FOR AI
Firewall for AI Developers
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Prevent Secrets Sprawl
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Where are your credentials and secrets, and how are you protecting them?
AI-powered solutions are becoming increasingly prevalent in enterprise environments, leading to a new trend in procurement: the need for comprehensive AI governance frameworks.
These popular DLP policies are useful tools for protecting your sensitive data, preventing data exfiltration, securing your AI usage, and more.
In the rapidly evolving AI landscape, the principle of least privilege is a crucial security and compliance consideration.
As organizations increasingly build AI applications, firewalls for AI are an important component of modern data security strategies.
As we continue to innovate, Nightfall's central mission is to empower enterprises big and small to navigate the digital world with confidence, while ensuring the security and privacy of their sensitive data.
As GenAI transforms customer support, data privacy remains paramount.
4 ways to secure your HAR files.
AI development tools introduce a number of new security challenges. Here's how to stay secure.
Nightfall’s vision for DLP in an age of GenAI.
In this blog post, we will discuss the essential cybersecurity requirements that vendors must adhere to when selling to banks.
In this blog post, we will discuss some essential cybersecurity requirements for selling to health insurers, including deploying cloud data leak prevention (DLP)
Detect, pinpoint, and triage the highest risks in your environments: Exposed secrets and credentials.
Learn how to add DLP to communications services like Twilio and SendGrid.
Learn about the essential cybersecurity requirements that businesses must meet to sell to the federal government.
A deep dive into the six key security risks posted by Generative AI and the role of Data Leak Prevention (DLP) solutions in mitigating potential risks.
Enterprise data is already transmitting to systems like OpenAI via some of your favorite apps.
Learn how to implement content filtering when using ChatGPT.
When two companies merge, there is typically a lot of data that needs to be transferred between the two organizations. This data may include confidential information such as customer records, financial reports, and employee data. If this data falls into the wrong hands, it could be used to commit fraud or theft. That's where data loss prevention (DLP) comes in.
Jira and Confluence house high volumes of customer information, tickets, notes, wiki articles, and more. To scan Jira and Confluence Data Center or Server editions, you can use Nightfall’s APIs to scan data at-rest in these silos. In this article, we’ll walk through how you can run a full historical scan on your Jira and Confluence data to discover sensitive data, like API keys and PII.
How to implement data loss prevention (DLP) on Slack, and detect leakage of sensitive data across any Slack workspace.
Salesforce houses high volumes of customer information, support tickets, quotes and files, synced emails, tasks & notes, and much more. This data can often be accessed by teams across the company who may leverage Salesforce to provide prospects and customers with a great customer experience.
In this tutorial, we will build a lightweight endpoint DLP scanner that scans files on your device in real-time for sensitive data like PII & secrets using Nightfall's data loss prevention APIs.
In this tutorial, we will create and deploy a server that scans files for sensitive data (like credit card numbers) with Nightfall's data loss prevention (DLP) APIs and the Flask framework.
In this tutorial, we'll demonstrate how easy it is to redact sensitive data and give you a more in-depth look at various redaction techniques, how Nightfall's data loss prevention (DLP) API works, and touch upon use cases for redaction techniques.
In this tutorial, we will walk through the end-to-end process of scanning your Amazon S3 buckets for sensitive data with Nightfall's S3 Sensitive Data Scanner.
Git is a complicated protocol and because git is designed to be a full historical trail of commits, remediation is non-trivial. As a result, we’ve outlined best practices to remediate violations below.
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