Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Large data breaches rarely begin with dramatic system failures. More often, they start with sustained, unauthorized access to sensitive data that goes undetected for months. The recent breach at Coupang, South Korea’s largest e-commerce platform, illustrates this pattern clearly. Nearly 34 million customer records were likely exposed over an extended period before detection.

DLP solutions have a challenge in detecting standard document types: financial records, source code, and customer lists. Moreover, what happens when your organization needs to protect business-critical documents that don't fit pre-built categories? Or when you need more granular classification to support specific workflows? Traditional approaches force you to choose between brittle regex patterns that generate false positives.

In our recent analysis of AI browser exfiltration risks, we exposed how OpenAI's Atlas and Perplexity's Comet create permanent backdoors to sensitive data through persistent memory, autonomous agents, and cross-platform sync. The challenges with AI native browsers strongly resonated with CISO’s and security leaders we speak with on a daily basis. But the threat extends far beyond Atlas and Comet.

Customer support teams face an impossible paradox: they need to help customers quickly, but customers routinely share sensitive information that creates compliance risks and security exposure. Credit card numbers pasted into chat. Driver's licenses attached to verification tickets. Medical records uploaded to troubleshoot healthcare apps. Social security numbers submitted through web forms. Traditional DLP wasn't built for this reality.

A leading cybersecurity vendor recently terminated an employee who took internal screenshots and shared them with threat actors, who then attempted to pass off the leaked material as evidence of a system breach. While no customer data was compromised and production systems remained secure, the incident exposed a blind spot that should concern every CISO: authorized users with legitimate access becoming your biggest vulnerability.

In finance, protecting customer data isn’t just good practice. It’s a regulatory mandate. The SEC’s Regulation S-P (Privacy of Consumer Financial Information) requires financial firms to guard against unauthorized access, maintain robust data-disposal practices, and have a formal incident response program. As the threat landscape has evolved, so has the regulation. This all means one thing: complacency is no longer an option.

For CISOs planning cloud migrations: Your decade of Jira and Confluence data contains thousands of exposed credentials, patient records, and payment card numbers. Here's how to clean it up and secure it after migration.

This blog post is adapted from a recent episode of The Cloudcast podcast featuring Rohan Sathe, CEO and co-founder of Nightfall AI. Listen to the full conversation here. Your employees are uploading company documents to ChatGPT. Your healthcare teams are transcribing sensitive call recordings and feeding them into LLMs. Your finance department is pasting confidential spreadsheets into publicly accessible AI tools. And unless you have visibility into these workflows, you have no idea it's happening.

Here's the latest product news and updates from Nightfall at a glance.