Our customers often tell us about how they implement manual classification policies. However, with several hundreds of files created daily, and constant sharing between teams, it becomes impossible to enforce secure sharing and sensitive data protection. Imagine that your sales team just accidentally shared a spreadsheet containing customer credit card details with an external vendor. Or perhaps your HR department stored employee health records in a folder that wasn't properly restricted.

Where are your credentials and secrets, and how are you protecting them? These are fair questions, considering the pervasiveness of secrets sprawl. We recently conducted research over 12 months to determine where enterprises’ secrets were residing within their systems, like GitHub, Confluence, Zendesk and Slack. In addition to API keys and passwords, secrets like SSL certificates, usernames and others are spilling into enterprises’ cloud environments and increasing the risk of a breach.

When it comes to building a comprehensive data security strategy, everything hinges on finding and accurately classifying all your sensitive data. It seems security professionals have finally given up on legacy solutions that require extensive labeling and manual data mapping — and not a moment too soon. We're confident no one will mourn the passing of legacy solutions.

These days, your organization's data is its most valuable asset. But what happens when that data slips through your fingers? Picture this: It's Friday afternoon, and your security team receives an alert about an unusual number of downloads from your cloud storage app, Google Drive. As you investigate, you realize it's corporate IP that's being downloaded en masse by a departing employee. This type of exfiltration incident isn't just hypothetical; it's a reality that organizations face on the daily.

AI-powered solutions are becoming increasingly prevalent in enterprise environments, leading to a new trend in procurement: the need for comprehensive AI governance frameworks. This shift reflects a growing awareness of AI-specific risks and the need for more granular controls. As the founder of Nightfall AI, a startup building AI models for data security, I've navigated hundreds of procurement cycles and observed a rise in AI-specific requirements.

Data security leaders, take note: Nightfall has emerged as a frontrunner in G2's Fall 2024 reports, securing top positions in Data Loss Prevention Software, Data Security Software and Sensitive Data Discovery Software categories. This achievement reflects our unwavering commitment to excellence, as well as your trust in our AI-powered solutions. We extend our deepest gratitude to our valued customers and supporters, as your feedback helps us to drive innovation.

San Francisco, California, September 18, 2024 — Nightfall AI has been named a winner in the prestigious 2024 SC Awards. Nightfall has been recognized in the Best Data Security Solution and Best Insider Threat Solution categories, underscoring their team’s commitment to excellence and leadership in the cybersecurity industry. View the full list of 2024 SC Awards winners here.

These days, we use Gmail for everything from customer service to telehealth. Over time, your Gmail account might become a treasure trove of sensitive PII, PCI, PHI, and passwords that hackers can leverage. In fact, as of this year, just under half of all data breaches involve email. So, what can you do to protect your inbox? Enter: email encryption.

As Generative AI (GenAI) and Large Language Models (LLMs) become integral to modern apps, we face a critical challenge of protecting sensitive user data from inadvertent exposure. In this article, we’ll explore the importance of content filtering in LLM-powered apps, and provide strategies for its implementation. Looking for step-by-step tutorials on prompt sanitization for OpenAI, Langchain, Anthropic, and more? Skip down to the “Tutorials & further learning” section below.