Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Your Microsoft 365 and Google Workspace security dashboards show green across all metrics. You've implemented data loss prevention policies, enabled advanced threat protection, and your team regularly audits security logs. Yet sensitive data continues to leave your organization through email channels. Why? Because attackers and even non-malicious insiders aren't using the obvious exfiltration techniques your tools were built to detect.

How should data security teams walk the fine line between enabling AI innovation, safeguarding sensitive data, and ensuring compliance? That question drives everything we build at Nightfall. It’s also an excellent jumping off point for an in-depth discussion among security experts.

The rapid adoption of AI, particularly generative AI tools like ChatGPT and Copilot, presents both immense productivity opportunities and significant data security challenges for organizations. While employees increasingly leverage AI for various business functions, this widespread use often occurs outside sanctioned channels, creating what's known as shadow AI.

At first, hearing this common refrain from security leaders comes as a shock. But if you know about the limitations of legacy data loss prevention (DLP) solutions, this statement makes perfect sense. Legacy DLP can leave security teams with the assumption that they have full control over their data risk profile and vectors. In reality, blind spots can occur in any security configuration.

Generative AI has gone from a novelty to an essential part of daily workflows across all teams at an organization. Whether it’s ChatGPT, Microsoft Copilot, Claude, or Google Gemini, employees are using chatbots to copy, paste, summarize, and query data at a pace and scale we have never seen before. Unfortunately, data security has not been a fundamental feature of generative AI as the technology’s popularity and functionality has exploded.

Managing endpoint security just got easier with Nightfall. Our latest updates enhance device management for endpoint security and expand data protection to give security and IT teams greater control with less overhead. Here’s the latest updates and features from Nightfall at a glance: Read more on how these updates help keep your security posture strong while minimizing distractions and unnecessary alerts.

Nightfall has been named a leader in Data Loss Prevention (DLP), Sensitive Data Discovery, Data Security, and Cloud Data Security in G2’s Spring ‘25 reports. We’d like to extend a huge thank you to all of Nightfall’s customers and supporters for making this possible - and an even bigger thank you goes to the Nightfall team’s tireless dedication to building solutions that protect our customers’ sensitive data across the sprawling enterprise attack surface.

API keys and passwords are the keys to digital kingdoms, granting access to an organization’s most valuable systems and data. Traditional data loss prevention (DLP) systems often fall short in their attempts to protect sensitive data and secrets, leaving security teams overwhelmed with false positives and noise. At Nightfall, we understand these challenges and the evolving threat landscape across SaaS and endpoints.

Earlier this year, security researchers found more than 1 million records, including user data and API keys, in an exposed DeepSeek database. This massive exposure event tells us that data exfiltration risk and AI proliferation are forever linked together: as AI tools grow in popularity and complexity, exfiltration risk rises in kind.

Security leaders in the life sciences and health technology fields know how important it is to safeguard sensitive data like protected health information (PHI), personally identifiable information (PII), and confidential research data. They also know what’s at stake with a security breach or data exfiltration event. But what’s not always clear is how to find the right solution to keep all that data safe.