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Nightfall AI launched the industry’s first autonomous data loss prevention platform Wednesday, introducing an AI agent that automatically investigates security incidents and tunes policies without human intervention — a breakthrough that could reshape how enterprises protect sensitive information in an era of expanding cyber threats.
The San Francisco-based startup’s new platform, called Nightfall Nyx, represents a fundamental shift from traditional data loss prevention tools that rely on manual rule-setting and generate high volumes of false alerts. Instead, the system uses an AI agent to mirror the work of security analysts, automatically prioritizing threats and distinguishing between legitimate business activities and genuine security risks.
“Security teams are drowning in alerts while sophisticated insider threats slip through legacy DLP systems,” said Rohan Sathe, CEO and co-founder of Nightfall, in an exclusive interview with VentureBeat. “When analysts spend hours investigating false positives only to discover that real threats went undetected because they didn’t match a predefined pattern, organizations aren’t just losing time—they’re losing control over their most sensitive data.”
The announcement comes as enterprises grapple with an explosion of data security challenges driven by remote work, cloud adoption, and the rapid proliferation of AI tools in the workplace. The global cybersecurity market, valued at approximately $173 billion in 2023, is expected to reach $270 billion by 2026, with data protection representing a significant portion of that growth.
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How AI-powered detection cuts false alerts from 80% to 5%
Traditional data loss prevention systems have long frustrated security teams with accuracy rates as low as 10-20%, according to Sathe. These legacy platforms rely heavily on pattern matching and regular expressions to identify sensitive data, creating a constant stream of false alerts that require manual investigation.
“What ends up happening is you end up staffing like a SOC analyst to go and sift through all the false positives,” Sathe explained. “With an AI kind of native approach to actually doing content classification, you can get in that like 90, 95% accuracy.”
Nightfall’s approach combines three AI-powered components: advanced content classification using large language models and computer vision, data lineage tracking that understands where information originates and travels, and autonomous policy optimization that learns from user behavior over time.
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