AI-native data loss prevention across SaaS, endpoints, and GenAI apps
Nightfall AI builds data loss prevention software spanning SaaS apps, endpoints, and generative AI tools. The stack shows deep endpoint work: Swift, Objective-C and IOKit for macOS system extensions, plus eBPF and netfilter for a Linux DLP agent, while Rust adoption hints at lower-level performance needs. Senior and VP-heavy hiring alongside efforts to build marketing and GTM from scratch points to a company professionalizing go-to-market, even as overall hiring slows.
Nightfall AI is a San Francisco-based, privately held company founded in 2018 with 51–200 employees. It sells a data loss prevention platform to security teams, covering data discovery and classification, exfiltration prevention, and controls on sensitive data shared with AI applications. Its product spans SaaS integrations, endpoint agents for macOS and Linux, and real-time detection services built on Python, Go, Java and Kafka. The company hires in the United States and India, with engineering as its largest function.
Nightfall AI uses Python, Go, Java, C++, Swift and Objective-C, with Kafka, Cassandra, MySQL, Hadoop and Hive for data, plus eBPF and netfilter for Linux endpoint work. It is adopting Rust.
Yes. Engineering is Nightfall AI's largest hiring area, skewed toward senior roles, with additional openings in security, marketing, sales and support across the United States and India.
Nightfall AI is building real-time microservices for data detection, streaming data infrastructure, macOS system extensions, and an AI-native DLP agent for Linux using eBPF kernel-level event interception.
Other companies in the same industry, closest in size
Nightfall AI's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.