HiddenLayer
Security platform for AI models and the ML lifecycle

AI-native data loss prevention across SaaS, AI apps and endpoints.
Nightfall AI is an enterprise data-security product bought by security and compliance teams, not an individual tool, and adoption typically runs through procurement and security review. Its ML-based detection can reduce false positives versus pure regex, and its coverage of generative-AI data leakage is timely, but pricing is quote-based and effectiveness depends on how well it maps to your specific SaaS stack and workflows. Pilot it against real data flows before committing.
Nightfall AI is an AI-native data loss prevention platform that uses machine learning to discover and protect sensitive data, PII, credentials and secrets across SaaS apps, AI tools and endpoints. It targets enterprise security teams, adds AI-assisted incident triage, and uses quote-based pricing. Effectiveness depends on integration with an organization's specific stack and workflows.
Nightfall AI is a cloud-native data security platform that uses machine learning to detect and protect sensitive information, personal data, credentials and secrets, across the SaaS apps, AI applications and endpoints an organization uses. Rather than relying solely on regex rules, its ML detectors aim to reduce false positives while catching data that traditional pattern-matching misses. The platform has expanded from cloud DLP into broader data protection, including coverage for generative AI usage where employees might paste sensitive data into chatbots. In 2026 the company leaned into AI automation with features like an autonomous DLP analyst that helps triage and respond to incidents, reflecting a shift toward AI-assisted security operations. Nightfall is an enterprise-oriented product: pricing is quote-based, typically starting in the low five figures annually and scaling by users, data volume and API usage. Buyers should scope it against their specific SaaS stack, compliance requirements and incident-response workflows, and plan for a security-procurement process rather than self-serve signup.
Nightfall AI is an AI-native data loss prevention platform that uses machine learning to find and protect sensitive data across SaaS apps, AI tools and endpoints. It targets enterprise security teams and has added AI-assisted incident triage. Pricing is quote-based, often starting in the low five figures annually. It is an enterprise product requiring security procurement, not a self-serve tool.
Nightfall AI (nightfall.ai) is a cloud-native data security company focused on data loss prevention using machine learning.
It serves security and compliance teams at growth-stage and enterprise organizations, and has expanded from cloud DLP into broader protection covering AI applications and endpoints.
The platform uses ML detectors to identify PII, credentials, secrets and other sensitive data across integrated SaaS apps, AI tools and endpoints, with a browser and API-based deployment model. Integrations span Slack, Google Workspace, Microsoft 365, GitHub and more.
In 2026 it emphasized AI automation, including an autonomous DLP analyst to help triage and respond to incidents, reflecting a shift toward AI-assisted security operations.
Enterprise and growth-stage organizations with significant SaaS and AI usage that need to prevent sensitive-data leakage and meet compliance requirements.
Security analysts and DLP administrators monitoring alerts and tuning policies.
CISOs, security leaders and compliance officers.
IT and security architects, auditors and data-privacy teams.
A growth-stage or enterprise organization with heavy SaaS and AI usage that needs ML-driven DLP integrated across its stack.
Nightfall AI raised a $40 million Series B in 2022 led by WestBridge Capital, with participation from Next Play Capital and existing investors including Bain Capital Ventures, Venrock and Pear VC. Total disclosed funding is reported around $60 million-plus.
It discovers and protects sensitive data such as PII, credentials and secrets across SaaS applications, AI tools and endpoints.
It uses machine-learning detectors rather than relying solely on regex, aiming to reduce false positives and catch data that pattern-matching misses.
Yes, it covers generative-AI usage, helping prevent employees from exposing sensitive data in chatbots and AI applications.
No. Pricing is quote-based and scales by users, data volume and API usage; contact the vendor for a quote.
It is built for enterprise and growth-stage security and compliance teams rather than individuals or very small teams.
Side-by-side pages for pricing, features, and best-fit use cases.
Security platform for AI models and the ML lifecycle
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