Nightfall AI
AI-powered cloud data loss prevention platform that detects PII, secrets, and PHI across Slack, GitHub, Google Drive, and 100+ cloud apps in real time.
Nightfall AI is a cloud-native data loss prevention (DLP) platform that uses machine learning to detect sensitive information - PII, PHI, API keys, passwords, and credit card numbers - across cloud collaboration tools, code repositories, and SaaS applications in real time. Founded in 2018 in San Francisco, Nightfall raised $40 million in Series B funding and serves security teams at companies including Clubhouse, Hinge, and ComScore. Unlike legacy DLP tools built for on-premise networks, Nightfall connects via API to Slack, GitHub, Google Drive, Confluence, Jira, and 100+ other cloud apps to scan messages, files, and code commits as they are created. The platform automatically remediates violations by deleting content, quarantining files, or alerting the data owner. An API-first design enables embedding Nightfall's detection capabilities into custom applications and CI/CD pipelines.
Key Features
- Real-time PII, PHI, API key, and secret detection across Slack, GitHub, Google Drive, Jira, and 100+ cloud applications
- Custom detectors define organization-specific sensitive data patterns using regex rules, ML models, or a combination
- Automated remediation deletes, quarantines, or notifies on policy violations without requiring manual security team review
- Developer REST API and SDK embed sensitive data detection into custom applications, data pipelines, and CI/CD workflows
- GitHub secret scanning identifies accidentally committed credentials, tokens, and private keys in repositories before exposure
- HIPAA, PCI-DSS, SOC 2, and GDPR compliance reporting aggregates policy violation data for regulatory audit requirements
- Risk dashboard shows sensitive data exposure events across all connected cloud apps for unified security team visibility
Use Cases
- Security teams preventing PHI from being shared through Slack or Google Drive in violation of HIPAA requirements
- Developer platforms scanning code commits for accidentally exposed API keys and credentials before they reach production
- Compliance teams monitoring cloud collaboration tools for credit card numbers and SSNs shared outside approved channels
- SaaS companies embedding Nightfall detection into their own product to scan user-uploaded content for sensitive data violations
Pros
- API-first design makes Nightfall embeddable in custom applications and CI/CD pipelines beyond standard SaaS app integrations
- Real-time detection with automated remediation catches and removes sensitive data before it spreads across connected systems
- Broad integration coverage across Slack, GitHub, Google Drive, and 100+ apps reduces blind spots in cloud security posture
Cons
- ML detection produces false positives on synthetic test data, obfuscated credentials, and non-sensitive pattern matches
- Pricing scales with data volume and integrations - large organizations with many connected apps face significant annual costs
- Automatic deletion remediation requires careful policy tuning to avoid removing legitimate business files or messages
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