Nightfall
Nightfall
Updated August 20, 2025
Updated August 20, 2025
Nightfall uses machine learning to detect and protect sensitive data across cloud services and applications. It integrates with existing workflows to automate data classification and enforce security policies.
Nightfall uses machine learning to detect and protect sensitive data across cloud services and applications. It integrates with existing workflows to automate data classification and enforce security policies.
Data Discovery and Classification
Data Loss Prevention
Cost considerations
Cost considerations
Functionality
Functionality
Compatibility
Compatibility
User experience
User experience
Customer support
Customer support
Why these ratings?
Cyberse perspective
Cyberse perspective
Solution details
Target industry
Technology
Healthcare
Financial services
Services support
In-house services
Managed services
Product features
Data Loss Prevention (DLP)
Data classification
Pricing
Free trial available
Market segment
Small business
Enterprise
Midmarket
Key features
API access
Integrations
Security automation
Deployment
Cloud-native
Cloud ecosystem partners
Amazon Web Services
Microsoft Azure Cloud
Google Cloud Platform
Ratings
Cost considerations
Nightfall uses simple per-user tiers, and buyers pay a median $23 672 per year—well below many legacy DLP suites. Extra costs arise mainly for large at-rest data packs or extra endpoints, keeping add-ons modest for most teams. The predictable structure and frequent discounts give finance leaders clear budget visibility and a sound cost-to-risk ratio.
Cost considerations
Nightfall uses simple per-user tiers, and buyers pay a median $23 672 per year—well below many legacy DLP suites. Extra costs arise mainly for large at-rest data packs or extra endpoints, keeping add-ons modest for most teams. The predictable structure and frequent discounts give finance leaders clear budget visibility and a sound cost-to-risk ratio.
User count
Data scanned (TB/month)
Connector count
Functionality
Nightfall uses AI to automatically discover and classify sensitive data across major SaaS apps and enforce DLP actions. Its automated email encryption adds another layer of protection, yet the documentation shows no full key-lifecycle management or dedicated user behavior analytics, placing functionality just below the top tier
Functionality
Nightfall uses AI to automatically discover and classify sensitive data across major SaaS apps and enforce DLP actions. Its automated email encryption adds another layer of protection, yet the documentation shows no full key-lifecycle management or dedicated user behavior analytics, placing functionality just below the top tier
Compatibility
Nightfall offers plug-and-play connectors for major SaaS services and cloud storage like Slack, GitHub, AWS S3, and Google Drive, and forwards findings to Splunk or other SIEMs through syslog or JSON webhooks. Scanning databases such as Snowflake is supported, and on-prem data can be protected via the public REST API with modest scripting. Because almost all mainstream environments connect natively and only a few require light customization, Compatibility scores 4
Compatibility
Nightfall offers plug-and-play connectors for major SaaS services and cloud storage like Slack, GitHub, AWS S3, and Google Drive, and forwards findings to Splunk or other SIEMs through syslog or JSON webhooks. Scanning databases such as Snowflake is supported, and on-prem data can be protected via the public REST API with modest scripting. Because almost all mainstream environments connect natively and only a few require light customization, Compatibility scores 4
User experience
Nightfall offers an intuitive dashboard with guided policy templates and alerts that users say are easy to set up and review, keeping the learning curve short. Available information does not mention visual data-flow maps or risk heatmaps, so the user experience aligns with a solid level 4 rather than the rubric’s highest tier.
User experience
Nightfall offers an intuitive dashboard with guided policy templates and alerts that users say are easy to set up and review, keeping the learning curve short. Available information does not mention visual data-flow maps or risk heatmaps, so the user experience aligns with a solid level 4 rather than the rubric’s highest tier.
Customer support
Support is offered via email, phone, live chat and an online FAQ but public materials do not promise 24×7 coverage. Most users describe helpful, knowledgeable agents, yet several reviews note tickets sitting for days, so responsiveness is solid but not consistently prompt. No clear evidence of regular content update cycles or expert advisory services keeps the score at mid-tier.
Customer support
Support is offered via email, phone, live chat and an online FAQ but public materials do not promise 24×7 coverage. Most users describe helpful, knowledgeable agents, yet several reviews note tickets sitting for days, so responsiveness is solid but not consistently prompt. No clear evidence of regular content update cycles or expert advisory services keeps the score at mid-tier.
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