Nightfall AI vs.
Legacy DLP
Nightfall’s generative AI (GenAI) platform provides a more precise, efficient, and scalable solution for the enterprise.
Compare Nightfall to legacy DLP
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Legacy DLP never worked
Disparate solutions
Legacy DLP requires a patchwork of point solutions and techniques to monitor different parts of the enterprise stack.
Noisy alerts
Built on rules and heuristics, legacy DLP solutions burden security teams with a high volume of false positive alerts.
Spotty security
When security teams are overwhelmed by alerts, it’s more likely for high-risk true positives to slip through the cracks—putting security and compliance at risk.
Blunt remediation
By blocking web traffic, legacy DLP creates a rift between security teams and employees.
How does Nightfall compare to legacy DLP?
Level up your DLP program with enhanced detection, automated workflows, and faster time to remediation.
Features
Nightfall AI
Legacy DLP
Installation
Nightfall AI
Takes minutes to install and create custom policies
Legacy DLP
Takes weeks or months to configure policies
Coverage
Nightfall AI
Provides high visibility and granular remediation options across SaaS apps, GenAI tools, browsers, email services, endpoints, custom apps, and LLMs, all from a unified platform
Legacy DLP
Requires a patchwork of point solutions and DLP techniques to monitor different parts of the enterprise stack
Detection
Nightfall AI
Pinpoints sensitive data with 2x greater precision than legacy DLP, leading to 4x fewer false positive alerts
Legacy DLP
Has a high volume of false positive alerts
Impact
Nightfall AI
Enhanced precision translates to fewer workflow interruptions
Legacy DLP
More false positives means employees experience frequent workflow interruptions
Total cost of ownership (TCO)
Nightfall AI
4x lower operational costs, not including additional time savings from automation
Legacy DLP
Limited opportunities for automation due to low precision and high volume of false positive alerts
Workflows
Nightfall AI
Automated remediation and “Human Firewall” feature educates employees about security policies and empowers them to remediate their own policy violations, creating a stronger culture of security
Legacy DLP
Slower time to remediation results in data leaks, privilege escalation attacks, and noncompliance
Scalability
Nightfall AI
Can adapt over time with minimal fine-tuning
Legacy DLP
Rules-based approaches require frequent manual updates
Security Posture
How an organization manages security; Mature organizations prefer to be further to the left: Proactive ←→ Passive ←→ Reactive
Features
Pros
Cons
Cloud DLP
Shift-left security that detects and protects data at the source, before proliferation and wide disclosure
Does not block data movement.
Network DLP
Inline (in the middle) scanning all web traffic
Compromised end-user privacy as all data is decrypted and scanned, much of which should remain private (e.g. credentials, private keys)
Endpoint DLP
Last line of defense (command and control)
Compromised end-user privacy for anything that should remain private that is on disk (e.g., private keys)
Depth of insight
The data that can be monitored and protected
Features
Pros
Cons
Cloud DLP
Real-time and historical data insight with context and granular remediation actions
Limited by cloud provider functionality / API limitations (e.g. download activity data)
Network DLP
Real-time of data in transit on the network
Only data in transit, no insight of historical data
Endpoint DLP
Monitoring at the endpoint for file activity from downloads to removable media
Limited to what is saved on endpoint disk
Scalability
Ability to protect data without greater consumption of resources
Features
Pros
Cons
Cloud DLP
Powerful data protection that does not depend on the availability of a device (computer / network) or resources (CPU, memory and bandwidth)
Limited to specific SaaS / Cloud apps
Network DLP
Not limited by endpoint resources
Singular point of failure and bottleneck on the network; adds latency and availability of network and bandwidth
Endpoint DLP
Covers managed endpoints
Limited interoperability with endpoint OS / updates / other agents and highly dependent on endpoint resources (CPU, memory, bandwidth)
Coverage
The users and methods that are accessing data that require protection.
Features
Pros
Cons
Cloud DLP
All users and system accounts, APIs and BYODs
Does not cover shadow IT
Network DLP
User and system accounts and sometimes BYOD
API connection data movement is not monitored
Endpoint DLP
All managed endpoints
No coverage of BYOD, API, or third party unmanaged devices
Impact
the user experience
Features
Pros
Cons
Cloud DLP
Business-enabling with no blocking of end-users
Does not block users
Network DLP
Can block users and monitor shadow IT beyond sanctioned apps
Offers blocking only and may break applications with network filtering
Endpoint DLP
Can block user actions
Can block users and will require security intervention.
Accuracy
competency in finding data that requires protection
Features
Pros
Cons
Cloud DLP
Continuously improving high accuracy from machine learning
Network DLP
Regular express and rules-based
Limited complexity due to network requirements for low latency and responsiveness
Endpoint DLP
Regular express and rules-based
Limited complexity due to low latency requirements (CPU, memory) on endpoint
Total Cost of Ownership (TCO)
time and money spent on installation, configuration, and maintenance.
Features
Pros
Cons
Cloud DLP
Real-time coverage in minutes - no agent, device, or network installation or configuration
1-2 weeks of tuning for appropriate insight
Network DLP
Deployment and maintenance is highly expensive, complex and resource intensive
Endpoint DLP
Deployment and maintenance is expensive, complex and resource intensive; re-deployment is required for every new endpoint
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Not all DLP is created equal
Your go-to cloud DLP solution should leverage AI to cut through the noise, reduce security team workloads, and improve your security posture.
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