Nightfall AI vs. Google Cloud DLP

What’s the role of DLP in the modern enterprise?

In today’s cloud-based work environments, it’s all too easy for assets with sensitive data like PII, PCI, PHI, secrets, and intellectual property (IP) to be sprawled across the enterprise tech stack. With the skyrocketing costs of data breaches, one sprawled secret can cost organizations an average of $4.45 million. This is where Data Leak Prevention (DLP) solutions come in to limit secret sprawl, prevent data leaks, and ensure continuous compliance with leading standards.

However, not all DLP solutions are created equal. An effective DLP solution should cover multiple aspects of data security and address key challenges related to:

1. Data Detection and Response: Detect and prevent the sprawl of sensitive data like PII, PCI, PHI, secrets, and IP.
2. Data Security Posture Management: Identify inadvertent or malicious exposure of IP via risky sharing or permission settings. 
3. Data Exfiltration Prevention: Detect insider threats and stop exfiltration events. 
4. Data Encryption: Share sensitive data securely using context-aware data encryption.‍
5. Data Discovery and Classification: Discover and classify all historical data at rest with automated remediation.

In other words, it’s important to have a DLP program that provides comprehensive data protection for business-critical SaaS apps as well as for GenAI apps, email, and endpoints.

What’s the difference between legacy DLP and AI-native DLP?

To maximize your DLP program’s effectiveness, it’s also important to compare the capabilities of a legacy DLP solution versus an AI-native DLP solution. 

Legacy DLP solutions are built using regexes and low-fidelity machine learning (ML) approaches, which lead to gaps in detection, as well as to more cumbersome workflows for security teams and non-security employees alike.

On the other hand, AI-native DLP leverages advanced ML and Large Language Models (LLMs) for cutting-edge detection, seamlessly automated workflows, and lower signal-to-noise ratios. For solutions like Nightfall, this means a 4x decrease in false positive alerts, as well as 4x savings in operational costs—not including additional time savings from automation.

Choosing the right DLP solution is one of the most important tasks that any security team will undertake. By investing in AI-native DLP, you’re investing in improving your security posture—as well as your security workflows—over time.

How do I choose the right DLP solution for me?

Though every organization’s needs may be different, there are several criteria that are important to consider while selecting a DLP tool: Breadth of coverage, accuracy of detection, ease of use, and cost of ownership. Let’s dive into each of these categories for both Nightfall AI and Google Cloud DLP. 

Coverage

Nightfall AI

• Supports 5 use cases: Data Detection and Response, Data Security Posture Management, Data Exfiltration Prevention, Data Encryption, and Data Discovery and Classification
• Secures the entire Google Workspace environment, from Gmail to Google Drive
• Broad DLP coverage of business-critical SaaS applications, including Slack, Jira, Confluence, Zendesk, Salesforce, GitHub, and more
• Unified workflow across SaaS apps and email
• Data encryption with employee opt-in; several robust administrative controls like setting expiration dates and blocking email forwarding

Google Cloud DLP

• Supports data detection and response, but not additional use cases such as data exfiltration prevention, data encryption, etc.
• Disjointed, unintuitive DLP capabilities
• Protects Google Workspace only
• Separate workflows for Google Drive and Gmail
• Encryption with no employee opt-in; lack of administrative controls in Gmail

Summary

Nightfall and Google Cloud DLP offer distinct capabilities in data coverage, each tailored to different aspects of modern enterprise needs. Nightfall stands out with its comprehensive scanning across more than 50 file types, including .zip archives, as well as its ability to perform OCR and image classification. This breadth of coverage ensures thorough detection of sensitive data like PII, PCI, PHI, and IP across diverse data formats and sources. Nightfall’s integration with SaaS and GenAI apps further enhances this coverage, facilitating real-time alerting and automated incident response.

In contrast, Google Cloud DLP primarily focuses on scanning and protecting data within its ecosystem, such as BigQuery, Cloud Storage, and Cloud SQL. While robust within its domain, Google Cloud DLP lacks native integrations for broader application coverage beyond Google Cloud services. It also does not scan .zip archives or classify images, thereby limiting its effectiveness in detecting sensitive data comprehensively across diverse cloud environments and apps outside the Google ecosystem.

Organizations seeking extensive coverage across various file types, as well as support across a variety of use cases, may find Nightfall more suitable for addressing their data protection needs compared to the more confined scope of Google Cloud DLP.

Detection

Nightfall AI

• Leverages state-of-the-art neural networks and fine-tuned LLMs in detecting PII, PCI, PHI, passwords, API keys, and IP with high precision and recall, ensuring minimal noise and fewer missed detections
• Detects sensitive data in 50+ file types, including docs, images, spreadsheets, HTML, PDFs, logs, images, and .zip archives
• Leverages columnar information in spreadsheets and CSVs for improved accuracy
• Utilizes a sophisticated combination of PII and medical entity detection models, enhanced with GenAI, to accurately detect PHI and minimize noisy alerts

Google Cloud DLP

• 33% lower precision and 60% lower recall in detecting PII, PCI, PHI, and credentials, leading to an overwhelming number of false positives and missed detections
• No support for embedded images or .zip archives 
• No special handling for spreadsheets or CSVs, resulting in increased noise
• No dedicated PHI detector; provides a limited set of standalone PII and health entity detectors, and lacks combination logic and GenAI validation, leading to unreliable detection

Summary

Nightfall’s state-of-the-art AI detection is powered by neural networks and fine-tuned LLMs to help customers pinpoint sensitive data with 2x greater precision than legacy DLP solutions. This leads to a 4x reduction in false positive alerts, which translates to better operational efficiency and fewer unnecessary workflow interruptions.

In contrast, Google Cloud DLP exhibits a 33% lower precision in detecting sensitive information like PII, PCI, PHI, and credentials, which translates to a higher incidence of false positives. Moreover, Google Cloud DLP’s 60% lower recall means more missed detections, which compromises overall security.

Ease of use

Nightfall AI

• Flexible remediation options include automated, manual, and employee remediation (with Human Firewall)
• Human Firewall feature offers employee self-remediation options as well as coaching to automate incident response and maintain a secure-by-design culture
• Seamless integration with leading SIEM and SOAR tools (e.g. Azure Sentinel and Splunk) via webhooks and REST APIs
• Customizable, real-time alerting for admins and employees via Slack, Jira, webhooks, and email
• Option for employees report false positives and provide business justifications for sharing sensitive data, leading to improved model fit and reduced noise

Google Cloud DLP

• Manual admin-only remediation 
• No employee self-remediation options
• No webhooks or APIs to automate DLP incident triage and response
• No flexibility to send alerts to non-Google Workspace platforms
• No option for employees to report false positives or business justifications

Summary

Nightfall offers seamless integration with a wide range of SaaS and GenAI apps such as Slack, Salesforce, GitHub, and Google Drive, facilitating real-time alerting and automated incident response. Furthermore, its REST APIs can integrate with leading SIEM or SOAR tools like Azure Sentinel and Splunk. Nightfall also supports AI-driven employee coaching, which enhances employee awareness and adherence to data security policies.

While Google Cloud DLP provides robust data detection capabilities within its ecosystem (e.g. BigQuery, Cloud Storage), it lacks native integrations and API support for ingesting violations into SIEM/SOAR systems. This limitation hinders the automation of incident response and restricts its utility across diverse application environments and platforms outside Google Cloud services.

Total cost of ownership

Nightfall AI

• Effortless setup, intuitive triage, seamless remediation, and comprehensive reporting
• Customizable detectors, detection rules, and policies
• Intelligent policy engine with the ability to define policies according to users, user groups, drives, file permissions, and more

Google Cloud DLP

• Steep learning curve
• Lack of customizable policy options 
• Limited support for different types of detectors

Summary

Nightfall distinguishes itself with user-friendly workflows, customizable policies, and centralized reporting capabilities that simplify security management and enhance operational visibility. Its intuitive interface and robust help documentation support efficient deployment and ongoing maintenance of data protection strategies.

Google Cloud DLP presents a steeper learning curve with a complex setup and management processes, particularly in customizing workflows and handling alerts across disparate apps and environments. This complexity may challenge organizations that are seeking straightforward DLP deployment.

TL;DR: Nightfall vs. Google Cloud DLP

The choice between Nightfall and Google Cloud DLP hinges on organizational priorities, ranging from detection to automation to user experience. Nightfall emerges as a comprehensive solution with superior accuracy, extensive integration options, and advanced remediation features that cater to modern security challenges across diverse cloud environments. In contrast, while Google Cloud DLP offers powerful data detection capabilities within its ecosystem, its limitations in integrations, remediation options, and user experiences may pose challenges for organizations that require broader and more automated data protection solutions.

See why companies choose Nightfall

Ready to experience the ultimate in AI-native DLP? Contact our team today to schedule a personalized demo.