Nightfall AI vs. Zscaler

Why is it important to secure sensitive data in the modern enterprise?

In today's rapidly evolving digital landscape, securing sensitive data has become imperative for modern enterprises. The stakes are higher than ever due to several key factors:

• Proliferation of sensitive data across platforms: With the widespread adoption of cloud-based apps, sensitive data—including Personally Identifiable Information (PII), Payment Card Industry (PCI) data, Protected Health Information (PHI), secrets, and Intellectual Property (IP)—is all too easily sprawled across multiple platforms. This includes SaaS applications, Generative AI (GenAI) apps, email, and endpoints, all of which significantly expand the potential attack surface.
• Escalating financial risks: The cost of data breaches has skyrocketed. According to IBM, a single leaked secret can cost organizations an average of $4.88 million. This financial impact extends beyond immediate losses to include regulatory fines, legal fees, and long-term reputational damage.
• Regulatory compliance mandates: With stringent data protection regulations such as ISO 27001, HIPAA, and PCI-DSS, organizations face increasing pressure to ensure continuous compliance or risk severe penalties and loss of customer trust. 
• Evolving threats: As cyber threats become more sophisticated, the risk of both external attacks and insider threats has intensified, necessitating robust protection mechanisms.‍
• Trust and reputation: In an era where data breaches make major headlines, it’s paramount to maintain customer trust and corporate reputation. A single data leak can erode years of built-up trust and severely impact an organization's market position.

To address these critical challenges, data security solutions with data leak prevention (DLP) features have emerged as a vital component of enterprise security strategies. An effective data security program should cover data security across multiple dimensions, including the following. 

1. Data detection and response: Proactively identify and prevent the sprawl of sensitive data across communication and collaboration platforms. 
2. Data security posture management: Mitigate risks associated with improper sharing or permission settings.
3. Data exfiltration prevention: Defend your data against both inadvertent leaks and insider threats.
4. Data encryption: Enable secure data sharing through automated, context-aware encryption.‍
5. Data discovery and classification: Scan historical data at rest and automatically remediate sensitive data.

In today’s increasingly complex digital ecosystem, securing sensitive data isn’t an option—it's a necessity. For modern businesses that are aiming to protect their “crown jewels,” maintain compliance, and safeguard their reputation, it’s imperative to implement a robust data security solution that covers SaaS apps, GenAI apps, email, and endpoints.

How do I choose the right data security solution to protect my sensitive data from leakage and exfiltration?

When selecting a data security tool for DLP, several key criteria should guide your decision, including:

1. Detection platform: Evaluate the accuracy and sophistication of the solution's detection capabilities, including its ability to identify sensitive data across various formats and contexts.
2. Comprehensive coverage: Ensure the tool protects data across all relevant platforms, including SaaS apps and GenAI apps as well as email and endpoints. 
3. Ease of use: Consider the solution's user experience and how easily it can be deployed and managed by your team on a daily basis.
4. Automated data protection: Look for features that automate remediation actions to reduce manual workload and improve response times.
5. API-driven cloud architecture: Prioritize solutions built on modern, scalable cloud infrastructures that can seamlessly integrate with your existing platforms.
6. Human-centric approach: Choose a tool that balances robust protection with user productivity in order to minimize false positives and unnecessary friction.
7. Security workflow integration: Assess how well the solution integrates with your existing SIEM/SOAR tools, as well as if it can enable a unified response strategy.‍
8. Total cost of ownership: Consider not just the initial price, but also ongoing costs like maintenance, updates, and potential scalability.

Let's examine how Nightfall AI and Zscaler measure up against these critical factors to help you make an informed decision for your organization's data security needs.

How does Nightfall compare to Zscaler?

Coverage

Nightfall AI

• Offers out-of-the-box detectors for PII, PCI, PHI, secrets, and IP, which can be applied across SaaS apps, GenAI apps, email services, and endpoint devices.
• Supports 50+ file types, including docs, spreadsheets, HTML, PDFs, logs, images, and zip archives.
• Scans all images embedded in docs, PDFs, etc. 
• Leverages columnar information in spreadsheets and CSVs for improved accuracy.

Zscaler

• No support for images, or embedded images.
• No special handling for spreadsheets or CSVs, resulting in increased noise.

Detection

Nightfall AI

• Leverages a detection engine built with GenAI at the core, which means that it’s 2x more precise, and has 4x fewer false positives than the competition. 
• Deploys an advanced image classification model to detect passports, credit cards, social security cards, and other sensitive data in images. 
• Utilizes a sophisticated combination of PII and medical entity detection models, enhanced with GenAI, to accurately detect PHI and minimize noisy alerts.

Zscaler

• Relies on regex-based pattern matching, leading to a much lower precision in detecting sensitive data. 
• No image classification.
• 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 and noisy alerts.

Remediation

Nightfall AI

• Nightfall’s “Human Firewall” feature dispatches custom notifications to inform employees about policy violations and empower them to remediate their policy violations. 

Zscaler

• Manual, admin-only remediation. 

Security workflows

Nightfall AI

• Seamless integration with leading SIEM and SOAR tools (e.g. Azure Sentinel and Splunk) via webhooks and REST APIs.
• Real-time alerting for admins and end users via Slack, Jira, webhooks, and email. 
• Users can report false positives, resulting in improved model fit and reduced noise.

Zscaler

• No alerting options for Jira or webhooks.
• No false positive annotation options.

Ease of use

Nightfall AI

• Nightfall’s human-centric workflows, flexible policy customization, and centralized reporting capabilities enable organizations to proactively identify risk posture, prevent data exposure, improve security hygiene, and maintain continuous compliance.
• Flexible remediation options include automation, manual remediation, and end-user remediation (as part of the “Human Firewall” feature).

Zscaler

• Steep learning curve with complex workflows to manage policies and alerts.

TL;DR: Nightfall AI vs. Zscaler

Nightfall emerges as the more efficient and cost-effective choice for modern enterprises. By choosing Nightfall AI, organizations can enhance their security posture, reduce noise in their alerts, and achieve better overall protection—all while saving valuable resources.

Opt for Nightfall AI to secure your enterprise while minimizing your costs and streamlining your security workflows. Click here to schedule your personalized demo today.