Nightfall AI vs. Microsoft Purview

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 who 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:

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 Microsoft Purview 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 Microsoft Purview?

Detection platform

Nightfall AI

• Leverages state-of-the-art neural networks and fine-tuned Large Language Models (LLMs) in detecting PII, PCI, PHI, secrets, and IP with high precision and recall, all to minimize noise and 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 without the noise.

Microsoft Purview

• 25% lower precision and 50% lower recall in detecting PII, PCI, PHI, and secrets, 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; also lacks combination logic and GenAI validation, leading to unreliable detection.
• Significant delays in alerting, with critical violations sometimes not detected for days or weeks.

Coverage

Nightfall AI

• Comprehensive coverage by both use case and platform. 
• Supports 5 use cases: Data Detection and Response, Data Security Posture Management, Data Exfiltration Prevention, Data Encryption, and Data Discovery and Classification. Furthermore, Firewall for AI for Developers is available to support custom GenAI applications.  
• Broad DLP coverage of business-critical SaaS apps, including M365 Teams, OneDrive, Slack, Jira, Confluence, Zendesk, Salesforce, GitHub, Google Workspace, and more.
• Unified workflow across SaaS apps, GenAI apps, email, and endpoints. 
• Data encryption with employee opt-in; several robust administrative controls like setting expiration dates and blocking email forwarding.

Microsoft Purview

• Supports M365 ecosystem only. Requires additional subscription to E3/A3/G3 licenses. DLP support is not available with E1/A1/G1 or business premium licenses.
• Use case coverage is limited and does not support exfiltration prevention, data security posture management, or data discovery. 

Ease of use

Nightfall AI

• Intuitive and user-friendly interface.
• Simple policy creation and management.
• Tailored policies by users, user groups, teams, or other dimensions for each app. 
• Quick and easy setup process to integrate and start protecting sensitive data in minutes.
• Clear and actionable alerts via Slack, Teams, email, or any other enterprise system via Webhooks.

Microsoft Purview

• Steep learning curve with complex workflows to manage policies and alerts. 
• Difficult to create flexible policies based on a variety of dimensions.
• No easy alerting mechanism. 
• Cumbersome policy and rule updates take anywhere from 30 minutes to 3 hours or more.
• Complex and time-consuming process for creating exceptions (e.g., for printing or RDP port configs).
• Poorly organized dashboards make it difficult to find information quickly.
• Multiple clicks and window panes required for simple tasks like looking up alerts and incidents.
• Difficult to navigate between different Purview components. Challenging to set up and configure, often requiring expert assistance.

Automated data protection

Nightfall AI

• Flexible remediation options include automated, manual, and employee remediation (with Human Firewall).
• End-user remediation and inline coaching automates incident response and nurtures a secure-by-design culture across your organization.
• Immediate, real-time, or delayed remediation actions are fully customizable by SaaS app, GenAI app, or endpoint.

Microsoft Purview

• Limited automated remediation options.
• Inconsistent application of policies across different Microsoft services.
• Delays in policy enforcement sometimes take hours to apply changes. Default alerts are ineffective, often not working for actionable events.
• Significant delays in alert processing sometimes take days or weeks to generate investigations for critical events like pre-resignation data exfiltration.

API-driven cloud architecture

Nightfall AI

• API-first, enterprise-scale architecture to integrate with SaaS and GenAI apps. 
• No impact to source apps with low-latency, best-in-class detection at petabyte scale.

Microsoft Purview

• Limited API functionality for custom integrations.
• Difficulties in scaling the solution for large enterprises.
• Performance issues when dealing with large volumes of data.

Human-centric approach

Nightfall AI

• No impact on end-user productivity.
• Educational approach helps users to understand how to best use sensitive data. 
• Ability to involve end-users in the remediation process when appropriate.
• Users can report false positives, resulting in improved model fit and reduced noise.

Microsoft Purview

• Lack of user-friendly interfaces for end-users to manage their data.
• Limited options for user feedback on policy effectiveness.
• Potential for over-blocking, impacting user productivity.

Security workflow integration

Nightfall AI

• Seamless integration with leading SIEM and SOAR tools (e.g. Azure Sentinel and Splunk) via Webhooks and REST APIs.

Microsoft Purview

• No webhooks. No APIs to integrate with enterprise security ecosystems. Tightly integrated with Microsoft Sentinel. 
• Limited ability to export data to external SIEM systems, forcing reliance on built-in case management and interfaces.
• While Graph API integration is possible, it's not designed for seamless SIEM integration.
• Restricted control over data flow, limiting the ability to customize security workflows.

TL;DR

Nightfall AI outperforms Microsoft Purview in several key areas, offering superior detection accuracy, comprehensive coverage, ease of use, automated protection, and integration capabilities. While Purview is limited to the Microsoft ecosystem and faces challenges with usability and performance, Nightfall provides a more flexible, user-friendly, and effective solution for modern enterprises.

Looking for an enterprise DLP solution with intuitive workflows, seamless automation, and comprehensive data protection across SaaS, email, endpoints, and more? Nightfall’s got you covered. Click here to schedule a personalized demo.