Best Google Workspace DLP Solutions in 2026

Google Workspace powers collaboration for millions of organizations, but that same ease of sharing creates significant data security challenges. Sensitive information flows through Gmail, Google Drive, Chat, and Calendar every day, and traditional security approaches struggle to keep pace. The rise of AI tools like ChatGPT, Gemini, and autonomous AI agents has accelerated data movement beyond what regex and keyword only policies were originally designed to handle. AI now moves data at machine speed, and security teams face a dual challenge: protecting data from both human error and agentic workflows. Choosing the right Google Workspace DLP solution requires understanding which platforms can address modern data movement across SaaS, endpoints, and AI applications. This guide examines seven solutions that serve different organizational needs in 2026, starting with Nightfall AI, the AI data security platform built to control AI agents and all the data they touch.

Key Takeaways

• AI-native detection is the central accuracy question: Platforms built on machine learning and LLM classifiers deliver materially higher precision than context-free rules. Nightfall reports 95% detection precision out of the box, against the 5% to 25% baseline it observes from legacy DLP tooling, and an AI-powered detection platform that cuts false positives by 99%. Regex and keyword only policies generate substantial noise when they lack sufficient context
• Shadow AI protection has become a core evaluation criterion: Data leakage to ChatGPT, Gemini, Claude, and other AI tools is now a standard item on DLP requirement lists, which makes Shadow AI coverage a baseline requirement. Workspace DLP by itself is not general purpose browser DLP for third-party AI sites, although Google can extend DLP to browser activity through the separately licensed Chrome Enterprise Premium
• MCP and AI agent security is an emerging control plane: Agentic workflows require controls designed for agent and tool-call traffic. Several modern data security platforms now provide some level of MCP, agent, or IDE-related protection, including Strac, Metomic, and Cyera, and coverage varies by architecture. Nightfall runs one detection brain across local stdio MCP, IDE-embedded agents, and remote HTTP paths, with full inline blocking rather than alerting alone
• Deployment and time to value vary by architecture and scope: API and cloud-native products can begin producing visibility without lengthy implementation projects. Nightfall connects a first SaaS application in about 10 minutes and reaches full endpoint coverage across macOS and Windows within about a week
• Automated and assisted remediation reduces operational burden: Nightfall reports that 80% of incidents are resolved through a combination of automation and employee self-remediation, which reduces manual triage while maintaining compliance
• Unified platforms consolidate tool sprawl: DLP, insider risk, and AI governance used to mean three contracts. Organizations evaluating coverage across SaaS, endpoints, email, browsers, and AI apps increasingly prefer one control platform and one contract

1. Nightfall AI

Nightfall AI is the AI data security platform that governs data movement across humans and AI agents in real time. The platform provides unified visibility and control across Google Workspace, endpoints, email, browsers, MCP servers, and AI applications, addressing the full spectrum of modern data security challenges. AI moves your data. Nightfall controls it.

How Does Nightfall AI Work?

Nightfall uses AI-native detection powered by supervised fine-tuned models to identify sensitive data with high accuracy. Teams connect their first SaaS application in about 10 minutes, and Nightfall provides native API integrations for Google Drive and Gmail alongside real-time and historical scanning across 13 SaaS applications.

Core Capabilities:

• AI-Native Detection Engine: ML detectors for PII, PHI, secrets, credentials, and financial data plus LLM classifiers across 20+ categories, with Nightfall reporting 95% precision out of the box
• Real-Time Controls: Granular remediation including block, coach, redact, delete, revoke permissions, quarantine, and encrypt, applied through each integration's native capabilities. Gmail supports blocking, quarantining, and encrypting messages, while Google Drive supports permission changes, labels, coaching, and other automated actions
• Shadow AI Protection: Monitor and control data flowing to ChatGPT, Gemini, Claude, Perplexity, DeepSeek, Grok, and other generative AI applications, with browser and endpoint controls that inspect prompts, uploads, and clipboard interactions before submission
• MCP and AI Agent Security: Coverage for local stdio and remote HTTP/SSE MCP workflows, MCP tool-call inspection with risk scoring by what each tool can do (read, read/write, destructive), prompt injection detection on agent traffic, and IDE hooks for Cursor, Claude Code, and VS Code
• Endpoint Coverage: A single lightweight agent covering human and AI/MCP traffic across 10+ vectors on macOS and Windows, using approximately 1% CPU and about 50 MB of RAM

Documented Results

Nightfall's enterprise deployments show consistent, quantifiable outcomes:

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• Nightfall's Snyk case study reports a 94% true positive rate measured from March to September 2024, and Victor Sogaolu, Staff Security Engineer, summarizes the difference: "When it says there's a detection, we trust that detection."
• Unit21 notes: "We want to allow our folks to use the power of generative AI but in a safe and approved way."
• Nightfall's ROI calculator assumes an 85% reduction in manual investigation time through AI-based detection, investigation, and response, and its pricing FAQ states that AI-based detection cuts manual alert investigation by about 85%
• Organizations generally see 6x ROI within the first 90 days of deployment

What Makes Nightfall AI Unique

• LLM-Based File Classification: Pre-trained models identify sensitive document types based on structure, layout, and semantic meaning, detecting financial statements, source code and engineering artifacts, HR records, contracts and NDAs, product roadmaps, and tax or audit records that pattern matching misses
• AI-Based Data Lineage: Nightfall's lineage is intentional. AI-native detection decides what is risky first, then data lineage shows the trail on what matters, from source to destination, regardless of content transformation through downloads, renames, compression, or uploads to personal cloud storage
• Nyx Autonomous DLP Analyst: An agentic DLP analyst that sees, reasons, and acts on DLP incidents, further reducing manual investigation workload
• One Detection Brain, Every Surface: One policy across endpoint, SaaS, email, browser, and AI agents, so a single detection engine operates across every surface rather than requiring separate tools for each, with posture and discovery delivered as a byproduct of prevention

Best For: Organizations seeking AI-native data security that covers Google Workspace alongside endpoints, Shadow AI, and AI agent workflows with real-time controls and automated remediation.

2. Google Workspace DLP (Native)

Google Workspace includes built-in data protection, and what an organization receives depends on its edition. Google's current DLP documentation lists Frontline Standard, Frontline Plus, Enterprise Standard, Enterprise Plus, Education Fundamentals, Education Standard, Education Plus, and Enterprise Essentials Plus as the editions supported for full Workspace DLP rules, and Google's pricing page places data loss prevention under its Enterprise tiers. Business Plus customers instead receive Security Advisor for data protection, which protects Gmail and Drive with a subset of predefined content detectors and offers a narrower rule creation and customization experience than the one available to Frontline Plus and Enterprise customers.

Key Features

• Native Integration: Policy enforcement inside Drive, Gmail, Chat, and Calendar with no deployment steps. DLP for Calendar scans event titles, descriptions, and locations when events are saved, and can block, warn, or audit
• Layered Detection Conditions: Predefined content detectors, custom regular expressions, word lists, adjustable detection confidence thresholds, proximity matching, nested conditions, metadata conditions, and classification labels. Custom content detectors let admins build rules for organization-specific data patterns
• AI Classification for Drive: Eligible customers can use organization-specific machine learning models and a Gemini-based beta method that applies LLMs and natural-language instructions to classify Drive content. Availability varies by edition and add-on
• Included Pricing: No additional cost for organizations already on eligible Workspace editions

Capabilities and Scope

Google Workspace DLP operates within the Google ecosystem, applying policies to content created, shared, and stored in native Workspace applications. Rules can block sharing, warn users, or audit activity, and violations can be investigated through Google's security tooling.

Coverage Includes:

• Gmail message content and attachments
• Google Drive files and sharing permissions
• Google Chat messages
• Google Calendar event titles, descriptions, and locations
• Google Meet recordings and transcripts are saved to the meeting organizer's Google Drive, which means Drive protections can subsequently apply to those stored artifacts. Google does not document a standalone Meet DLP layer that inspects live meeting audio or video

Considerations

• Workspace-Scoped Enforcement: Native Workspace DLP applies to Google's own applications. Slack, Microsoft 365, Salesforce, and other third-party SaaS platforms are not native Workspace DLP applications
• Browser and AI Coverage Requires Additional Licensing: Workspace DLP alone is not general purpose browser DLP for third-party AI websites. Google can extend DLP controls to browser activity through the separately licensed Chrome Enterprise Premium, which can monitor uploads, downloads, pastes, printing, and URLs and enforce Block, Warn, or Audit actions. Google also ships templates for auditing visits to generative AI sites, blocking paste operations on generative AI sites, and blocking visits to those sites. Managed Gemini experiences also receive Google Workspace enterprise data protections
• No Endpoint DLP Agent: Workspace DLP applies inside Google's applications rather than providing device-level controls for local file operations, removable media, or printing
• Edition-Dependent Capability: Capability varies by Workspace edition, with Business Plus Security Advisor providing a narrower feature set than full Enterprise DLP rules

Native controls are often the default rather than a deliberate choice, and as AI moves data autonomously, the gap they leave is an active exposure rather than passive risk. Nightfall extends the same detection engine beyond a single ecosystem, covering Google Workspace alongside endpoints and browsers, email, other SaaS applications, and AI agent workflows from one control plane.

Best For: Organizations standardized on Google Workspace that need baseline DLP inside Google's own applications, with the option to license Chrome Enterprise Premium for browser-level controls.

3. Strac

Strac combines agentless, API-based data security posture management (DSPM) and DLP for SaaS and cloud data with endpoint DLP delivered through software installed on Windows and macOS endpoints. Strac describes the two architectures as complementary rather than interchangeable.

Core Capabilities

• Hybrid Architecture: API-based agentless DLP for SaaS and cloud data, plus an endpoint agent for device-level coverage
• Broad Integration Footprint: Strac advertises a broad set of integrations spanning SaaS, cloud, endpoint, and GenAI surfaces
• ML and OCR Classification: Machine learning and optical character recognition for sensitive data detection in documents and images
• Remediation Actions: Redaction, masking, tokenization, and deletion capabilities for sensitive data
• MCP DLP Support: Coverage for AI agent tool calls and responses through Model Context Protocol integration, including Claude Code inspection and enforcement
• Browser-Level AI Protection: Monitoring data sent to generative AI applications

Deployment and Integration

Strac supports deployment through agentless API connections for SaaS and cloud sources, enabling organizations to gain visibility into Google Workspace and other SaaS applications, while endpoint coverage is added through the installed agent where device-level control is required.

Integration Scope:

• Google Drive and Gmail
• Slack, GitHub, Salesforce
• Cloud storage services
• Browser-based AI tools
• Windows and macOS endpoints via agent

Buyers comparing this category on architecture will find that Nightfall runs one detection brain across SaaS, endpoint, browser, email, and agentic surfaces, with AI-native detection and full inline blocking native to the platform and included in every tier rather than packaged as a separate line item.

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Best For: Organizations that want agentless API coverage for SaaS and cloud data with the option to add endpoint agents, alongside GenAI and MCP-related controls.

4. Varonis

Varonis delivers a data security platform with access governance and behavioral analytics capabilities, supporting hybrid on-premises and cloud environments, including agentless Google Workspace coverage.

Platform Strengths

• Data Access Governance: Permissions analytics and access monitoring across file systems and cloud storage
• Behavioral Analytics: Anomaly detection based on user behavior patterns
• Insider Threat Detection: Identification of suspicious activity indicating potential data exfiltration
• Hybrid Environment Support: Coverage for both on-premises file servers and cloud storage including Google Drive
• Automated Remediation: Varonis advertises automatically revoking risky access, continuously reducing excessive permissions, and auto-fixing certain security settings, with continuous automated policy enforcement across its platform

Enterprise Focus

Varonis positions itself for large enterprises requiring comprehensive data governance with detailed visibility into who has access to what data and how permissions have changed over time.

Key Considerations:

• Deployment Model: Varonis states that its cloud-native Data Security Platform supports agentless connection to data sources, with visibility and protection beginning once a source is connected. Broad hybrid implementations still involve policy design, migrations, and change management, which is a function of scope rather than a product-level requirement
• Pricing Model: Pricing varies by product, SKU, and environment, with some offerings priced by data volume and others exposing a per-user dimension
• Enforcement Model: Varonis is centered heavily on access, permissions, configuration, and connected DLP controls rather than every inline content-movement channel

Access governance and permissions analytics remain valuable, and Nightfall complements them by controlling the data movement itself in real time across SaaS, endpoints, browsers, email, and agent workflows. Data discovery and classification and the ability to revoke inappropriate data sharing arrive as a byproduct of prevention rather than as a prerequisite for it.

Best For: Large enterprises requiring deep access governance, behavioral analytics, and automated exposure reduction across hybrid on-premises and cloud environments.

5. Cyera

Cyera began as a cloud-native data security posture management platform and has expanded by 2026 into DLP orchestration and AI and browser enforcement.

Core Capabilities

• Agentless Cloud Discovery: Scanning across AWS, Azure, and GCP without agents
• AI-Powered Classification: Machine learning classification for sensitive data across supported sources
• Broad Source Coverage: Cyera classification coverage now spans AWS, Azure, GCP, Snowflake, Databricks, Microsoft 365, Google Workspace, Salesforce, ServiceNow, and on-premises file systems. Its June 2026 Access Trail for Google Workspace release added deeper audit visibility across My Drive and Shared Drives with classification context and optional access revocation
• Data Risk Assessment: Visibility into where sensitive data resides and how it flows
• AI Runtime Protection: Monitoring, alerting, and blocking combining Omni DLP, Browser Shield, and AI Firewall
• Browser Shield: An endpoint-resident browser extension deployable through MDM across Chrome, Edge, Opera, and Brave on Windows and macOS, which inspects and blocks risky AI prompts before submission

Architecture Notes

Cyera's expansion into enforcement is layered on top of its discovery and classification foundation, so understanding where Cyera acts directly and where it orchestrates existing tools matters when scoping a Google Workspace deployment.

Key Considerations:

• Orchestration Layer: Cyera states that Omni DLP does not replace existing enforcement points. It sits above existing DLP controls to provide centralized decisioning, prioritization, and policy orchestration
• Endpoint Scope: Browser Shield provides endpoint-resident browser controls, and Cyera positions Omni DLP as an orchestration layer rather than a full-spectrum endpoint DLP agent controlling every USB copy, local file operation, or print event
• Cloud Heritage with Expanding SaaS Coverage: Cyera retains multicloud DSPM capabilities and now also spans major SaaS environments, Google Workspace, and on-premises data

DSPM still has relevance for enterprises, and prevention does not require posture as a prerequisite. Today's data is no longer static, so AI agents call for runtime governance. Nightfall starts preventing on day one and delivers real data discovery as a byproduct, which means an existing DSPM investment can stay in place while data exfiltration prevention begins immediately.

Best For: Organizations that want multicloud and SaaS data discovery with DLP orchestration layered over existing enforcement points, plus browser and AI runtime controls.

6. Spin.AI (SpinOne)

Spin.AI offers a unified security platform combining backup, ransomware protection, DLP, and SaaS security posture management.

Unified SaaS Security

• Backup and Recovery: Automated backup with AI ransomware recovery for Google Workspace data
• DLP Capabilities: SpinOne's current materials describe a multilayer AI/ML engine incorporating private LLMs, natural-language processing, named-entity recognition, and neural classifiers, with detection across a broad set of sensitive-data types, alongside predefined detectors and custom regex
• OAuth Risk Monitoring: Third-party application risk assessment and access control
• SSPM Features: Security posture management for SaaS configurations

Coverage Approach

SpinOne is well established for Google Workspace and Microsoft 365 backup and SaaS security, and its coverage is no longer limited to those two ecosystems. Its current listing includes Google Workspace, Microsoft 365, Salesforce, Slack, Jira, Jira Service Management, Jira Product Discovery, Assets, and Confluence, with DLP scanning also described for Dropbox, Box, and GitHub.

Key Considerations:

• Combined Backup and Security: Organizations seeking both backup and DLP benefit from the unified platform
• AI/ML Plus Pattern Detection: SpinOne supports predefined and pattern-based detection alongside AI/ML-based classification
• Deployment: SpinOne's marketplace listing describes cloud-native deployment

Where backup and posture management protect SaaS availability and configuration, Nightfall consolidates DLP, insider risk, and AI governance into one platform and one contract, extending the same detection engine to endpoints, browsers, email, and secure AI usage across agentic workflows.

Best For: Organizations seeking combined backup, ransomware recovery, SSPM, and DLP for Google Workspace and a growing set of other SaaS platforms.

7. Metomic

Metomic provides SaaS-wide DLP with emphasis on contextual detection and self-remediation workflows that empower end users, and has expanded into browser and AI agent controls.

Key Features

• SaaS Visibility: Coverage across Google Workspace, Slack, Jira, Confluence, Notion, Microsoft 365, and other SaaS platforms
• Contextual Controls: Behavior-aware detection that considers usage patterns
• Self-Remediation: Workflows enabling end users to resolve violations directly
• MCP Gateway: A dedicated gateway that sits between AI agents and tools, databases, and models, inspects requests, and can allow, redact, hold, or block them, with Claude and Cursor shown as governed MCP clients
• AI-Path Classification: Metomic states that its data-classification engine now operates in AI-agent request paths and in the browser
• Customizable Policies: Configurable rules engine for organization-specific requirements

User-Centric Approach

Metomic emphasizes enabling business productivity while maintaining security, with self-remediation capabilities that reduce security team workload by empowering users to fix issues themselves.

Key Considerations:

• Endpoint Scope: Metomic focuses on SaaS, browser, and gateway-level coverage rather than full-spectrum endpoint DLP for every local device, USB, or print use case, and it provides a browser extension for AI visibility and control, while its MCP architecture is designed to require no endpoint agent
• Pricing: Metomic's public marketplace listing shows contract pricing with unit-based overages

A gateway is one layer of the agentic picture, and it proxies remote MCP traffic. Nightfall covers remote MCP as well, and because its agent also runs on the device, it sees the local stdio server, the Cursor or Claude Code session, and the file an agent just touched, classifying and enforcing on the content moving through each of those paths. That combination is what turns MCP security from a feature into a platform, and it is why AI agent security is best evaluated across the full surface rather than one slice of it.

Best For: Organizations emphasizing user-friendly security with self-remediation across multiple SaaS applications, plus MCP-level agent controls.

Why Nightfall AI Stands Out for Google Workspace DLP

AI-Native Detection That Reduces Alert Fatigue

DLP policies built only on regex, keywords, and context-free rules generate substantial false-positive noise, forcing security teams to chase irrelevant alerts. Nightfall's AI-native detection engine reports 95% precision and cuts false positives by 99%. That accuracy comes from transformer-based ML detectors trained on labeled sensitive data rather than regular expressions or keyword lists, plus LLM classifiers that evaluate document structure and semantic meaning. Victor Sogaolu, Staff Security Engineer at Snyk, captures the difference: "When it says there's a detection, we trust that detection."

Coverage That Follows Data Across Human and Agent Workflows

AI has changed who moves data. Employees use ChatGPT to draft emails. Developers rely on Cursor and Claude Code for assistance. AI agents execute autonomous workflows through MCP servers. The same employee runs a local MCP server in Cursor, fires prompts at a remote LLM, and pulls a file off the endpoint, and single-surface tools do not see that crossover. Native Google Workspace DLP applies within Google's own applications: Google can extend DLP to browser activity through the separately licensed Chrome Enterprise Premium, and managed Gemini experiences receive Workspace enterprise data protections, while MCP and IDE-level control over agent tool calls sits outside that scope. Nightfall provides Shadow AI protection across ChatGPT, Gemini, Claude, Perplexity, and other generative AI applications, and its MCP security capability extends to local stdio and remote HTTP/SSE MCP workflows with hooks for Cursor, Claude Code, and VS Code. Nightfall is the only platform that controls data movement in real time across endpoints, MCP servers, email, browsers, and SaaS, with one detection brain running on every one of them.

Real-Time Control, Not Just Visibility

Visibility without control is just a dashboard. Seeing the leak is not the win. Stopping it is. Nightfall goes beyond detection to provide enforcement actions including block, coach, redact, delete, revoke permissions, quarantine, and encrypt, applied through each integration's native capabilities. Nightfall reports that 80% of incidents are resolved through a combination of automated remediation and employee self-remediation, which means four in five incidents are resolved via automation or by employees themselves. Every incident ships with a full forensic story: who, role, lineage, and prior behavior, backed by forensic search and app intelligence.

Fast Time to Value

Nightfall connects a first SaaS application in about 10 minutes, completes SaaS integrations in under an hour, and starts delivering value immediately. For endpoints, Nightfall cites roughly 10 minutes for deploying the agent to hundreds of users and about 30 minutes for MDM-based distribution, with full endpoint coverage across macOS and Windows reached within about a week.

Unified Platform for Consolidated Security

Managing separate tools for DLP, insider risk, and AI governance creates operational complexity and visibility gaps, and enterprises increasingly want one vendor. Nightfall consolidates these capabilities into one control platform with one policy across endpoint, SaaS, email, browser, and AI agents, and reports that customers consolidate three to five security solutions into the platform. See how Nightfall compares across the wider data security market.

Enterprise Adoption and Reported Economics

Hundreds of organizations run on Nightfall, including Sierra AI, Legora, Mercado Libre, Nubank, Rackspace, and DraftKings, with customer stories published on its site. On economics, Nightfall publishes 20x average ROI, with organizations generally seeing 6x ROI within the first 90 days, a result buyers can model in its pricing calculator. Nightfall's pricing FAQ reports that customers see roughly 50x lower total cost of ownership than legacy DLP suites. The driver behind those figures is the same: deployment speed, detection accuracy, and automated plus employee-assisted remediation translate directly into operational efficiency and risk reduction.

For security teams evaluating Google Workspace DLP solutions, Nightfall delivers the AI-native capabilities required for modern data protection. Explore how Nightfall can secure your Google Workspace environment by requesting a demo.

Frequently Asked Questions

What is the difference between traditional DLP and AI-native data security for Google Workspace?

Traditional DLP relies on pattern matching, regular expressions, and keyword detection to identify sensitive data. Rules of that kind generate substantial false-positive noise when they lack sufficient context, and tuning them is an ongoing operational cost. AI-native platforms like Nightfall use machine learning detectors and LLM classifiers trained on labeled data to understand context, and Nightfall reports 95% precision out of the box against the 5% to 25% baseline it observes from legacy DLP tooling. It is also worth noting that Google itself has moved beyond pure pattern matching: Workspace DLP supports predefined detectors, confidence thresholds, proximity matching, nested and metadata conditions, and classification labels, and eligible customers can use AI Classification for Drive. The distinction that matters in 2026 is whether detection reasons about content and context across every surface where data exfiltration can occur.

Can Google Workspace's built-in DLP protect against Shadow AI and AI agent data exfiltration?

Not on its own. Native Workspace DLP monitors data within Google's own applications, currently Drive, Gmail, Chat, and Calendar, rather than acting as general purpose browser DLP for third-party AI websites. Google does offer adjacent controls: the separately licensed Chrome Enterprise Premium can apply DLP to browser uploads, downloads, pastes, printing, and URLs, including templates for auditing or blocking generative AI sites, and managed Gemini experiences receive Workspace enterprise data protections. MCP and IDE-level visibility into AI agent workflows sits outside that scope. Organizations running agentic workflows therefore add solutions such as Nightfall's Shadow AI protection for ChatGPT, Gemini, Claude, Perplexity, and other generative AI applications, plus its MCP security for local stdio, remote HTTP/SSE, and IDE-embedded agent traffic.

What are the key considerations when choosing a DLP solution for Google Workspace in 2026?

Security teams should evaluate detection approach (AI/ML classifiers versus rules-only policies), coverage scope (Workspace-only versus SaaS plus endpoints plus browsers plus email plus AI tools), remediation capabilities and which actions are available on which integrations, licensing and edition requirements including whether browser coverage requires an additional Google license, time to value measured on a like-for-like basis, and total cost of ownership including operational burden. Because AI agents now move data autonomously at machine speed, AI agent security and Shadow AI protection have become primary evaluation criteria rather than optional extras, and a single platform that covers both human and agentic data movement reduces the number of tools and contracts required.

How does Nightfall ensure a low false-positive rate compared to other DLP tools?

Nightfall's detection engine uses transformer-based, supervised fine-tuned ML models trained on labeled sensitive data rather than relying on regex patterns and keyword lists. The platform includes LLM classifiers that evaluate document structure and semantic meaning, identifying sensitive content based on context. Nightfall reports 95% precision out of the box and a 99% reduction in false positives, with customer-trainable and auto-retraining capabilities that improve accuracy over time for organization-specific data types.

What kind of remediation actions can Nightfall take on sensitive data in Google Workspace?

Across supported integrations, Nightfall offers block, coach, redact, delete, revoke permissions, quarantine, and encrypt, applied through each integration's native capabilities. For Google Workspace specifically, Gmail supports blocking, quarantining, and encrypting messages, while Google Drive supports permission changes, labels, coaching, and other automated actions. Security teams can configure automated responses or require manual approval based on risk level, and the platform supports end-user self-remediation with coaching workflows that educate employees while resolving violations. For a deeper walkthrough, see Nightfall's Google Drive DLP guide.