Enterprise Security for AI Tools
Secure AI agents without slowing down innovation
Nightfall delivers enterprise-grade security for Model Context Protocol (MCP) and AI agentic workflows. Gain complete visibility into agent activity, enforce granular access controls, and stop sensitive data exposure - all without disrupting developer productivity.
The Problem
The invisible security gap in your AI transformation
Your organization has embraced AI agents for productivity gains - Cursor for coding, Claude for analysis, ChatGPT for research. But these agents now access your most sensitive systems through 18,000+ unmanaged MCP servers, creating security blind spots that traditional DLP tools can't address.
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Security teams cannot inventory which MCP servers employees are using
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No insight into what corporate data agents are accessing
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Audit logs don't capture programmatic AI data retrieval
"Observability is an epic fail. We only discover MCPs through daily Slack self-confessions."
Head of Security, Legal Tech Company
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MCP servers can be maliciously updated overnight (proven by recent incidents)
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No detection of "rug pull" attacks on trusted tools
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Dependencies change without security review
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Version drift across teams creates unknown exposure
Employees use personal ChatGPT, Copilot accounts when enterprise licenses aren't available
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Corporate data flows to consumer AI services via browser plugins and desktop apps
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Personal Google Drive used as staging area for AI workflows
Customer quote
"We need to enforce that only ChatGPT Enterprise can access our corporate Google Drive - technical enforcement, not just policy."
The Solution
Three-layer defense for agentic security
Nightfall provides the only comprehensive security platform purpose-built for AI agents and MCP workflows. Deploy in hours, not months - no policy tuning required.
Know every AI agent in your organization
Automatically discover and catalog all MCP servers across Claude Desktop, Cursor, VS Code, and custom integrations. Map which employees use which agents, what systems they access, and track usage patterns over time.
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Real-time configuration scanning (detects new MCPs in 60 seconds)
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User and device attribution for full accountability
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Shadow AI detection - flag unapproved tools instantly
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Export audit-ready reports for compliance teams
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Detect risky agent behavior before data leaves
Monitor every MCP tool call in real-time. Nightfall's AI-powered analysis identifies sensitive data in agent prompts, file uploads, API calls, and responses - with 95% accuracy and near-zero false positives.
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Content inspection at the protocol level:
Intercept data before agents process it
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Supply chain monitoring:
Alert on MCP version changes
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Behavioral anomaly detection:
Flag unusual data access patterns (e.g. developer suddenly querying customer database)
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Semantic analysis beyond pattern matching:
Detect "our unreleased financials and investor updates" even without PII, PHI, PCI keywords
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Code Repository Exposure Prevention
Developer uses Cursor with GitHub MCP to analyze codebase
Nightfall detects embedded AWS credentials in code being sent to Claude
Action: Auto-redact secrets, notify security team, allow rest of query
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Shadow AI Data Exfiltration Block
Employee uploads company financials to personal ChatGPT via browser
Nightfall identifies data lineage of document from approved Google Drive
Action: Block upload, present in-browser coaching: "Use ChatGPT Enterprise for corporate data"
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Malicious MCP Update Detection
Approved "slack-mcp-server" pushes version 2.1 with new data exfiltration tool
Nightfall's continuous scanning flags new "export_channel_history" capability
Action: Auto-quarantine update, alert SecOps for review before rollout
Enforce least-privilege access for every AI agent
Move beyond "allow all or block all." Define role-based policies that give developers the AI tools they need while protecting your most sensitive data - with no manual policy tuning.
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Curated MCP registry:
Approve the 50 MCPs that serve 90% of use cases, block 17,950+ others by default
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Role-based access control:
Engineering gets code analysis tools, Sales gets CRM access, Finance gets read-only access to reporting
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Data classification enforcement:
Block PHI/PII/PCI/IP in prompts to non-compliant AI services
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Time-based policies:
Restrict after-hours access to production databases via AI agents
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Exception workflows:
Allow users to request access with business justification, explicit on-demand approval by SecOps
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Graduated Response
Developer tries to send customer database schema to ChatGPT
Nightfall detects PII (customer names, emails) in schema
Action: Auto-redact PII, allow query with anonymized data, log event for review
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Scenario 2: Hard Block
Analyst attempts to use personal ChatGPT with corporate Google Drive OAuth
Nightfall detects non-enterprise ChatGPT connection
Action: Block connection, display message: "Use ChatGPT Enterprise. Request access with IT Team"
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Why Nightfall for AI Agent Security
Built for the agentic era - not retrofitted from legacy DLP
Capability
Traditional DLP + IRM
Gateway-Only Solutions
Nightfall AI
MCP Discovery
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No visibility
Limited to hosted MCPs
Full endpoint + cloud discovery
Desktop App Coverage
Blind to Cursor, Claude Desktop
No endpoint agent
Native desktop app monitoring
AI-Native Detection
Regex-based, 50% accuracy
Basic keyword filtering
LLM-powered, 95% accuracy
Policy Tuning Required
6-8 months tuning
Manual rule creation
Zero tuning - pre-trained
Deployment Time
12-18 months
4-6 weeks
Production in 2 weeks
False Positive Rate
80-95% alerts are noise
40-60% false positives
<5% false positives
Developer Experience
Heavy agents, constant friction
Gateway latency issues
Lightweight, millisecond overhead
Unified Platform
Buy 3-4 separate tools
MCP-only, no SaaS DLP
MCP + SaaS + Endpoint + GenAI
Full support
Limited / partial support
Not supported
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First-Mover Advantage
Nightfall is the first enterprise DLP platform purpose-built for MCP and agentic workflows. While competitors retrofit legacy architectures, we've designed for AI-first data flows from day one.
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AI-Native Detection
Pre-trained LLM and computer vision models eliminate the 6-8 month policy tuning death march. Our models understand context: "our Q4 roadmap" is sensitive even without PII/PHI patterns.
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Deploy in Days, Not Months
API-based integration with Claude, Cursor, VS Code, ChatGPT. No on-prem appliances. Get audit-ready visibility in your first week.
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Unified Platform Strategy
Don't buy separate tools for MCP security, SaaS DLP, endpoint DLP, and GenAI governance. Nightfall delivers one platform with consistent experience across your entire environment.
How Nightfall secures agentic workflows
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Customer Success Stories
Proven protection across critical AI agent scenarios
Challenge 1
Use Case 1: Secure AI-Powered Development
The Challenge
Engineering teams adopt Cursor and Claude for 10x productivity gains, but CISOs worry about code repositories with embedded secrets, customer data in test files, and unreleased IP being sent to external AI services.
Nightfall Solution
• Discover all GitHub MCP connections across all developers
• Scan code and commit history sent to AI agents for secrets, API keys, PII
• Alert on anomalous repo access (e.g., developer querying customer database repos outside their team)
• Scan code and commit history sent to AI agents for secrets, API keys, PII
• Alert on anomalous repo access (e.g., developer querying customer database repos outside their team)
Results
• 147 active secrets discovered and auto-rotated in first 30 days
• 100% visibility into which codebases are being analyzed by AI agents
• Zero developer complaints about productivity impact
• 100% visibility into which codebases are being analyzed by AI agents
• Zero developer complaints about productivity impact
Challenge 2
Use Case 2: Compliance-Ready AI Agent Auditing
Problem
Healthcare organization needs to prove to auditors that PHI accessed by AI agents (for clinical documentation, patient outreach, research analysis) complies with HIPAA. Current systems can't attribute AI agent actions to actual users or show what data was processed.
Nightfall Solution
• Log every MCP tool call with: timestamp, user, agent, data accessed, classification, action taken
• Distinguish AI agent actions from human actions in audit trails
• Automatic PHI detection in prompts and responses
• Distinguish AI agent actions from human actions in audit trails
• Automatic PHI detection in prompts and responses
Results
• Passed HIPAA audit with zero findings on AI agent data access
• Reduced audit preparation time from 4 weeks to 3 days
• Enabled compliant use of AI for clinical documentation (20 hours/week saved per provider)
• Reduced audit preparation time from 4 weeks to 3 days
• Enabled compliant use of AI for clinical documentation (20 hours/week saved per provider)
Schedule a live demo
Speak to a DLP expert. Learn the platform in under an hour, and protect your SaaS, AI apps in less than a day.
Not yet ready for a demo? Read our latest e-book, Protecting Sensitive Data from Shadow AI.
