Nightfall AI
AI-native DLP that blocks sensitive data leaving through AI agents, MCP servers, endpoints, and SaaS apps.
If your organization runs coding agents or MCP servers today, Nightfall is one of the few DLP platforms with genuine hook-level enforcement: prompts, MCP tool calls, tool responses, and shell commands can be scanned and blocked on Cursor, Claude Code, and VS Code, not just policy-documented. The Claude Compliance API monitoring and Claude Cowork OpenTelemetry trail make it the sharpest pick for Claude-heavy shops specifically. Against Microsoft Purview, the deciding question is whether your risk actually lives in agent and MCP traffic. Pricing is custom per user per year with no published rate, so budget-conscious teams without SecOps headcount should weigh Varonis, Strac, or a Purview
Verified 8d ago · liveness 78/100 · cite: rightaichoice.com/tools/nightfall-ai
- Security teams deploying Cursor, Claude Code, or VS Code agents that need hook-level DLP enforcement
- Organizations running MCP servers that need discovery, inventory, and per-server shadow-MCP risk scoring
- Claude Enterprise customers wanting conversation, file, and project monitoring via the Compliance API
- Regulated healthcare, fintech, legal, and manufacturing teams drowning in legacy DLP false positives
- Air-gapped or fully on-premises environments with no SaaS or AI tool usage
- Organizations that only need basic endpoint DLP with no AI app or agent coverage
- Companies with a mature Microsoft Purview deployment and no AI agent or MCP traffic today
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Skip Nightfall if you only need basic endpoint DLP with no AI app, agent, or MCP traffic — the hook-level agent enforcement is the reason to pay for it, and you'd be buying capability you never exercise.
Data Discovery and Classification includes 150GB of scanning volume; going past it means buying 1TB, 3TB, 5TB, or 20TB add-on tiers priced per year on top of your per-user license.
Nightfall's Foundation Edition and Premier Edition are both quoted per user per year, so the real cost scales with headcount rather than seats-in-use — a 2,000-person org pays for 2,000 users even if only a few hundred touch AI agents daily. It sits in the same bracket as Forcepoint DLP and Varonis, above lighter GenAI-only tools like Strac, and roughly comparable to Microsoft Purview if you already license E5. Vendor-published 2026 comparisons pitch 10x lower total cost of ownership versus
In short
Nightfall AI — AI-native DLP that blocks sensitive data leaving through AI agents, MCP servers, endpoints, and SaaS apps. Best for Security teams deploying Cursor, Claude Code, or VS Code agents that need hook-level DLP enforcement, Organizations running MCP servers that need discovery, inventory, and per-server shadow-MCP risk scoring, Claude Enterprise customers wanting conversation, file, and project monitoring via the Compliance API. Paid, in a currency we have not confirmed — see the pricing table for the vendor’s own figures.
What's new in Nightfall AI
Checked 8 days agoAcross the latest 4 updates: 2 pricing changes and 2 news mentions.
Nightfall publishes competitor pricing breakdowns for Cyera, Trellix, Strac and BigID
Pricing explainers for Cyera, Trellix DLP, Strac and BigID contrast their models with Nightfall's per-user pricing.
Nightfall posts 2026 DLP guides for ISO 27001 and CCPA/CPRA compliance
Two compliance-focused DLP guides cover ISO 27001 and CCPA/CPRA requirements, comparing Nightfall with Purview, Cyberhaven and Strac.
Nightfall publishes 2026 DLP buyer comparisons for insurance and fintech
Nightfall posted comparison guides for insurance and fintech DLP, positioning itself against Microsoft Purview, Forcepoint, Netskope and Symantec.
Nightfall publishes Varonis and Forcepoint DLP pricing comparisons
Pricing analyses for Varonis and Forcepoint DLP weigh TCO, MCP controls and AI security against Nightfall's per-user model.
Viability Score
How well maintained and how widely used is Nightfall AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- AI-based content classification across 100+ detectors, reported 95% detection precision
- LLM-powered file classifiers for contracts, HR records, source code, and financials
- Computer vision models for image and document content inspection
- AI-based data lineage tracking from source to destination, even after file renaming
- Shadow AI prevention for ChatGPT, Copilot, Gemini, Claude, Deepseek, Perplexity, Grok
- Hook-level interception for AI coding agents on prompts, MCP tool calls, tool responses, shell commands
- Hooks for Cursor, Claude Code (IDE and CLI), and VS Code on macOS and Windows
- LLM model responses inside agent hooks are monitor-only, not blocked
- MCP server discovery, inventory, and shadow-MCP detection with per-server risk scoring
- Local stdio plus remote HTTP and SSE MCP server discovery
- Prompt injection blocking via hook-level interception before tool execution
- Claude Compliance API monitoring for Claude Enterprise conversations, files, projects, activity feed
- OpenTelemetry audit trail for Claude Cowork and Claude Code sessions (cost, tokens, tool invocations)
- Nyx autonomous DLP analyst for investigation with full data lineage
- Automated remediation resolving roughly 80% of incidents, plus employee self-remediation workflow
About Nightfall AI
Nightfall AI is a data loss prevention platform built for security teams protecting sensitive data that now moves through AI agents, MCP servers, endpoints, browsers, email, and SaaS apps. Its engine combines more than 100 AI-based detectors, LLM-powered file classifiers, and computer vision models that the company reports classify content at 95% detection precision, against the 5-25% accuracy Nightfall attributes to pattern-matching rivals. That precision claim is the core pitch: fewer false positives, less alert fatigue, alerts a SecOps team actually trusts. Nyx, an autonomous DLP analyst, runs investigations, and the company reports automated remediation resolves roughly 80% of incidents. The product surface covers Data Exfiltration Prevention, Data Detection & Response, Data Discovery & Classification, and MCP & AI Agent Security. On the agent side, hook-level interception covers Cursor, Claude Code (IDE and CLI), and VS Code on macOS and Windows, scanning prompts, MCP tool calls, tool responses, and shell commands, while LLM model responses stay monitor-only. It discovers and inventories local stdio plus remote HTTP and SSE MCP servers, scores shadow-MCP risk per server, and exports OpenTelemetry audit trails for Claude Code and Claude Cowork sessions covering cost, tokens, and tool invocations. Shadow AI coverage spans ChatGPT, Copilot, Gemini, Claude, Deepseek, Perplexity, and Grok alongside endpoint agents, browser plugins, and API-based SaaS integrations. Who it's for: regulated healthcare, fintech, legal, and manufacturing security teams drowning in legacy DLP false positives, and any company that has put coding agents or MCP servers into production. Pricing is custom per user per year across Foundation Edition and Premier Edition, with a separate Data Discovery and Classification add-on. If your risk is entirely in AI agent and MCP traffic, Nightfall is one of the few platforms with real hook-level enforcement rather than a bolted-on AI policy page.
Behind the Verdict
Nightfall's differentiation is not the DLP category, it's where the detection happens. Legacy DLP was built for human-driven file transfers over email and file shares; Nightfall was built for agents that move data with no human in the loop. That shows up concretely. Hook-level interception in Cursor, Claude Code (IDE and CLI), and VS Code on macOS and Windows means a prompt, an MCP tool call, a tool response, or a shell command can be blocked before execution — including indirect prompt injection arriving via email that triggers an agent tool call. MCP server discovery covers local stdio plus remote HTTP and SSE, inventories unknown servers across endpoints, and scores shadow-MCP risk per server. For Claude Enterprise shops, the Compliance API covers conversations, files, projects, and the activity feed, and OpenTelemetry audit trails for Claude Code and Claude Cowork sessions surface cost, tokens, and tool invocations. Strengths: reported 95% detection precision across 100+ AI-based detectors, LLM file classifiers, and vision models, which matters more than any single feature because false-positive volume is what kills DLP programs; AI-based data lineage that reconstructs a chain even after a file is renamed and re-synced to personal storage; automated remediation the company reports resolves roughly 80% of incidents, plus employee coaching with business-justification workflow; API-based SaaS integrations the vendor says deploy in minutes, with lightweight endpoint agents and browser plugins; and comparison content that is unusually forthcoming about competitors, with 2026 pricing breakdowns published for Varonis, Forcepoint DLP, and Microsoft Purview. Weaknesses and honest constraints: agent hooks are macOS and Windows only, and inside those hooks LLM model responses are monitor-only while prompts, tool calls, tool responses, and shell commands can be blocked. AI Agent Security is sold in coverage tiers — Tier 1 for up to 3 apps versus Tier 2 for all supported apps. Deployment is real integration work across SaaS apps, AI tools, endpoints, MCP servers, and IDEs; this is not a set-and-forget tool. Pricing is custom per user per year with no public rate card, which makes it hard to model before a sales conversation. Where it fits: security teams at regulated companies with an AI usage problem they can already see, Claude-heavy enterprises, and anyone who has discovered unknown MCP servers running on developer laptops. Where it doesn't: air-gapped environments with no SaaS or AI tool usage, teams that only need basic endpoint DLP, and companies with a mature Purview deployment and no agent or MCP traffic today.
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Real-world workflow fit
Concrete scenarios for the personas Nightfall AI actually fits — and what changes day-one when you adopt it.
A developer's Claude Code session reads a proprietary payments codebase through a GitHub MCP server and tries to push a snippet out through an unknown local stdio MCP server that appeared on the machine last week.
Outcome: The hook intercepts the shell command before execution, the unknown MCP server shows up in the inventory with a shadow-MCP risk score, and the engineer gets an alert with the full tool-call chain rather than a mystery block.
An employee pastes PHI from a patient export into ChatGPT to draft a summary, which is the exact workflow Shadow AI prevention exists to catch.
Outcome: The upload is blocked at the browser, the employee is prompted for a business justification and redirected to an approved AI tool, and the incident resolves through self-remediation instead of a manual triage ticket.
Monthly review of the alert queue: 4,000 pattern-match hits, most of them false positives, and no way to tell which file actually left the building.
Outcome: Nightfall's detectors classify content with reported 95% precision, AI-based lineage reconstructs a file's path even after renaming and re-syncing, and roughly 80% of incidents close through automation or employee self-remediation.
Use Cases
- Prevent secrets and credentials from leaking in Slack messages or Git commits.
- Auto-redact PHI in Gmail or Google Drive to maintain HIPAA compliance.
- Block intellectual property from being uploaded to unauthorized AI apps like ChatGPT.
- Revoke over-permissioned sharing of sensitive files in Google Drive and OneDrive.
- Monitor and block data exfiltration via USB drives, clipboard, or print on managed endpoints.
- Investigate data exfiltration incidents using the Nyx autonomous analyst with full lineage.
- Secure AI agent tool calls to Salesforce, preventing data exposure through MCP servers.
- Detect and block indirect prompt injection attacks targeting Claude and other agentic AI.
Models Under the Hood
as of 2026-09-22
Limitations
- AI agent security hooks cover Cursor, Claude Code (IDE and CLI), and VS Code on macOS and Windows only — Linux and other IDEs aren't in the documented hook set.
- Within those hooks, LLM model responses are monitor-only, while prompts, MCP tool calls, tool responses, and shell commands can be scanned and blocked.
- AI Agent Security is sold in coverage tiers: Tier 1 covers up to 3 apps, Tier 2 covers all supported apps.
- Data Discovery and Classification includes 150GB of scanning volume, with 1TB, 3TB, 5TB, and 20TB add-on tiers above that.
- Endpoint and browser coverage is documented at 2 devices per user in the plan comparison.
- Deployment is real integration work across SaaS apps, AI tools, endpoints, MCP servers, and IDEs rather than a single switch.
as of 2026-09-30
Verification history
We have re-verified Nightfall AI 19 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 19 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Nightfall AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Foundation Edition
Custom (per user/year)
Ideal for
Security teams that need AI-native DLP across SaaS, email, AI apps, and endpoints but haven't put coding agents or MCP servers into production yet.
What this tier adds
Starting tier — all Data Detection & Response plus Data Exfiltration Prevention features, dedicated CSM, and priority support with a 1-hour SLA.
Premier Edition
Custom (per user/year; Tier 1 up to 3 apps, Tier 2 all suppo
Ideal for
Engineering-led organizations running Cursor, Claude Code, or VS Code agents and Claude Enterprise, where the risk travels through agent tool calls rather than file shares.
What this tier adds
Adds agent security on top of Foundation: hooks for Cursor, Claude Code, and VS Code, MCP tool-call scanning and blocking, MCP server discovery, OpenTelemetry audit trail, and Claude Compliance API monitoring.
Data Discovery and Classification Add-On
Custom (150GB included; 1TB / 3TB / 5TB / 20TB tiers)
Ideal for
Teams that need to find and clean up years of accumulated sensitive data exposure across SaaS apps, separate from real-time blocking.
What this tier adds
Add-on to either edition — data-at-rest scanning and remediation across 12+ SaaS apps, with 150GB included and 1TB, 3TB, 5TB, and 20TB tiers above it.
Where the pricing makes sense
The company stage and team size where Nightfall AI's pricing actually pencils out — and where peers do it cheaper.
Nightfall's Foundation Edition and Premier Edition are both quoted per user per year, so the real cost scales with headcount rather than seats-in-use — a 2,000-person org pays for 2,000 users even if only a few hundred touch AI agents daily. It sits in the same bracket as Forcepoint DLP and Varonis, above lighter GenAI-only tools like Strac, and roughly comparable to Microsoft Purview if you already license E5. Vendor-published 2026 comparisons pitch 10x lower total cost of ownership versus
Setup time & first value
How long it actually takes to get something useful out of Nightfall AI — broken out by persona, not the marketing-page minute.
The vendor states API-based SaaS integrations deploy in minutes and the Foundation Edition can be deployed in about an hour for the core detection engine. Endpoint agents and browser plugins install per device through Jamf or Intune, and the plan comparison documents 2 devices per user. Agent security hooks for Cursor, Claude Code, and VS Code are a separate rollout with developer coordination.
Switching to or from Nightfall AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Microsoft Purview: start with Nightfall's published 2026 Purview pricing comparison, then run both in parallel on SaaS and email while agent hooks are validated on a pilot developer group.
- →From Varonis: use the vendor's 2026 Varonis pricing breakdown as the business case, and scope the Data Discovery and Classification add-on first to match Varonis's data-at-rest scanning footprint.
- →From Forcepoint DLP: map existing endpoint policies to Nightfall's endpoint agent, then add AI app and MCP coverage that Forcepoint's comparison doesn't address.
- →From manual Shadow AI monitoring (browser logs and firewall rules): deploy the browser plugin and SaaS integrations first to get visibility before turning on blocking.
- ↗To Microsoft Purview: viable only if you drop AI agent and MCP hook enforcement, since Purview's strength is Microsoft 365 data rather than in-session agent control.
- ↗To Varonis: a path for data-at-rest discovery-heavy programs that don't need IDE-level agent hooks.
- ↗To Strac: lighter GenAI and AI agent coverage at a lower price point if MCP server inventory and Claude Compliance API monitoring aren't requirements.
- ↗To Cyberhaven: an option named in Nightfall's own Google Drive DLP comparison for teams prioritizing file lineage on endpoints over SaaS and email coverage.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Nightfall AI”, and we withheld 6: 6 could not be judged, because “Nightfall AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Nightfall AI.
Official links
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