No solution approach in the current research connects this vendor to this use case.
Vendor research
Nightfall AI
Review what this vendor says publicly, the security topics those statements may support, what remains unverified, and factual company context. This is not an assessment of product effectiveness or fit.
Use-case context
How this vendor relates to the selected use case
No related approach foundBack to use case →
These links show approaches associated with this vendor. The relationship label describes how the approach maps to the use case—not product effectiveness, complete requirement coverage, or fit.
Company scale
Growth stage?
Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.
?
Growth stageA provider with at least $25M in known funding or at least 51 employees that has not reached the scaled threshold.This is a company-scale signal, not a product-quality rating.A descriptive band derived from retained public revenue, workforce, ownership, or funding signals.
Company scale is separate from product features, effectiveness, and suitability.
- $60.3M known funding
- 51-200 employees
- Founded 2018
- Private-company revenue and profitability not sourced
Company context
Private, VC-backed
More mature data-security/data loss prevention (DLP) vendor than most AI-native entrants; AI positioning builds on established data loss prevention (DLP) motion
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 21 records
- Source-checked records
- 13
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 5
Company intelligence
Who is behind the product
Company facts provide evaluation context. Each signal is kept separate because tenure, workforce, funding, and hiring answer different questions.
Founded2018
HeadquartersSan Francisco, California
OwnershipPrivate, VC-backed
Employees51-200
Capital and scaleIndependent company
Nightfall AI
- Known funding
- $60.3M
- Operating scale
- More mature data-security/DLP vendor than most AI-native entrants; AI positioning builds on established DLP motion
- Backing context
- At least $60.3M raised from WestBridge Ventures, Venrock, Pear VC, Bain Capital Ventures, and others
Series B · $40M · 2022-08-10
Named investors
WestBridge Capital · Next Play Capital · Bain Capital Ventures · Venrock · Pear VC
Founders and leadership2 people listed
- Rohan SatheCurrent role listed
Co-Founder & CEO
- Isaac MadanCurrent role listed
Co-Founder
Operating signalsRead each signal separately
There is no combined company rating. The company-scale label uses stated size thresholds; product features and effectiveness require separate evidence.
- Company tenure
- 2018
- Workforce scale
- 51-200
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Company sources and research limits6 linked public sources
Only company facts supported by retained public sources are shown. Missing values remain unknown, and company scale does not establish product effectiveness.
Company sourceGaebler funding profilePublic funding profile lists San Francisco headquarters, private status, and at least $60.3M raised.Company sourceNightfall homepageNightfall positions itself around AI data security and DLP.Company information sourceNightfall company pageSupports the company facts shown in this profile.Company information sourceNightfall Series B announcementSupports the company facts shown in this profile.Company information sourceNightfall founder historySupports the company facts shown in this profile.Company information sourceNightfall AI LinkedIn company profileSupports the company facts shown in this profile.
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Relevant where sensitive-data protection and data loss prevention (DLP) are central to the AI security ask.
- Buyer diligence should compare AI controls against existing enterprise data loss prevention (DLP), cloud access security broker (CASB), browser, and endpoint controls.
Related frameworks
Where public vendor statements relate to framework requirements
11 related frameworks · expand when needed
Related frameworks
Where public vendor statements relate to framework requirements
These links show related requirements for further review. They do not establish framework compliance or control implementation. Open the full framework crosswalk →
Current referenceCIS Critical Security Controls
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 13
- Related requirements
- 14
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 35
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 71
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 39
- Requirements with public support
- 13
- Related requirements
- 12
- References
- 31
- Requirements with public support
- 13
- Related requirements
- 11
- References
- 27
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 31
- Requirements with public support
- 13
- Related requirements
- 13
- References
- 25
- Requirements with public support
- 1
- Related requirements
- 3
- References
- 3
Evaluation questions
What to verify beyond public claims
These questions come from security requirements with some public support. Use them as starting points for demonstrations, documentation review, customer references, or a buyer-observed pilot.
- 01Unapproved AI use discovery
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
- 02Unapproved AI use discovery
The test user's AI usage activity can be filtered or exported with AI-specific context.
- 03AI-feature discovery in business applications
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
- 04AI-feature discovery in business applications
The inventory shows which users, data classes, integrations, or providers are associated with the AI-enabled software as a service (SaaS) app.
- 05Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 06Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 07Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 08Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
Strong public supportDiscover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
AI-feature discovery in business applications
Limited public supportInventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A software as a service (SaaS) app with an embedded AI feature appears in the software as a service (SaaS) AI inventory with app, provider, and feature context.
Approved AI usage monitoring
No supporting claim foundMonitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Controls for unapproved AI use
Strong public supportBlock, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Sensitive-data protection for generative AI
Strong public supportDetect, classify, redact, or block sensitive data in prompts, responses, files, retrieval, memory, and AI-connected workflows.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
Browser and business-application controls
Strong public supportApply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
Generative AI application security
Limited public supportProtect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
AI governance, risk, and compliance
Strong public supportInventory AI systems and owners, translate policy and regulatory obligations into governed workflows, assess risk, manage approvals and exceptions, and retain audit evidence across the AI lifecycle.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
AI assurance and adversarial testing
No supporting claim foundTest models, applications, retrieval-augmented generation (RAG) systems, and agents before release and continuously thereafter using adversarial probes, evaluation suites, attack simulation, and security release gates.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
AI model and supply-chain security
Strong public supportDiscover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
AI gateway, tool-connection, and runtime controls
Strong public supportMediate model, agent, tool, application programming interface (API), connector, and Model Context Protocol (MCP) traffic through an enforcement point that applies identity-aware policy, content controls, routing, rate limits, and auditable allow or deny decisions.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Action-taking agent monitoring
Strong public supportObserve and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Agent-to-agent communication security
Limited public supportAuthorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Non-human identity and service-account security
No supporting claim foundInventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
AI agent identity and permissions
Limited public supportRegister AI agents as accountable identities, bind them to owners and delegating users, authorize task- and tool-level access, issue short-lived credentials, review access, and revoke or suspend agent authority.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
AI coding-agent and workstation security
Strong public supportDiscover and govern AI coding agents, integrated development environment (IDE) assistants, command-line agents, skills, hooks, extensions, Model Context Protocol (MCP) tools, filesystem access, commands, network activity, secrets, and software-supply-chain actions on developer workstations and build environments.
NIST AI RMF PlaybookNIST Cybersecurity Framework 2.0CIS Critical Security ControlsISO/IEC 42001OWASP GenAI Security Solutions Landscape
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
AI cost and usage controls
No supporting claim foundVisibility, attribution, budgeting, rate limiting, anomaly detection, and optimization for AI usage and spend across models, agents, workflows, and owners.
Commercial MetadataNIST AI RMF PlaybookNIST Cybersecurity Framework 2.0ISO/IEC 42001OWASP Agentic AI Security Solutions Landscape
A controlled AI usage event is attributed to user, team, model, workflow, or owner with cost or token metrics.
Licensing model
No supporting claim foundPublicly discoverable commercial model such as per user, per seat, per app, per token, per integration, or enterprise platform license.
The vendor can map the sourced commercial model to per-user, per-seat, per-app, per-token, per-integration, or platform packaging.
Public sources
Vendor statements and quoted evidence
Showing the first 6 of 21 source records. Open additional records only when needed.
Nightfall claims agent and Model Context Protocol (MCP) inventory, user and device attribution, usage mapping, audit-ready reports, full request and response logs, approval status, and consistent generative AI governance across software as a service (SaaS), endpoints, and agentic workflows.
Export audit-ready reports for compliance teams.
Nightfall Model Context Protocol (MCP) and agent security materials reviewed did not provide a public product claim for automated adversarial testing, repeatable attack suites, model or agent red teaming, or release-gate evaluation.
No quoted source text is recorded for this claim.
Nightfall claims a registry of more than 20,000 Model Context Protocol (MCP) servers, real-time configuration scanning, version-change monitoring, tool-capability analysis, dependency drift detection, and automatic quarantine of malicious updates before rollout.
Continuous scanning flags new capabilities and auto-quarantines the update for review before rollout.
Nightfall claims protocol-level interception, full request visibility, granular server and tool control, sensitive-data detection, auto-redaction, blocking, anomaly detection, and quarantine across Model Context Protocol (MCP) prompts, files, application programming interface (API) calls, responses, and tools.
Monitor every MCP tool call in real-time.
Nightfall claims employee and device attribution, agent-to-system access mapping, allowlisted Model Context Protocol (MCP) servers, granular tool authorization, and blocking of unapproved tools and connections.
Map which employees use which agents, what systems they access, and track usage patterns over time.
Nightfall claims controls for Cursor, VS Code, Claude, and custom Model Context Protocol (MCP) integrations including source-code and file inspection, embedded-secret redaction, server and tool allowlisting, version monitoring, request logging, and malicious-update quarantine.
Automatically discover and catalog all MCP servers across Claude Desktop, Cursor, VS Code, and custom integrations.
Show 15 additional evidence records
Nightfall AI and Model Context Protocol (MCP) security materials reviewed did not provide a public product claim for AI usage-cost attribution, token or spend metrics, budgets, chargeback, or cost-aware model routing.
No quoted source text is recorded for this claim.
Nightfall claims Shadow AI security across generative AI apps including ChatGPT, Copilot, Gemini, Deepseek, Claude, Perplexity, and others.
Nightfall provides comprehensive Shadow AI security across any generative AI app including ChatGPT, Copilot, Gemini, Deepseek, Claude, Perplexity and more.
Nightfall claims its AI-native data loss prevention (DLP) platform prevents sensitive data exposure and exfiltration across software as a service (SaaS), endpoints, email, browsers, and AI apps.
Nightfall is the AI-native DLP platform that prevents sensitive data exposure and exfiltration across SaaS, endpoints, email, browsers, and AI apps.
Nightfall claims comprehensive shadow-AI security coverage across major generative AI applications including ChatGPT, Copilot, Gemini, Claude, and others.
Nightfall provides comprehensive Shadow AI security across any generative AI app including ChatGPT, Copilot, Gemini, Deepseek, Claude, Perplexity and more.
No public claim found for this capability.
No quoted source text is recorded for this claim.
Nightfall claims it automatically blocks secrets, credentials, PHI, PCI, and other confidential information through file uploads and clipboard actions.
Nightfall automatically blocks secrets, credentials, PHI, PCI, or other confidential information via file uploads or clipboard copy/paste actions.
Nightfall claims real-time visibility and control over sensitive data movement across AI agents, Model Context Protocol (MCP) servers, endpoints, and software as a service (SaaS), preventing data that should not leave.
Control data movement across AI agents, MCP servers, endpoints, and SaaS—without slowing innovation. Nightfall gives you real-time visibility and control over how sensitive data moves — and prevents what shouldn't leave. Legacy data security can't see or stop this.
Nightfall claims unknown Model Context Protocol (MCP) servers can be discovered across endpoints and surfaced in a dashboard with policy defined to block Model Context Protocol (MCP) servers.
Unknown MCP servers discovered running locally across 12 endpoints > full inventory surfaced in the dashboard. Policy defined to block MCP servers.
Nightfall claims Model Context Protocol (MCP) gateway controls, audit logs, registry scanning, and per-tool policy enforcement for AI-agent workflows.
Every tool call flows through the gateway
Nightfall claims detection of secrets, credentials, application programming interface (API) keys, and certificates in generative AI prompts and AI workflows.
credentials and secrets such as passwords or API keys
No public claim found for this capability.
No quoted source text is recorded for this claim.
Nightfall claims its browser plugins and endpoint agents monitor AI interactions in real-time, analyzing prompts and file uploads before they reach AI platforms.
Our browser plugins and endpoint agents monitor AI interactions in real-time, analyzing prompts and file uploads before they reach AI platforms.
Nightfall claims AI-agent security controls that intercept and block indirect prompt-injection driven tool calls.
Indirect prompt injection via email triggers Claude agent tool calls > Nightfall's hooks intercept and block before execution.
No public claim found for this capability.
No quoted source text is recorded for this claim.
No public claim found for this capability.
No quoted source text is recorded for this claim.