No solution approach in the current research connects this vendor to this use case.
Vendor research
Pangea / CrowdStrike Falcon AIDR
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
Established?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity signal, not a product-quality rating.
?
EstablishedA provider with at least $1B in annual revenue, at least 1,000 employees, or backing from an established owner.This is a company-maturity 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.
- $4.8B annual revenue (2026-01-31)
- $52M known funding
- 11-50 employees
- Founded 2021
Company context
Acquired by public company CrowdStrike (NASDAQ: CRWD) in September 2025
Pangea positions AI Guard and Prompt Guard across AI application guardrails, prompt injection, sensitive data leakage, malicious content, audit logging, and Model Context Protocol (MCP) guardrail workflows
Research coverageCounts describe available public research, not product quality.View details
- Vendor statements
- 19 records
- Source-checked records
- 9
- Evaluation requirements
- 19 in this research model
- Unresolved requirements
- 10
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.
Founded2021
HeadquartersPalo Alto, California
OwnershipAcquired by public company CrowdStrike (NASDAQ: CRWD) in September 2025
Employees11-50
Capital and scaleIndependent company
Pangea Cyber
- Known funding
- $52M
- Current owner annual revenue
- $4.8B
- Operating scale
- Pangea positions AI Guard and Prompt Guard across AI application guardrails, prompt injection, sensitive data leakage, malicious content, audit logging, and MCP guardrail workflows
- Backing context
- $26M Series B led by GV; approximately $52M total raised before acquisition, with Decibel, Okta Ventures, Ballistic Ventures, and SYN Ventures participating
Series B · $26M · 2022-11-30
CrowdStrike Holdings, Inc. (CRWD) · period ended 2026-01-31 · filed 2026-03-05
Named investors
GV · Decibel · Okta Ventures · Ballistic Ventures · SYN Ventures
Founders and leadership2 people listed
- Oliver FriedrichsStatus not confirmed
Founder & CEO before acquisition
- Sourabh SatishStatus not confirmed
Co-Founder & CTO before acquisition
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
- 2021
- Workforce scale
- 11-50
- Hiring activity
- Not displayed
A current count requires a retained, clickable source URL.
Core company facts have supporting public sources.
Company sources and research limits9 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 sourcePangea AI Guard and Prompt Guard launch postPangea says AI Guard and Prompt Guard defend AI data ingestion and inference pipelines from LLM threats such as prompt injection.Company sourcePangea AI Guard and Prompt Guard GA announcementPangea describes generally available AI Guard and Prompt Guard capabilities for sensitive-data leakage, malicious content, prompt injection, and AI visibility/access-control context.Company sourcePangea MCP guardrails blogPangea describes an MCP server pattern for invoking AI security guardrails around prompts, redaction, secure audit logging, IP/domain reputation, and tool/data-source outputs.Company sourceCrowdStrike Form 10-QCrowdStrike's quarterly filing records its September 26, 2025 acquisition of Pangea Cyber Corporation.Company sourcePangea Series B announcementPangea announced a $26M Series B and approximately $52M total funding.Company information sourcePangea company pageSupports the company facts shown in this profile.Company information sourcePangea company profileSupports the company facts shown in this profile.Company information sourcePangea LinkedIn company profileSupports the company facts shown in this profile.Regulatory filing10-K annual filingCrowdStrike Holdings, Inc. (CRWD) · period ended 2026-01-31
Solution areas
These areas describe how the vendor approaches enterprise AI security. They do not establish product quality or fit.
Buyer context
- Treat Pangea as an AI application guardrails and security-services application programming interface (API) candidate rather than a workforce shadow-AI discovery or browser/software as a service (SaaS) session control platform.
- Public evidence supports prompt-injection defense, sensitive-data redaction, malware and malicious-entity scanning, audit logging for sensitive-data processing, and Model Context Protocol (MCP) guardrail workflows.
- Public pages reviewed did not expose software as a service (SaaS) embedded-AI inventory, browser/session controls, non-human identity (NHI) lifecycle management, AI FinOps controls, or a current public AI Guard/Prompt Guard licensing unit.
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
- 8
- Related requirements
- 11
- References
- 58
- Requirements with public support
- 8
- Related requirements
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- References
- 71
- Requirements with public support
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- Related requirements
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- References
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- Requirements with public support
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- Related requirements
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- References
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- Requirements with public support
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- Related requirements
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- References
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- Requirements with public support
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- Related requirements
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- Requirements with public support
- 8
- Related requirements
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- References
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- Requirements with public support
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- Related requirements
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- Requirements with public support
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- Related requirements
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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.
- 01Approved AI usage monitoring
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
- 02Approved AI usage monitoring
Prompt, model, or admin activity can be exported or correlated for the selected approved AI platform.
- 03Controls for unapproved AI use
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
- 04Controls for unapproved AI use
The control event records policy reason, user, destination, action, and timestamp.
- 05Sensitive-data protection for generative AI
Sensitive prompt, response, or file test data is detected and classified during an AI interaction.
- 06Sensitive-data protection for generative AI
A policy redacts, blocks, coaches, or records the sensitive data event before it leaves the approved path.
- 07Generative AI application security
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
- 08Generative AI application security
A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
Detailed security-requirement research18 evaluation items · supporting evidence and open research are shown separatelyExpand
Security requirementPublic supportRelated frameworksWhat to verify
Unapproved AI use discovery
No supporting claim foundDiscover 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
No supporting claim foundInventory 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
Limited public supportMonitor 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
Limited 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
No supporting claim foundApply 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
Strong 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
Limited 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
No supporting claim foundDiscover, 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
Limited 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
No supporting claim foundRegister 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
No supporting claim foundDiscover 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 19 source records. Open additional records only when needed.
Pangea AI Guard claims customizable security recipes, centralized usage summaries, tamper-resistant activity logging, attribution, webhooks, and audit trails for AI application events.
Requests to the AI Guard APIs and their processing results are logged in your Pangea project’s audit trail.
Pangea AI Guard and Prompt Guard 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.
Pangea AI Guard materials reviewed did not provide a public product claim for model artifact scanning, provenance, signing, dependency inventory, tamper analysis, or registry release controls.
No quoted source text is recorded for this claim.
Pangea AI Guard claims application programming interface (API) and gateway-integrated enforcement across prompts, model responses, retrieval-augmented generation (RAG) ingestion, agent plans, tool inputs, and tool outputs using configurable block, report, redact, encrypt, and defang actions.
Recipes can be configured to block, report, redact, encrypt, or defang sensitive or malicious content.
Pangea AI Guard materials reviewed did not establish agent identity registration, distinct agent credentials, delegated authorization, ownership, rotation, revocation, or agent access-review lifecycle.
No quoted source text is recorded for this claim.
Pangea AI Guard materials reviewed did not establish governance of coding-agent commands, developer-workstation files or networks, integrated development environment (IDE) extensions, skills, hooks, secrets, or package actions.
No quoted source text is recorded for this claim.
Show 13 additional evidence records
Pangea materials reviewed did not provide a public claim for discovering and monitoring workforce shadow AI tools, accounts, domains, models, users, or unapproved employee AI usage.
No quoted source text is recorded for this claim.
Pangea materials reviewed did not provide a public claim for software as a service (SaaS) AI inventory, embedded software as a service (SaaS) AI feature discovery, or third-party AI service provider monitoring.
No quoted source text is recorded for this claim.
Pangea says its Secure Audit Log service should track access to and processing of sensitive data inside generative AI applications.
Pangea’s Secure Audit Log service should be used to track access to, and processing of sensitive data.
Pangea claims Prompt Guard analyzes user and system prompts to block jailbreak attempts and organizational limit violations.
Pangea Prompt Guard analyzes user and system prompts to block jailbreak attempts and organizational limit violations.
Pangea claims AI Guard scans and sanitizes prompts and uploaded files, removes malicious content, and redacts sensitive information.
AI Guard scans and sanitizes all prompts and uploaded files of malware, leaked credentials, and malicious IPs and domains, and automatically redacts sensitive information with over 75 classification rules out of the box and support for custom data classification rules.
Pangea materials reviewed did not provide a public claim for browser extension, enterprise browser, software as a service (SaaS) session controls, upload/download/copy/paste controls, or identity-aware browser/software as a service (SaaS) policy.
No quoted source text is recorded for this claim.
Pangea claims Prompt Guard detects and stops direct and indirect prompt injection attacks and jailbreak attempts in AI applications.
Pangea Prompt Guard detects and stops direct and indirect prompt injection attacks and jailbreak attempts in AI applications.
Pangea claims traffic through its Model Context Protocol (MCP) server, including user prompts from the large language model (LLM) and outputs from tools and data sources, can be checked by configured guardrails.
All traffic through the MCP server—user prompts from the LLM and outputs from tools and data sources—would be checked by configured guardrails.
Pangea claims its Model Context Protocol (MCP) server can call guardrail services for malicious prompt checks, redaction, secure audit logging, and IP/domain reputation checks.
With Pangea’s new open-source MCP server, organizations can directly call Pangea AI security guardrail services that check for malicious prompts or prompt injection attempts as well as redact sensitive information, implement secure audit logging, check IP addresses and domains for malicious reputations, and perform WHOIS / geolocation lookups.
Pangea materials reviewed did not provide a public claim for AI-agent identity inventory, service-account ownership, scoped credentials, secrets rotation, least privilege, or non-human identity (NHI) lifecycle management.
No quoted source text is recorded for this claim.
Pangea materials reviewed did not provide a public claim for AI spend attribution, model cost routing, budget enforcement, rate limits, runaway token controls, anomaly detection, or AI return on investment (ROI) reporting.
No quoted source text is recorded for this claim.
Pangea materials reviewed did not provide a public per-user, per-seat, per-app, per-token, usage-based, or enterprise AI Guard/Prompt Guard licensing model.
No quoted source text is recorded for this claim.
Pangea claims AI Guard and Prompt Guard defend AI data ingestion and inference pipelines from large language model (LLM) threats alongside authorization and audit logging services.
These new services equip customers to defend AI data ingestion and inference pipelines from LLM threats like prompt injection and, in combination with Pangea’s existing suite of security services like authorization and audit logging, offer the industry’s most comprehensive set of security guardrails for AI applications.