ISO/IEC 42001 for Pangea / CrowdStrike Falcon AIDR
See how Pangea / CrowdStrike Falcon AIDR's public claims connect to security requirements and ISO/IEC 42001 references.
What this page shows
Requirements connected to Pangea / CrowdStrike Falcon AIDR's public claims
Only requirements with strong or limited public support appear. The framework references identify what to investigate; they do not establish implementation, conformance, certification, or product effectiveness.
VersionISO/IEC 42001:2023Current source
Related requirements10security questions in this research
Security requirements with public support8strong or limited public support
References30identifiers, clauses, safeguards, or categories
How to use this map
Framework connections help structure your evaluation
Each connection shows how a security requirement relates to this framework. Public vendor claims are shown separately, and deployed effectiveness still requires confirmation or testing.
Related context
7
Closely aligned
3
Requirement connections
From Pangea / CrowdStrike Falcon AIDR's public claims to questions to verify
Each row starts with a security requirement that has public support, then shows the connected framework references and the next question to verify.
Related contextSpecific reference · research-team interpretation
AI usage inventory
Maintain an inventory of AI tools, services, models, agents, software as a service (SaaS) AI capabilities, data flows, and provider relationships.
Framework references
A.4.2 · A.4.3 · A.4.4
Lifecycle
Govern · Identify · Monitor
Security requirements with public support
1 requirement with public support
Limited public supportApproved AI usage monitoring
Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Monitor AI usage, user activity, prompts, responses, provider calls, runtime actions, and anomalous behavior.
Framework references
9.1 · A.6.2.6 · A.6.2.8
Lifecycle
Monitor · Detect · Operate
Security requirements with public support
2 requirements with public support
Limited public supportApproved AI usage monitoring
Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
Public claims reviewed
1
Next question to verify
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
Public claims reviewed
1
Next question to verify
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Related contextSpecific reference · research-team interpretation
generative AI application security
Secure large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, tool calls, application programming interfaces (APIs), model interactions, and runtime behavior.
Framework references
A.6.2.4 · A.6.2.5 · A.6.2.6 · A.6.2.8
Lifecycle
Develop · Test · Release · Deploy · Operate
Security requirements with public support
4 requirements with public support
Strong public supportGenerative AI application security
Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
Public claims reviewed
1
Next question to verify
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Block, coach, redirect, or contain non-approved AI use and policy-violating AI interactions.
Public claims reviewed
1
Next question to verify
A policy blocks, coaches, redirects, or contains a test interaction with an unapproved AI destination.
Review source claims →Strong public supportAI gateway, tool-connection, and runtime controls
Mediate 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.
Public claims reviewed
1
Next question to verify
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Maintain accountable AI inventory, policy, risk assessments, approvals, exceptions, regulatory mappings, third-party oversight, and audit evidence across the AI lifecycle.
Framework references
A.9.2 · A.9.3 · A.10.2
Lifecycle
Govern · Identify · Assess · Approve · Monitor
Security requirements with public support
2 requirements with public support
Limited public supportAI governance, risk, and compliance
Inventory 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.
Public claims reviewed
1
Next question to verify
A test AI system is registered with owner, intended use, risk tier, lifecycle state, and applicable obligations.
Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
Related contextSpecific reference · research-team interpretation
Agent-to-agent and tool communication security
Secure trust, authorization, message flows, tool access, and communication between agents, tools, application programming interfaces (APIs), and external services.
Framework references
A.6.2.4 · A.6.2.8 · A.10.2
Lifecycle
Identify · Protect · Deploy · Monitor
Security requirements with public support
4 requirements with public support
Limited public supportAgent-to-agent communication security
Authorize, log, and control agent-to-agent, agent-to-tool, Model Context Protocol (MCP), connector, and tool-chain handoffs.
Public claims reviewed
1
Next question to verify
An agent, tool, connector, or Model Context Protocol (MCP) handoff logs source identity, destination, and authorization decision.
Protect enterprise-built large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, application programming interfaces (APIs), model calls, tools, and production runtime behavior.
Public claims reviewed
1
Next question to verify
A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
Review source claims →Strong public supportAI gateway, tool-connection, and runtime controls
Mediate 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.
Public claims reviewed
1
Next question to verify
A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
Related contextSpecific reference · research-team interpretation
AI FinOps and cost accountability
Attribute AI usage and spend to accountable owners, workflows, agents, models, and business units while enforcing budget, rate-limit, and routing controls.
Framework references
A.9.2 · A.9.3
Lifecycle
Govern · Operate · Optimize
Security requirements with public support
2 requirements with public support
Limited public supportApproved AI usage monitoring
Monitor approved AI workspaces, tenants, gateways, and model platforms such as ChatGPT Enterprise, Claude Enterprise, Gemini, Microsoft Copilot, Vertex AI, Elvex, or internal AI gateways.
Public claims reviewed
1
Next question to verify
Approved AI workspace activity appears with user, workspace or tenant, model or provider, and timestamp.
This page organizes research. Audit conclusions, certification assessments, control implementation statements, and vendor endorsements require separate evidence.