OWASP Top 10 for LLM Applications for Netskope AI Security
See how Netskope AI Security's public claims connect to security requirements and OWASP Top 10 for LLM Applications references.
What this page shows
Requirements connected to Netskope AI Security'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.
Version2025 (v2.0)Current source
Related requirements12security questions in this research
Security requirements with public support16strong or limited public support
References24identifiers, 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.
Contributes
7
Closely aligned
5
Requirement connections
From Netskope AI Security'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.
ContributesSpecific reference
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
LLM03:2025
Lifecycle
Govern · Identify · Monitor
Security requirements with public support
3 requirements with public support
Strong public supportUnapproved AI use discovery
Discover and monitor workforce AI tools, accounts, prompts, domains, models, users, and usage outside approved controls.
Public claims reviewed
1
Next question to verify
An unmanaged AI app used by a test user appears in discovery inventory with user, app or domain, and timestamp.
Review source claims →Strong public supportAI-feature discovery in business applications
Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
Public claims reviewed
1
Next question to verify
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.
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 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.
Apply session-level controls in browser and software as a service (SaaS) workflows, including uploads, downloads, copy/paste, sharing, and identity-aware access decisions.
Public claims reviewed
1
Next question to verify
A session-level policy controls upload, download, copy, paste, sharing, or form submission in a browser or software as a service (SaaS) workflow.
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.
Secure large language model (LLM) applications, retrieval-augmented generation (RAG) systems, prompts, tool calls, application programming interfaces (APIs), model interactions, and runtime behavior.
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
LLM03:2025
Lifecycle
Govern · Identify · Assess · Approve · Monitor
Security requirements with public support
3 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.
Review source claims →Strong public supportAI-feature discovery in business applications
Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
Public claims reviewed
1
Next question to verify
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.
AI assurance, red teaming, and supply-chain security
Test models, applications, retrieval-augmented generation (RAG) systems, agents, coding workflows, and AI artifacts before release and continuously thereafter, with reproducible findings and remediation gates.
Framework references
LLM01:2025 · LLM03:2025 · LLM05:2025
Lifecycle
Develop · Test · Release · Monitor
Security requirements with public support
3 requirements with public support
Strong public supportAI assurance and adversarial testing
Test 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.
Public claims reviewed
1
Next question to verify
A controlled test campaign exercises an AI model, application, or agent against named AI attack classes.
Discover, inventory, scan, validate, and monitor models, datasets, model artifacts, registries, dependencies, and AI development assets for tampering, unsafe serialization, provenance gaps, or malicious content.
Public claims reviewed
1
Next question to verify
A test model or AI artifact appears in inventory with origin, version, hash or provenance, and deployment context.
Discover 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.
Public claims reviewed
1
Next question to verify
A test coding agent and its skills, hooks, extensions, or Model Context Protocol (MCP) tools appear in an attributable inventory.
Third-party and software as a service (SaaS) AI risk
Understand and monitor AI risk introduced by external software as a service (SaaS), AI providers, embedded AI features, and supplier services.
Framework references
LLM03:2025 · LLM04:2025
Lifecycle
Govern · Identify · Detect
Security requirements with public support
2 requirements with public support
Strong public supportAI-feature discovery in business applications
Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
Public claims reviewed
1
Next question to verify
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.
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.
Review source claims →Limited public supportNon-human identity and service-account security
Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
Public claims reviewed
1
Next question to verify
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
Secure trust, authorization, message flows, tool access, and communication between agents, tools, application programming interfaces (APIs), and external services.
Framework references
LLM06:2025
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.
non-human identity (NHI) and AI-agent identity governance
Manage identities, credentials, privileges, secrets, service accounts, and lifecycle for AI agents and other non-human identities.
Framework references
LLM06:2025
Lifecycle
Govern · Protect · Deploy · Operate
Security requirements with public support
4 requirements with public support
Limited public supportNon-human identity and service-account security
Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
Public claims reviewed
1
Next question to verify
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
Register 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.
Public claims reviewed
1
Next question to verify
A test agent is registered with a unique identity, accountable owner, purpose, and permitted resources.
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.
Review source claims →Strong public supportAI-feature discovery in business applications
Inventory software as a service (SaaS) applications that embed AI features, expose enterprise data to AI capabilities, or create AI-driven data movement.
Public claims reviewed
1
Next question to verify
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.
Attribute AI usage and spend to accountable owners, workflows, agents, models, and business units while enforcing budget, rate-limit, and routing controls.
Framework references
LLM06:2025 · LLM10:2025
Lifecycle
Govern · Operate · Optimize
Security requirements with public support
3 requirements with public support
Strong 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.
Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
Public claims reviewed
1
Next question to verify
A test agent run captures plan, steps, tool calls, outcome, and timestamps.
Review source claims →Limited public supportNon-human identity and service-account security
Inventory, least privilege, credential hygiene, monitoring, and lifecycle management for non-human identities, workloads, service accounts, application programming interface (API) keys, and machine credentials.
Public claims reviewed
1
Next question to verify
A test service account, agent identity, or non-human identity appears in inventory with owner and privileges.
This page organizes research. Audit conclusions, certification assessments, control implementation statements, and vendor endorsements require separate evidence.