Solution approach
AI API and web application protection
Find and protect AI-related application programming interfaces (APIs), model endpoints, tool servers, and agent traffic.
Use-case context
How this approach relates to the selected use case
Applications and agents: Additional if: System uses APIs, tool servers, or web apps — The system exposes or uses APIs, model endpoints, tool servers, web applications, or agent-to-tool traffic that application or API security controls can inspect.
View research coverageThese counts describe available public research, not product quality or suitability.Expand
- Vendor profiles
- 9 shown in this view
- Security requirements
- 3 to review
- Strong public support
- 19 requirement records
- Limited public support
- 5 requirement records
- Open research
- 3 with no supporting claim · 0 incomplete
Evaluation guide
What to verify for this use case
Generative 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.
- A test large language model (LLM) application event records prompt, application programming interface (API), model, retrieval, or tool interaction context.
- A prompt-injection or unsafe-output test is detected, blocked, or flagged by the guardrail or large language model (LLM) firewall.
- The evidence links the event to an application, endpoint, request identifier, or workflow.
AI 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.
- A model, agent, tool, or Model Context Protocol (MCP) request passes through a named policy enforcement point.
- A test policy allows, blocks, transforms, redirects, or rate-limits the request with an explicit reason.
- The audit event records caller identity, destination, tool or model, policy decision, and timestamp.
Action-taking agent monitoring
Observe and govern agent plans, memory, tool calls, delegated tasks, autonomy, runtime decisions, and outcomes.
- A test agent run captures plan, steps, tool calls, outcome, and timestamps.
- Agent memory, delegated task, autonomy, or runtime decision detail is visible in a timeline or log.
- Out-of-policy or unusual agent behavior can be flagged or filtered for investigation.
Vendor research
Vendors with public research for this approach
Only requirements explicitly connected to the selected use case are shown. Vendors are grouped by whether this approach is a core product focus or related coverage, then ordered by documented support across the requirements shown. This organizes public research coverage; it is not a product ranking or recommendation.
Company maturity remains a filter and profile attribute; it does not affect the research-coverage order.