Protect all your agents, even the ones you just found out about

AI-native application security is the practice of securing applications built with AI components such as LLMs, MCP servers, and third-party GenAI services. It requires runtime visibility into AI APIs, data flows, and model behavior to detect threats specific to AI systems — unlike traditional approaches focused solely on code vulnerabilities.

The AI blind spot refers to undiscovered AI components deployed without security team awareness. 62% of security practitioners say they have no way to tell where LLMs are in use across their organization, creating exploitable gaps that attackers can target.

Discovery requires continuous monitoring of runtime traffic to identify every LLM, MCP server, and GenAI service. Runtime API traffic analysis can detect AI assets as they appear — including shadow AI that was never formally inventoried.

MCP security protects connections between MCP servers, clients, tools, and resources. MCP servers represent a significant and often overlooked attack surface that requires continuous monitoring and vulnerability assessment.

AI-SPM continuously assesses the security posture of AI assets including authentication, encryption, exposure, and data flows, providing a risk-based view of your AI attack surface.

Prompt injection inserts malicious instructions into LLM inputs. Protection requires real-time inspection of prompts and model responses at the API layer to detect and block malicious inputs before they reach the model.

All AI components communicate via APIs. Without deep visibility into runtime API traffic, security teams cannot discover all AI assets, monitor their behavior, or detect threats in real time.

AI security adds runtime protection layers that traditional tools lack. Static code analysis alone cannot detect threats like prompt injection, LLM jailbreaking, or sensitive data leakage through AI APIs.

Compliance requires automated discovery, risk scoring, and policy enforcement. Out-of-the-box compliance policies aligned with frameworks like the OWASP LLM Top 10 provide a starting point for governance.

LLM security protects models from prompt injection, jailbreaking, data leakage, and overconsumption. It requires visibility into every API connection to and from the model and continuous monitoring of model inputs and outputs.