AI Security

AI-native application security is the practice of securing applications that are built with AI components such as LLMs, MCP servers, and third-party GenAI services. Unlike traditional application security, which focuses on code vulnerabilities, AI-native application security requires runtime visibility into AI APIs, data flows, and model behavior to detect and respond to AI-specific threats.

The AI blind spot refers to AI components — such as LLMs, AI APIs, and MCP servers — that are deployed or connected to enterprise applications without the knowledge of security teams. According to Harness's State of AI-Native Application Security report, 62% of security practitioners say they have no way to tell where LLMs are in use across their organization, leaving dangerous blind spots that attackers can exploit.

Discovering AI assets requires continuous monitoring of runtime traffic to identify every LLM, MCP server, and third-party GenAI service communicating within your application environment. Unlike static scanning tools, runtime API traffic analysis can detect AI assets as they appear — including shadow AI that was never formally inventoried.

MCP (Model Context Protocol) security refers to the practice of securing the connections between MCP servers, clients, and the tools and resources they expose. As AI agents increasingly rely on MCP to connect to data sources and external services, MCP servers represent a significant and often overlooked attack surface that requires continuous monitoring and vulnerability assessment.

AI security posture management (AI-SPM) is the practice of continuously assessing the security posture of AI assets in your environment, including their authentication status, encryption, internet exposure, and sensitive data flows. AI-SPM gives security teams a risk-based view of their AI attack surface, helping them prioritize remediation based on the assets that pose the greatest risk.

Prompt injection is an attack where malicious instructions are inserted into an LLM's input — either directly through a user interface or indirectly through content the model retrieves — causing it to take unintended actions or expose sensitive data. Protection requires real-time inspection of prompts and model responses at the API layer to detect and block malicious inputs before they can cause harm.

Every AI component — from LLMs to MCP servers and third-party GenAI services — communicates through 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 tools that lack an API security foundation will inevitably miss assets and threats that only become visible at the network layer.

Traditional application security focuses primarily on finding vulnerabilities in code before deployment, using techniques like SAST and SCA. AI security requires an additional layer of runtime protection — continuously monitoring how AI components behave in production, what data flows through them, and whether they are being manipulated by attackers. Static code analysis alone cannot detect threats like prompt injection, LLM jailbreaking, or sensitive data leakage through AI APIs.

Ensuring AI compliance requires a combination of automated discovery to maintain an accurate inventory of AI assets, risk scoring to identify non-compliant assets, and policy enforcement to ensure every AI component meets your organization's security standards. Out-of-the-box compliance policies aligned with frameworks like the OWASP LLM Top 10 provide a starting point, while custom policies allow organizations to enforce their own unique requirements.

LLM security refers to the practice of protecting large language models and the applications built on them from threats including prompt injection, jailbreaking, sensitive data leakage, and unbounded consumption. Effective LLM security requires visibility into every API connection to and from the model, continuous monitoring of model inputs and outputs, and runtime protection to detect and block malicious activity before it causes harm.