Impart Resources

Runtime Layer 7 virtual patching lets security teams close the gap between vulnerability disclosure and code-level fixes, blocking exploits at the request level without waiting on release cycles.

Complete runtime protection detects and blocks attacks across every surface a modern application exposes—MCP servers, LLMs, AI agents, APIs, and the web layer—at the moment of execution. This post breaks down what "complete" actually means, which attack surfaces it covers, and what to look for in a platform.

Prompt sanitization addresses the input layer. Active runtime enforcement addresses the execution layer. For AI agents with access to private data and external channels, only one of those stops a successful attack.

Security engineering teams have spent years hardening application APIs. Auth tokens, rate limits, input validation; the fundamentals are well understood. But when you place an LLM behind that same API surface, the threat model changes in ways standard application security controls weren't built to handle. The endpoint doesn't just serve data anymore. It executes intent.

The AI security market is moving fast to respond to predictions that over 40% of enterprise applications will have embedded AI agents by the end of 2026. Every vendor is packaging their answer under the label "AI agent security." The problem is that "AI agent security" means something different depending on who's selling it. This post defines what each layer does, where each one runs out of road, and why the architecture needs both.

LLM security and LLM protection get used as synonyms across vendor briefings and CISO conversations. They describe fundamentally different capabilities. This post breaks down what each layer actually covers, where each earns its place in a production LLM defense architecture, and where each leaves teams exposed when an attack executes.