Carding tests thousands of stolen credit cards against your payment flow. Learn why WAFs struggle to stop it and why detection must move into the request path.

Most teams detect credential stuffing but can't enforce against it. Here's why WAF rules fail and what changes when detection moves inline.

Most security teams detect threats but never block them. Impart's Programmable Bot Protection brings runtime detection and enforcement together, inside the application.

We’ve delivered a major round of upgrades across the Impart platform, introducing new AI Bot/MCP and LLM Protection dashboards, a refreshed and more intuitive App Experience, a high-performance Inspector v0.42.0 release, expanded Inspector Metrics for deeper operational visibility, and new SQLi and XSS version control, allowing teams to choose between Detection Version 1, Version 2, or always use the latest release. These updates make it easier than ever to understand AI-driven traffic, configure protections with clarity, manage detection behavior with precision, monitor system performance, and optimize your entire Impart deployment.

AI-driven “vibe coding” is transforming software development by generating large amounts of new code quickly, but without the reviews, scans, or guardrails that once caught vulnerabilities early. This pace widens the gap between attackers who now use LLMs to build exploits in minutes and defenders still slowed by legacy tools and days-long workflows. The result is a rapidly growing, poorly governed attack surface and a surge in semantic attacks that signature-based WAFs cannot understand or stop. In the AI era, the old AppSec model breaks; security must shift to adaptive, runtime control that can match the speed and complexity of modern development.

This month, we’ve released major upgrades that give security teams tighter control over exception handling, better visibility into API data exposure, and smoother day-to-day navigation across Impart. These enhancements help teams reduce risk faster—without slowing down delivery.