Appknox Launches KnoxIQ, an AI Co-Pilot to Prioritize Real-World Exploitability in Application Security

New AI-native platform validates vulnerabilities, prioritizes real risk and delivers contextual remediation code inside developer workflows

Appknox, a leading provider of cutting-edge mobile app security solutions, launched KnoxIQ, an AI-native vulnerability assessment capability that introduces a new prioritization and remediation layer that sits between detection and development. The technology transforms raw vulnerability findings into validated, prioritized and developer-ready intelligence.

As AI-assisted development increases the volume and complexity of vulnerabilities, the harder problem for security teams has become identifying which ones represent real risk (and resolving them quickly). The launch of KnoxIQ addresses this core challenge for security teams.

Industry reports indicate that AI-generated code often produces 1.7x more security issues than human-written code. At the same time, traditional workflows are not designed for the speed of modern engineering. Static severity labels such as “high” or “critical” often fail to reflect real-world exploitability, leading teams to deprioritize issues that actually matter.

“Most traditional application security tools stop at vulnerability detection and reporting,” said Harshit Agarwal, CEO of Appknox. “KnoxIQ goes a step further by helping teams understand what’s actually exploitable and how to prioritize and remediate risk.”

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Key capabilities of KnoxIQ include:

• Direct integration into AI-native developer tools such as Cursor and Claude Code enables developers to act on issues within their existing workflows.
• Exploitability-based prioritization replaces static severity scoring with AI-driven analysis that ranks vulnerabilities based on real-world risk.
• Automated AI validation of findings reduces false positives and eliminates the need for manual reproduction.
• Proof-of-concept (PoC) generation helps teams quickly understand and confirm vulnerabilities.
• Contextual, application-specific remediation code replaces generic guidance with ready-to-use fixes tailored to each issue.

A key differentiator of Appknox’s approach is its binary-to-remediation model, which analyzes compiled applications based on runtime behavior rather than static code alone. This improves detection accuracy and creates a stronger link between vulnerabilities and actionable fixes.

“By grounding security findings in real application behavior, KnoxIQ eliminates noise and delivers fixes developers can immediately use,” said Raghunandan J, Head of Product at Appknox.

The result is a continuous loop from detection to remediation, helping organizations reduce time to fix and improve security posture.