RSAC 2026 Innovation Sandbox | ZeroPath: From Alarm Accumulation to Executable Fixes

Why It Matters

The solution tackles enterprise AppSec’s chronic alert fatigue and slow remediation, promising faster, higher‑confidence fixes that directly impact development velocity and risk reduction.

Key Takeaways

• AI engine consolidates four AppSec tool outputs
• Verifies exploitability via reachability analysis
• Generates automated repair suggestions and pull requests
• Targets complex business‑logic and access‑control flaws
• RSAC finalist signals market validation for AI‑native security

Pulse Analysis

Enterprises today drown in a deluge of security alerts generated by disparate SAST, SCA, secrets and IaC scanners. Each tool produces its own report, leaving security teams to manually correlate findings, assess real‑world exploitability, and hand off remediation tasks to developers. This fragmented workflow not only inflates noise but also slows the critical path from detection to fix, especially for business‑logic vulnerabilities that require contextual understanding of code semantics and access controls.

ZeroPath’s answer is a hybrid AI‑augmented analysis pipeline. It first extracts an enriched abstract syntax graph from the code base, then applies deterministic static analysis to map data flows, authentication checks, and dependency usage. LLMs supplement this graph with semantic rules that capture business‑logic constraints, enabling AI‑driven reachability analysis to filter out dead code and non‑exploitable paths. The platform then auto‑generates concise remediation suggestions and creates ready‑to‑merge pull requests, effectively closing the loop between security findings and developer action while preserving code integrity.

The market implications are significant. As major players like GitHub Copilot Autofix and Anthropic’s Claude Code Security introduce automated repair features, ZeroPath’s unified, end‑to‑end approach positions it as a direct competitor that promises lower false‑positive rates and broader coverage of complex vulnerabilities. Adoption will hinge on language support, integration with CI/CD pipelines, and robust verification to avoid regressions. Nonetheless, its RSAC finalist status underscores investor confidence and signals a broader industry move toward AI‑native, executable AppSec solutions that can keep pace with the rapid rise of AI‑generated code and escalating security debt.