Theori is unveiling 'Zint Code,' a Static Application Security Scanning (SAST) tool that uses AI to analyze large-scale software codebases. By adding internal code analysis capabilities to existing web security scanning solutions that detect external threats, the company has introduced a structure that enables enterprises to respond seamlessly, from checking attack scenarios to identifying the root causes of vulnerabilities.

Theory announced that it will unveil 'Zint Code' for the first time at the RSA Conference 2026, held in San Francisco, USA, from March 23 to 26. The company introduced the product as an LLM native-based SAST tool that operates on large-scale source code, configuration files, and binaries.

The key feature of Zint Code is that it is designed not only to compare against predefined rules but also to interpret the code's design intent, context, and business logic. According to Theory, this tool can fully scan millions of lines of code and related files within 12 hours, based on multi-LM reasoning and AI agent orchestration technologies. The analysis results present not only whether vulnerabilities exist but also how attackers can exploit them and how far the impact can extend.

Such capabilities have significant potential for use in cloud, platform, and enterprise service environments that operate large-scale software. Development organizations can identify vulnerabilities requiring priority action, while security organizations can broaden their response scope by examining both external attack surfaces and internal code flaws. Theori explained that they actually utilized this tool to discover a long-undiscovered vulnerability in the open-source database PostgreSQL.

The company plans to expand its security framework by integrating its existing black-box web security inspection solution, 'Jint Web,' with Jint Code. The vision is to enhance the continuity of corporate security operations by combining DAST, which detects web service threats from the perspective of external attackers, with SAST, which tracks defects at the internal source code level.

As AI-powered attack and defense technologies advance simultaneously, the ability to interpret context and determine priorities is becoming more important for security tools than simple detection. Theori's recent announcement is interpreted as an example demonstrating that AI security technology is expanding in earnest to the code analysis stage.