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Kodem makes static analysis smarter with AI use and adding runtime context. We use a blend of deterministic and LLM-based analysis to identify and confirm code vulnerabilities, then ground those findings in runtime evidence to show what actually runs, is reachable, and matters in production.
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Kodem harnesses its runtime expertise to release one of the strongest SAST offerings in the market. Finally, we can get real results.
We correlate vulnerable functions to real-world activity in your environment. Whether they were executed in production, which process loaded them, and how often. This is how you shift from "possible" to "provable" risk.
Kodem confirms which flagged functions are loaded, executed, and reachable from real input in production, so your team fixes provable risk and skips the dead or unreachable code that inflates SAST output.
Kodem maps runtime behavior with function traces, file open events, and symbol resolution across Java, Node.js and TypeScript, Python, Go, Rust, C and C++, Ruby, and Scala.
Once a function is observed running in production, Kodem keeps that context across scans, builds, and environments, so signal is never lost between releases.
Kai confirms which findings are true positives using application context, then generates the fix as a ready-to-review pull request. It never modifies your repo or trains on your code.
Kodem's SAST is powered by Opengrep, the open-source engine Kodem co-founded, with 1,000+ maintained rules across injection, deserialization, insecure cryptography, and exposed secrets including git history.
Findings surface in the IDE, in pull-request checks, and in CI/CD policy gates across GitHub, GitLab, Azure DevOps, and Bitbucket, with suppression at line, file, or project level.
Runtime-Aware AI SAST is a category of Static Application Security Testing that pairs source code analysis with live runtime execution data. Instead of flagging every theoretical vulnerability in your codebase, a Runtime-Aware AI SAST platform confirms which vulnerable functions actually execute in production. The result is a prioritized list of provable risks rather than thousands of findings most of which are unreachable.
Traditional SAST tools scan source code for patterns that look vulnerable. They produce findings without context, so engineering teams triage thousands of theoretical issues that may never run. Runtime-Aware AI SAST adds execution data: function-level traces, file open events, and symbol mapping. It confirms which flagged code is loaded, which functions are invoked, and which data paths are reachable in production. That filtering is what makes the output actionable.
Most SAST false positives come from flagged code that is dead, unreachable, or never invoked in production. Runtime-Aware AI SAST eliminates those by correlating each finding to actual execution data. If the function never runs and no input ever reaches it, the finding is filtered out. Kodem removes the large majority of SAST false positives this way.
Kodem's Runtime-Aware AI SAST platform does not. It uses out-of-band runtime sensors that observe function-level execution without modifying application code, recompiling, or restarting services. This is the operational difference from RASP-style approaches that require in-app instrumentation.
Kodem supports Java (Spring, Quarkus, Jakarta EE), JavaScript and TypeScript (Node.js, Deno, Bun), Python (Django, Flask, FastAPI, AI/ML workloads, CPython, PyPy), Go (containerized services), Rust (Tokio, threaded workloads), C and C++ (DWARF symbol resolution), Ruby (Rails, Sinatra), and Scala (Akka, Play, Spark). New runtimes are added regularly.
Yes. Once a function is observed running in production, Kodem maintains that runtime context across subsequent scans, builds, and deployments. Findings remain flagged until they are resolved, so signal does not get lost between releases.
Legacy SAST tools rank findings by severity scores and pattern matches, with no awareness of whether the code actually runs. Kodem ranks findings by runtime exploitability: is the function loaded, is it executed, is the call path reachable from user input, and is the exposure present in this specific environment? That context-aware model is what cuts noise and surfaces the small set of vulnerabilities engineering should fix first.