SAST Scan Results
Static application security testing findings across major projects — training data for vulnerability detection AI.
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Find Me This Data →Overview
What Is SAST Scan Results Data?
Static Application Security Testing (SAST) scan results are security findings generated by automated tools that analyze source code to identify vulnerabilities in first-party code written by development teams. SAST tools examine code at various stages of the software development lifecycle (SDLC)—from IDE plugins to CI/CD pipelines—to discover security flaws such as insecure coding practices, backdoors, and misconfigurations before code reaches production. These scan results serve as critical training data for building and improving vulnerability detection AI models, enabling machines to recognize patterns of insecure code and predict security risks with greater accuracy. The SAST market reflects growing enterprise demand for early-stage security integration, driven by increasingly sophisticated cyber threats, regulatory pressures, and the acceleration of DevOps and DevSecOps methodologies that mandate continuous security throughout development workflows.
Market Data
$3.8 billion
Global SAST Tool Market Size (2025)
Source: DataIntelo
$9.6 billion
Projected Market Size (2034)
Source: DataIntelo
10.8%
SAST Market CAGR (2025-2034)
Source: DataIntelo
22.82%
SAST Market CAGR (2025-2030)
Source: Mordor Intelligence
10-23%
Typical Application Code Composition (First-Party)
Source: Konvu
Who Uses This Data
What AI models do with it.do with it.
Enterprise Security Teams
Organizations use SAST scan results to identify vulnerabilities in proprietary code written by internal development teams, integrate security testing into CI/CD pipelines, and maintain compliance with regulatory frameworks in industries like BFSI, healthcare, and government.
AI/ML Model Developers
Security researchers and ML engineers train vulnerability detection algorithms on SAST scan results to teach models to recognize patterns of insecure code, predict new vulnerability types, and improve detection accuracy across diverse codebases and programming languages.
DevSecOps and Platform Teams
Development operations teams leverage SAST findings to shift security left, embedding automated code scanning directly into developer workflows, providing real-time feedback via IDE plugins and PR reviews, and reducing the time between code commit and vulnerability detection.
AppSec Tool Vendors
Security tool companies use aggregated SAST scan results to benchmark tool performance, improve rule engines, validate detection capabilities against real-world codebases, and enhance multi-tool orchestration platforms that coordinate SAST with complementary testing methods like SCA and DAST.
What Can You Earn?
What it's worth.worth.
Individual Scan Results (Limited Dataset)
Varies
Pricing depends on code volume, number of vulnerabilities, programming languages covered, and exclusivity terms. Smaller datasets with fewer findings typically command lower value.
Enterprise Scan Dataset (1000+ Projects)
Varies
Large, curated collections of SAST findings across diverse industries and codebases attract higher valuations from AI training companies and security vendors seeking representative training data.
Anonymized & Compliance-Ready Data
Varies
SAST results that have been de-identified, sanitized of proprietary logic, and verified for GDPR/regulatory compliance command premium pricing from institutional buyers.
Real-Time Streaming Scan Data
Varies
Continuous feeds of fresh SAST findings from active development pipelines are valued higher than static datasets due to their relevance for training models on emerging vulnerability patterns.
What Buyers Expect
What makes it valuable.valuable.
Accuracy and False Positive Rates
Buyers require SAST scan results with documented accuracy metrics and low false positive rates. Results must be traceable to specific code locations with clear vulnerability descriptions, severity classifications, and remediation guidance to ensure training data reliability.
Code Diversity and Language Coverage
Datasets should span multiple programming languages (Java, Python, C/C++, JavaScript, etc.), application types (web, mobile, cloud-native), and industry verticals (BFSI, healthcare, retail, IT/telecom, government) to train models that generalize across diverse development environments.
Metadata and Contextual Enrichment
Scan results must include comprehensive metadata: CVSS scores, CWE/OWASP classifications, code snippets, commit history, developer context, tool versions used for scanning, and timestamp information. This enrichment enables sophisticated AI training and historical trend analysis.
Privacy, Anonymization, and Compliance
All proprietary business logic, API keys, hardcoded credentials, and personally identifiable information must be removed or sanitized. Data must comply with GDPR, CCPA, and industry-specific regulations (HIPAA for healthcare, PCI-DSS for payment systems) with clear data provenance and licensing.
Volume and Continuity
Institutional buyers prefer datasets with sufficient scale (1000+ projects minimum) and ongoing access to fresh scan results. Consistent, predictable data updates enable continuous model improvement and reduce risk of training on stale vulnerability patterns.
Companies Active Here
Who's buying.buying.
Developer-focused AppSec platform using SAST findings to train vulnerability detection models and provide inline PR feedback for first-party code issues.
Lightweight SAST engine with customizable rules powered by machine learning; trains on scan result patterns to improve code analysis across CI/CD pipelines.
SAST orchestration platform that integrates multiple scanning tools and uses aggregated findings for centralized policy control and ML-driven security insights.
Enterprise SAST tool vendor analyzing vulnerability patterns from scan results to enhance detection rules and benchmark tool performance across industries.
All-in-one scanning platform combining SAST with SCA and IaC, leveraging SAST results for AI-driven threat prioritization and zero-config integration.
FAQ
Common questions.questions.
How do SAST scan results differ from other application security testing data?
SAST scan results specifically analyze source code written by development teams (first-party code, typically 10-23% of an application). They differ fundamentally from SCA (Software Composition Analysis) findings, which identify vulnerabilities in third-party dependencies comprising 77-90% of typical codebases. SAST and SCA are complementary tools covering different attack surfaces. SAST finds logic flaws and insecure coding practices in custom code, while SCA inventories known vulnerabilities in imported libraries. Other testing methods like DAST (dynamic testing) and IAST (interactive testing) examine running applications, whereas SAST analyzes code statically without execution.
What makes SAST scan results valuable for training AI vulnerability detection models?
SAST scan results provide labeled examples of vulnerable code patterns alongside their classifications (CWE/OWASP category, severity, remediation steps). This structured ground truth enables supervised learning—training ML models to recognize syntactic and semantic patterns of insecure coding across programming languages. The diversity of real-world codebases in SAST datasets helps models generalize to detect novel vulnerability variants. As applications grow in complexity and AI-assisted code generation accelerates vulnerability creation, SAST findings supply the continuous, representative training data needed to keep detection models current with emerging threat patterns.
What privacy and compliance considerations apply to selling SAST scan results?
SAST findings must be thoroughly anonymized and sanitized before commercialization. All proprietary business logic, hardcoded credentials, API keys, database connection strings, and personally identifiable information must be removed. Data must comply with GDPR (regarding EU subjects' code), CCPA (California residents), HIPAA (healthcare code), PCI-DSS (payment systems), and SOC 2 standards. Organizations should obtain explicit written consent from code owners and provide clear licensing terms. De-identification must be verified by independent auditors to ensure neither the original code nor business secrets can be reverse-engineered from the sanitized dataset.
How is the SAST market growing, and what drives demand for scan result data?
The SAST tool market is valued at $3.8 billion in 2025, projected to reach $9.6 billion by 2034 at a 10.8% CAGR. More aggressively, the dedicated SAST market alone grows at 22.82% CAGR (2025-2030). Key drivers include escalating cyber threat sophistication, regulatory compliance pressures (GDPR, HIPAA, SOX), rapid adoption of DevOps/DevSecOps methodologies requiring continuous security integration, and rising demand from enterprises to shift security left in the development lifecycle. As organizations scan more code faster, the volume of SAST findings creates abundant training data that AI companies, tool vendors, and security researchers aggressively acquire to improve detection models and competitive tool capabilities.
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