Beyond the Hype: The Veracode AI-Advantage in Application Security

For years, the cybersecurity industry has hyped AI as a game-changer, but what vendors often delivered was basic machine learning driven or simple predefined rules. The rise of ChatGPT and similar tools dramatically reshaped the landscape, prompting vendors to hastily identify real AI use cases in their offerings. 

Veracode has been at the forefront of the AI revolution in application security with Fix, our pioneering AI-powered remediation tool that’s been proven to help developers speed up the remediation process. This use case is of critical importance, as AI-generated code introduces security flaws in 45% of test cases. 

Not all AI is created equal when applying it to cybersecurity and code flaw fixes. It’s helpful to first understand the different types of AI learning capabilities, to see the power of Veracode’s approach. 

Understanding Classical Machine Learning  

AI’s learning approach is rooted in classical machine learning and shapes its effectiveness in securing applications. Different approaches shape how effectively AI can detect threats, analyze code, and protect applications. Let’s dive into the three primary ways AI systems learn, illustrating their core mechanisms and relevance to cybersecurity and application security testing (AST). 

Supervised Learning: Learning from a Teacher 

Supervised learning operates by training AI models on labeled datasets. This means every piece of input data (like a code snippet or an email) comes with a corresponding correct output, or “label,” such as “vulnerability found” or “this is spam.” The AI learns to map these inputs to their predefined outputs, much like a student studying a textbook with an answer key.  

• Core Mechanism: The model develops a function that accurately maps inputs to desired outputs by minimizing discrepancies between its predictions and the provided labels during training. 
• Cybersecurity Applications: 
  • Classifying malware types based on file signatures or behaviors. 
  • Identifying phishing emails by recognizing patterns in their content and sender information. 
  • Estimating vulnerability severity within software by analyzing code characteristics and historical data. 

Unsupervised Learning: Learning by Discovery 

In contrast to supervised learning, unsupervised learning deals with unlabeled data. Here, the AI model is given raw data and tasked with finding hidden patterns, structures, or relationships within it entirely on its own, without any explicit guidance or correct answers provided. Think of it like a security analyst sifting through massive amounts of network traffic logs, trying to find anything that looks “out of place” or naturally groups together, even without a predefined list of “bad” behaviors. 

• Core Mechanism: The model identifies inherent groupings (clustering), reduces data complexity (dimensionality reduction), or detects outliers (anomaly detection) based on the statistical properties of the data itself. 
• Cybersecurity Applications: 
  • Detecting abnormal network traffic patterns that could signify an intrusion or denial-of-service attack. 
  • Grouping similar user behaviors to identify potential insider threats or compromised accounts. 
  • Simplifying complex security datasets to reveal underlying relationships or reduce noise for further analysis. 

Self-Supervised Learning: Learning from Itself 

Self-supervised learning represents an increasingly prominent and powerful approach, especially for leveraging vast, unlabeled datasets. It operates by having the AI model generate its own “labels” or supervisory signals directly from the unlabeled input data itself. The model learns by solving a “pretext task,” which is a task designed to force the model to understand the underlying structure and relationships within the data. 

• Core Mechanism: The model transforms unlabeled data into a supervised-like problem (the “pretext task”). For example, it might be given a block of code with a missing snippet and tasked with predicting the missing part, or it might learn to predict the next word in a sequence. By repeatedly solving these internally generated puzzles, the model learns rich, general-purpose representations of the data.  
• Cybersecurity Applications: 
  • Pre-training models on colossal security codebases to learn the grammar, common patterns, and stylistic nuances of secure and insecure programming. 
  • Developing foundational models that possess a deep contextual understanding of security alerts, logs, and documentation by learning to predict masked or missing elements within these datasets. 

Veracode’s Proprietary AI: A Tailored Solution 

Veracode Fix uses fine-tuned AI models to generate precise code patch suggestions for vulnerabilities detected by our Static Application Security Testing (SAST) engine. Veracode’s approach focuses on targeted fine-tuning and manually curated reference patches.  

Here’s why it excels: 

1. Precision and Accuracy 

Veracode’s proprietary AI generates code patches for vulnerabilities identified by Veracode’s SAST engine, leveraging a curated dataset of expert-validated fixes and billions of analyzed code lines. 

The Veracode Advantage: Unlike general-purpose AI, Veracode models are tailored to produce accurate, context-aware code patches, reducing false positives and accelerating remediation with developer-ready suggestions. 

2. Proprietary Data and Expertise 

Veracode’s AI draws on: 

• Millions of Scan Results: Unique insights from building market-leading security tools. 
• Validated Reference Patches: Expert-curated patches ensure best practices. 
• Language Detection: Understands programming languages used in the codebase for relevant fixes. 

The Veracode Advantage: Unlike tools relying on public data, Veracode’s proprietary dataset and expertise deliver practical, validated patches integrated into development workflows. 

3. Responsible by Design 

Veracode prioritizes trust and transparency: 

• Explainability: Code patch suggestions are clear and tied to detected vulnerabilities. 
• Human Oversight: Veracode analysts review patches, ensuring accuracy and model refinement. 
• Bias Mitigation: Active efforts ensure fair, secure recommendations with multiple options developers can choose from. 

The Veracode Advantage: Built for security and reliability, our AI fosters developer trust, unlike general tools prioritizing speed over accuracy. 

4. Actionable Remediation 

Veracode’s AI generates concrete code patches for vulnerabilities, speeding up remediation and reducing technical debt. 

The Veracode Advantage: By focusing on targeted patch generation, Veracode outperforms broader tools, providing developers with practical, secure solutions. 

While the broader AI landscape is overwhelming, when it comes to the task of securing your applications, specialized, responsible, and data-driven AI is critical.  

Veracode Fix is a testament to this principle, providing developers with accurate, trustworthy, and actionable guidance to build secure software faster. 

Reach out to learn more about taking advantage of this power, reliable AI use case.