SAST Essentials for AI-Generated Code
Snyk handbook
A security leaders handbook
Understanding the challenges of securing AI-generated code in your development organizations
The adoption of AI tools in software development is happening at an unprecedented rate, twice as fast as the early internet boom. Whether you’re aware of it or not, odds are that AI-generated code is already in use across your organization. In fact, according to Snyk’s 2023 AI-Generated Code Security Report, 96% of development teams regularly use AI coding tools. If that statistic doesn’t give you pause — it should. Despite its many benefits, rapid integration poses unique security challenges, making robust measures essential to handle the surge in vulnerabilities introduced by AI-generated code.
What exactly are these challenges? To start, AI-generated code often harbors major security flaws. A recent study found that 26% of Common Weakness Enumerations (CWEs) in GitHub Copilot-created code were among the top 25 vulnerabilities of 2022, and 36% of this code contained vulnerabilities. Moreover, developers using AI assistants were more likely to believe their code was secure, even when significant security issues were present. This misplaced confidence can and will increase risks if not properly managed.
96%
of development teams regularly use AI coding tools
26%
of CWEs in GitHub Copilot-created code were among the top 25 vulnerabilities of 2022
60%
of AI coding tool usage is unauthorized
Why SAST? An introduction to SAST as GenAI-created code security
The primary challenge with AI-generated code is similar to traditional code—identifying and mitigating vulnerabilities. However, the key difference is the volume and speed at which AI tools produce code, which can lead to a significant increase in potential security issues. While AI is the culprit for these issues, it can also be the solution. This is where Static Application Security Testing (SAST) tools come in. These purpose-built tools act as AI guardrails, providing real-time security checks and fixes within the development workflow. While familiar security processes—such as policies, testing, and developer reviews—remain crucial; they need to be scaled to keep pace with AI coding tools' rapid introduction of vulnerabilities.
By integrating Snyk Code into their development environments and pipelines, one customer slashed their vulnerability remediation time by 84%, from 88.8 days to just 13.89 days in six months.
Gaps in legacy SAST and the need for modern tools
Legacy SAST tools often have a hard time keeping up with the fast-paced demands of modern development environments. They usually lack the advanced technology needed for quick setup and analysis, which means implementation can drag on for months. Once deployed, these tools come in disruptively late in the software development cycle, and can still be frustratingly slow, with scans taking up to hours to complete. This not only causes delays through waiting times and teams reactively hunting down vulnerabilities through the pipeline but also frustrates development teams, leading to lower adoption rates.
Newly developed, free, or open-source SAST solutions, while faster than legacy solutions, often compromise on depth and accuracy of analysis. This trade-off results in numerous false positives and, more worryingly, false negatives, eroding the return on investment (ROI) from these tools.
Modern SAST tools, like Snyk Code, overcome these limitations with several key benefits:
Speed and accuracy
Modern SAST tools like Snyk Code do not require code to be built or compiled, providing real-time scanning and remediation within the IDE, and enabling developers to address vulnerabilities as they code.
Ease of set-up
Quick and easy integration into existing workflows encourages higher adoption rates, ensuring consistent security practices.
Comprehensive coverage
These tools support a wide range of programming languages, IDEs, CI/CD tools, and offer flexible functionality through APIs as well as CLIs, providing extensive security coverage without disrupting workflows.
Security trust
Leveraging advanced AI and machine learning, modern SAST tools maintain high accuracy and relevance by continuously learning from vast open-source libraries and security experts.
When choosing a SAST tool to secure AI-generated code, consider the following features:
Key Requirements for Selecting a SAST Solution
Scans and
Fixes
Automatic fixes: Does this solution offer automatic fixes for detected vulnerabilities, so that remediation efforts do not slow down AI productivity gains?
Protection from AI libraries: Can this solution find insecure LLM sources in your code?
Educational explanations: Does this solution provide clear explanations for identified risks to help developers understand and learn from security issues?
A
B
C
Depth of
Analysis
Ease of
Set-up
Real-time Scanning: Does this solution run in the IDE for real-time scanning and proactive remediation? Does it have full IDE support capabilities across multiple languages?
A
B
Multi-model AI-powered Detection: Does this solution use hybrid AI models for accurate detection of AI-generated vulnerabilities without productivity bottlenecks?
Speed and
accuracy
Security-focused AI: Does this solution use AI specifically trained on security data? Is the AI fine-tuned by security experts to provide deep code visibility and accurately identify vulnerabilities, reducing false positives?
A
IDE Integration: Does this solution offer easy set-up with IDE plugins for rapid deployment and consistent security scaling across development teams?
A
Program Visibility
Proven Track Record
Future-proofing
Consolidated Overview: Does this solution provide a unified view of the entire security program, allowing for effective prioritization of findings and decision-making? Can you track trends across your program with this tool?
A
B
ROI Measurement: Does this solution show you gaps in the security program and help you to demonstrate a return on investment for security tools?
Battle-tested Solutions: Does this solution come from a vendor with extensive experience at the intersection of cybersecurity and AI, ensuring valuable data insights that translate into personalized features and impactful automation?
A
AI-mature Vendor: Does this solution come from a vendor with a long-term vision and commitment to securing emerging technologies, and ensuring adaptability to future AI advancements?
A
Implementing a SAST solution with the features outlined above can significantly alleviate the AI-related risks and uncertainties faced by CISOs and security teams. Look for a tool that not only integrates smoothly with developer workflows but is also purpose-built for security, reliably mitigating the risks associated with AI-generated code and building trust in your security measures.Is your organization prepared to secure AI-generated code with a state-of-the-art SAST solution? Snyk Code offers real-time scanning, automatic fixes, and comprehensive program visibility to help you stay ahead of potential vulnerabilities while enhancing developer productivity. By seamlessly integrating security into your development process, Snyk Code ensures you don’t have to settle for “good enough.â€Choose a solution that provides speed, accuracy, and deep analysis, backed by extensive experience in both cybersecurity and AI. Safely adopt AI with Snyk, who is recognized as a Gartner Magic Quadrant Leader for SAST. With four years at the forefront of cybersecurity and AI, Snyk Code is a pioneering hybrid AI SAST tool that is developer-friendly, fast, and accurate. It offers actionable results with in-IDE scans and in-line one-click remediation.
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Snyk’s AI Readiness Report highlights another critical issue: over 60% of AI coding tool usage is unauthorized, leading to what is termed 'Shadow AI'. This phenomenon poses significant risks as these unmonitored tools can introduce undetected vulnerabilities. Although insecure code is not a new issue, and AppSec teams have always been heavily outnumbered by developers, now—with developers armed with AI coding tools that create insecure code at previously impossible speeds and scale—addressing the volume and velocity of security issues has become more pressing than ever before.