## The Basics of Secure AI Code Generation A new era of development requires new security standards ### Table of Contents - [How AI code generation works](#how-ai-code-generation-works) - [The security landscape of AI-generated code](#the-security-landscape-of-ai-generated-code) - [The hidden security tradeoff](#the-hidden-security-tradeoff) - [The case for a hybrid security approach](#the-case-for-a-hybrid-security-approach) - [Why every business needs resilient AI code security](#why-every-business-needs-resilient-ai-code-security) - [How to evaluate AI coding tools for security](#how-to-evaluate-ai-coding-tools-for-security) - [The evolution of AI coding](#the-evolution-of-ai-coding) The way we build and secure software has radically changed. AI code generation has reshaped the software development life cycle (SDLC), from initial architecture to final deployment. The SDLC can be a long, complex process, but AI coding tools promise faster development cycles, increased productivity, and more consistent codebases. According to our recent report, “ [The State of AI-Powered Software Development](/content/resources/analyst-reports/state-of-ai-powered-software-development.html),” most development teams are embracing AI coding tools and seeing impressive benefits. 0% ###### Of developers are actively using AI coding assistants 0% ###### Of teams see improved productivity and code velocity 0 hours ###### Of time is saved by developers each week ### How AI code generation works Popular AI tools like ChatGPT and Perplexity are trained on broad datasets. AI code generators like Claude Code and Devin use AI models that are specifically trained on billions of lines of source code scraped from public repositories (GitHub, GitLab, Stack Overflow, etc.). Through this training, the AI model learns statistical patterns about - How code is structured - How functions relate to each other - What follows a function signature - How natural language descriptions map to executable logic Once the AI coding tool has consumed all the publicly available code on the internet and is fluent in every programming language, you can tell it what code you need, either in the form of natural language prompts or code snippets. The tool analyzes the prompt and suggests code that creates your desired solution. The AI can handle anything from autocompleting a line of code to writing entire applications. #### Types of AI coding tools There are new types of AI coding tools being introduced every day, but these are the most popular options. 1. ##### Code completion tools Code completion tools predict, generate, and autocomplete code as you type. These tools eliminate boilerplate, reduce context-switching, and accelerate the entire development workflow. Examples: GitHub Copilot, Tabnine, Amazon Q Developer 2. ##### AI coding assistants AI coding assistants work alongside developers and often reside within the IDE. They maintain conversational context, reason across multiple files, execute commands, interpret error messages, and propose changes for the developer to approve or reject. Examples: Cursor, Windsurf, Google Antigravity, Gemini Code Assist, GitHub Copilot 3. ##### Autonomous coding agents Autonomous coding agents can perform large tasks to accomplish a goal set by the developer. The agents can plan, execute, analyze, and iterate their own work before giving a result back to the developer. Examples: Claude Code, OpenAI Codex, Devin, Google Jules, GitHub Copilot 4. ##### AI code review tools AI code review tools leverage LLMs to automatically scan, analyze, and evaluate source code. Tools like these can reduce technical debt and improve security posture. Examples: CodeRabbit, Qodo (formerly Codium) ### The security landscape of AI-generated code AI doesn’t make security less relevant. It makes it more critical. Research shows that [62% of AI-generated code](https://sqmagazine.co.uk/ai-coding-security-vulnerability-statistics/) contains design flaws or security vulnerabilities. AI is changing every day, and bad actors are always learning new ways to attack. Application security tools such as static application security testing (SAST), software composition analysis (SCA), and dynamic application security testing (DAST) can uncover many of the AI-generated vulnerabilities in software code. But other types of vulnerabilities require additional tools and processes to protect your organization from attacks. ### The 10 critical risk categories 01. ##### Insecure code patterns If AI is trained on insecure code, there’s a good chance it will suggest code with security flaws. Insecure code makes applications, their underlying systems, and their data vulnerable to exploitation, and are a common entry point for large-scale attacks. 02. ##### Inadequate input validation Input validation ensures that inputs meet predefined criteria like format, type, and range. Malicious inputs can lead to attacks like SQL injection or cross-site scripting. 03. ##### Hardcoded secrets and credentials AI-generated code has been known to include hardcoded secrets and credentials that can grant access to many of the critical systems organizations rely on, as well as valuable or sensitive data. These leaks can also provide access to the AI code-generating tools themselves, which can result in huge costs for the organization. **Related reading:** [Read about our secrets scanning solutions](/content/resources/datasheets/secrets-detection.html) 04. ##### Insecure dependencies AI can suggest too many dependencies, and it can suggest dependencies that are outdated, vulnerable, or hallucinated. The more dependencies the AI suggests, the more likely it is a vulnerable package will be included—increasing your attack surface. 05. ##### Insufficient authentication/authorization Access control failures and threat vectors introduced by AI expand the attack surface. Deploying AI systems without properly verifying user identities or enforcing proper access controls can lead to your most sensitive data being exposed. 06. ##### Data exposure and privacy violations AI models are vulnerable to data leaks, which can expose personally identifiable information and violate laws on privacy protection, like the European Union’s General Data Protection Regulation. 07. ##### Cryptographic failures Cryptographic failures happen to AI models with weak encryption, poor key management, or outdated algorithms. These issues are caused when the AI’s training data includes deprecated code. 08. ##### Licensing and compliance issues AI coding tools learn from open source repositories, but not all open source code is free for commercial use. The 2026 “Open Source Security and Risk Analysis” (OSSRA) report found that [68% of audited codebases contained open source licensing conflicts](/content/resources/analyst-reports/open-source-security-risk-analysis.html). **Related reading:** [Learn how to manage license compliance with Black Duck SCA](/content/blog/manage-license-compliance-with-black-duck.html) 09. ##### Logic flaws and business logic errors AI coding tools are particularly vulnerable to logic flow and business logic errors because they generate code from patterns rather than understanding. As a result, they can produce code that passes surface review but encodes incorrect assumptions, hallucinated interfaces, or flawed control flow that only appears under real load. 10. ##### Overreliance and skill degradation The shift to AI has changed development, and many developers wrongly perceive AI-generated code as more secure than human-written code. Developers with an overreliance on AI may skip traditional security steps or testing, and that affects code quality, security posture, and the developer’s skillset. ### The hidden security tradeoff AI coding tools like Claude Code, ChatGPT Codex, GitHub Copilot, Cursor, Antigravity, Windsurf, and others are valuable resources for developers of all skill levels. But the gains in productivity come with increased risk. ### The case for a hybrid security approach Traditional SAST and DAST tools are built for predictable systems with predefined rules and static code. But as AI increases the amount of code being written, most traditional security approaches can’t keep pace with the accelerated rate of code creation and increasing number of defects and vulnerabilities in that code. This creates a large backlog of issues—all while bad actors are learning new ways to use AI to attack every day. ### Why every business needs resilient AI code security If your AI-generated code is vulnerable, a malicious attack nullifies any productivity benefits gained from AI and exposes you to potentially catastrophic risks. To prevent this from happening, robust security practices should be the foundation of your AI code strategy. That includes staying aware of new AI models, regulations, and compliance protocols that could impact your cybersecurity posture. ### How to evaluate AI coding tools for security How do you choose the right AI coding tool for your business? Start by understanding the options. - **Code completion** tools generate lines of code. - **AI coding assistants** integrate with developer environments to generate and analyze software code and configurations. - **Autonomous coding agents** plan, analyze, and iterate with little to no human oversight. - **AI code review** tools use LLMs to scan and evaluate source code. Once you know what type of tool you need, evaluate vendors on their security capabilities. Here are a few questions to ask. - Can you predict and govern costs? - How well does the tool understand my codebase? - How much will it cost to scan code? - Is there a reduced cost for certain team sizes? - Does it offer on-premises or virtual private cloud deployment? - What are the vendor’s data usage policies? - How well can the tool integrate with your IDE? ### The evolution of AI coding AI has been around for a lot longer than most people realize. Ideas of intelligent machines took form in the 1940s, and research into code completion started in 1957. - **1960s – 1970s:** The first wave of AI code editors brought us autocomplete. The AI could provide syntax-aware suggestions that were pulled from a local file. - **1970s – 1980s:** By the second wave, there was more structured data, so the AI could learn and build models that offered suggestions beyond local context. - **1990s – 2010s:** The third wave of AI coding tools introduced models that could produce entire functions, classes, and boilerplate blocks. By this time, there were billions of lines of public code for AI to train with, and it was able to generate code that was consistent with patterns in the surrounding codebase. - **Today:** The fourth and current wave has evolved from chat-native interfaces to full agentic capabilities. ### The future of AI coding Software architectures are evolving into multiagent networks, which include specialized AI agents that work together behind the scenes to complete complex workflows. Autonomous multiagent systems will push AI to become a full engineering collaborator. Agents will be embedded into every part of the SDLC, so developers can delegate tasks to AI and focus on business context and strategy that AI can’t handle. #### Ready to secure your AI-generated code? See how Black Duck helps teams govern AI-assisted development, reduce risk, and maintain security and compliance at scale.