Who's Testing the AI-Generated Code? Closing the Gap in AI Code Security
- 10Pearls Editorial Team
- 7 min read
- Updated Jul 2026
As AI-generated code redefines both software development and quality assurance, we explore why quality engineering must evolve
to address AI-generated code, emerging security risks, and modern testing practices that help enterprises deliver AI-enabled software with greater confidence.
For many engineering teams, AI has become a software development partner capable of producing code in a fraction of time. It can write functions, suggest architectures, refactor legacy applications, and even create unit tests. However, as organizations accelerate software delivery with AI, one critical question is receiving far less attention: Who’s testing the AI?
AI-generated code doesn’t simply change how software is written. It changes what quality engineering must validate. Security vulnerabilities, hallucinated dependencies, and unpredictable behavior can all pass conventional reviews while appearing perfectly legitimate.
However, 44.1% of organizations surveyed have deactivated AI features in production because of quality or reliability concerns, according to an Applause report. Rather than slowing adoption, this highlights the need for stronger validation and governance as AI-assisted development becomes mainstream. That is why AI code security is no longer just a developer concern and is becoming more of a quality engineering challenge.
Why AI code security is now a QA problem
With the help of AI, teams can produce code faster than it can be validated, and now the burden of finding defects is increasingly shifting downstream to quality engineering.
The challenge is not simply that AI introduces bugs. AI-generated code can appear well-structured, compile successfully, and pass peer review while still containing functional defects or security vulnerabilities. According to a recent study, 70% of respondents said test-suite maintenance is now a bigger burden than writing code itself, while 81% of enterprise technology leaders surveyed reported an increase in production issues linked to AI-generated code.
Every defect that reaches production, whether functional or security-related, is ultimately an AI code quality issue, making QA a crucial aspect of a layered validation model for AI-generated code and enterprise systems. Here are four of the biggest challenges in terms of AI code testing, and how leading enterprises are addressing them.
Where traditional QA still applies & where it doesn't
When testing AI-generated code, traditional software engineering practices still apply. Once implemented, that code behaves deterministically like any other application code and should be validated with conventional functional, regression, and security testing.
The challenge changes when the application itself incorporates generative AI at runtime. Large language models are probabilistic, meaning the same prompt can produce different (but equally acceptable) responses. In these cases, a single expected output is no longer sufficient to determine whether the system is behaving correctly.
Quality engineering must therefore complement deterministic testing with evaluation methods that assess consistency, safety, reliability, and adherence to business requirements across a range of acceptable outcomes. As enterprises increasingly combine AI-generated code with AI-powered features, QA teams need testing strategies that address both deterministic software behavior and probabilistic AI behavior.
Closing the AI code security gap
Traditional functional testing verifies that software works, but does not necessarily verify that it is secure. AI can reproduce familiar security flaws, creating AI-generated code vulnerabilities such as injection vulnerabilities, hardcoded secrets, weak input validation, insecure coding patterns, and outdated dependencies, at machine speed. Without dedicated security validation, these defects can move directly into production.
AI also introduces new risks. One emerging threat is package hallucination, where an AI model recommends a software library that does not exist. An attacker can register that package name in a public repository before a developer attempts to install it, introducing malicious code into the build process. This attack, known as slopsquatting, has become an emerging software supply chain security concern. A USENIX Security research paper found an average hallucinated-package rate of at least 5.2% for commercial models and 21.7% for open-source models, and 205,474 unique non-existent packages generated during testing.
These risks cannot be identified through functional testing alone. Modern quality engineering must extend beyond application behavior to include static analysis, dependency validation, software composition analysis, SBOMs, and continuous security testing.
Rethinking regression testing for AI
Regression testing depends on stable expectations. AI changes those expectations. When an AI-powered application produces different (but still acceptable) results, a changed output is not automatically a regression. At the same time, AI-generated code increases the volume of software being produced, creating more edge cases and interactions than traditional regression suites were designed to handle.
The result is a growing verification gap. QA teams must distinguish between legitimate variation and genuine defects while keeping pace with rapidly increasing development velocity. Behavioral baselines, continuous evaluation, and intelligent automation are becoming essential for maintaining confidence in AI-assisted software releases.
When AI tests its own code
Another major risk comes into play when AI is used to generate both the application code and the QA tests to validate it. While AI can be highly effective at accelerating both activities, using the same model for implementation and test generation can introduce correlated assumptions and shared blind spots. Without independently defined requirements, acceptance criteria, risk scenarios, and test oracles, the tests may validate that the code behaves as implemented rather than whether it behaves as intended.
The solution is not to avoid AI-generated tests, but to ensure they are grounded in independently established expectations. Quality engineering teams should define what success looks like before code is generated, then use AI to accelerate implementation and testing within those guardrails. This preserves AI’s productivity benefits while maintaining confidence that testing remains objective, comprehensive, and capable of identifying both functional and security defects.
How enterprises secure AI-generated code
Addressing these challenges requires a different quality engineering model, and leading organizations are combining traditional testing with AI-specific software supply chain security practices by:
- Evaluating behavior alongside deterministic outputs
- Keeping test specifications independent of AI-generated implementations
- Prioritizing edge cases, security boundaries, concurrency, and error handling
- Extending QA to include dependency validation, software composition analysis, secrets detection, and software supply chain security
- Automating continuous AI code testing and release gates so validation scales alongside AI-assisted development
The future of quality engineering
AI is redefining quality assurance, and as AI-generated code becomes standard practice across enterprise software development, quality engineering teams will be responsible for validating much more than just functionality. They will have to determine whether AI-generated software is trustworthy, resilient, and secure enough for production.
That makes AI code security one of the defining quality engineering challenges of the coming decade—and one of the greatest opportunities for organizations that invest in modern testing practices today.
At 10Pearls, we help enterprises build quality engineering capabilities tailored for AI-driven software development. Our software quality assurance services help enterprises embed these practices into their workflows to accelerate delivery without compromising security, quality, or compliance. The result is faster releases, stronger governance, and greater confidence that AI-generated code is reliable and ready for production.
Related blogs
AI/ML
Navigating the AI Shift: Lessons from Imran Aftab
In a recent Mauloa podcast, 10Pearls CEO Imran Aftab shares practical lessons on AI-native transformation, leadership, engineering excellence, and building...
AI/ML
How AI Is Reaching the Charity Sector
AI is reshaping how charities work and how people donate. The real opportunity lies less in new tools than in...
AI/ML
Top AI-Powered Software Testing Companies
Compare the top AI-powered software testing services and learn how automation, self-healing tests, and AI-augmented QA de-risk enterprise releases.
AI/ML
Why AI-Powered QA Still Needs Human Judgement
AI is helping QA teams move faster, generating test cases, healing broken automation, and flagging risk before it becomes a...
AI/ML
How AI Is Changing Software Engineering Roles
AI is changing what makes software engineers valuable. Learn why business context, architecture, and problem-solving matter more than ever in...
AI/ML
Corporate AI Implementation Failure – Role of Leadership
Most enterprises now share the same models and tools, so why does AI still fail? The gap is leadership: prioritization,...
AI/ML
AI Skill Erosion – The Hidden Cost of AI Dependency
As AI adoption accelerates, enterprises face a hidden challenge: skill erosion. Learn how governance, oversight, and human judgment prevent thinkslop.
AI/ML
How AI creates value with open banking data
Every fintech company with an open banking license in Saudi Arabia must build the basic infrastructure to receive open banking...
AI/ML
Outcome Based Pricing in Digital Engineering
AI is making outcome-based pricing more viable, but success still depends on clear metrics, shared accountability, and disciplined execution.
AI/ML
The Hidden Risk Behind AI-Powered Software Development
AI coding tools are accelerating development, but code review, governance, and quality assurance aren't keeping pace. Learn how enterprises can...
Get in touch with us
Global digital transformation and product engineering partner.