Sixty-one percent of developers use or plan to use AI tools for generating code. While 81% of developers say these tools improve their productivity, they aren’t without risk.
Without proper safeguards in place, AI code could introduce novel security risks that creative attackers are aware of and know how to exploit.
AI chatbots are helpful for development, but this innovative addition shouldn’t come at the expense of security. This article will help you understand the biggest risks of AI-generated code and how to protect your organization from AI-driven threats.
AI can write code fast, but not always safely. AI-generated code may look clean and functional, but beneath the surface, it can harbor critical vulnerabilities.
From improper input validation to insecure authentication, these issues can create exploitable gaps that developers might miss during a quick review. Every line of AI-assisted output should be treated as untrusted until you verify it through secure code review and testing.
AI-generated code may introduce subtle bugs, performance bottlenecks, or structural inefficiencies that compromise long-term maintainability. What looks like a functional solution today can quickly spiral into technical debt tomorrow, requiring costly rework and patching.
AI-generated code also lacks contextual understanding. AI doesn’t truly “understand” your application’s business logic, user requirements, or industry regulations.
That means it might generate code that technically runs, but fails to meet crucial standards, especially in high-stakes environments like finance or healthcare.
Many AI models are trained on large-scale public datasets that include proprietary or licensed code. As a result, they can reproduce snippets that closely resemble copyrighted material.
If that code makes its way into your product, you could face copyright infringement claims or licensing violations, even if the reuse was unintentional. Organizations operating in regulated industries or handling sensitive IP must be especially cautious.
What seems like a helpful shortcut could turn into a costly legal dispute or force you to refactor entire systems.
AI tools learn from what you feed them. When developers use AI models with proprietary or sensitive source code, there’s a real risk of that information being captured, stored, or reproduced without clear boundaries.
In some cases, input data can unintentionally train public or third-party models, creating a scenario where external users or attackers get access to internal logic, trade secrets, or confidential architecture.
This risk is especially high with AI tools that transmit data to external servers for processing. Without clear data governance and secure model usage policies, your team may inadvertently leak valuable intellectual property, providing competitors or threat actors with a window into your core technology.
The convenience of AI can dull developers’ skills, especially if they lean too heavily on AI-generated code. Skill atrophy can cause developers to become less familiar with the underlying architecture of the solution, making future debugging, refactoring, or scaling much more difficult.
Worse, AI can create a false sense of confidence. When developers copy and paste output without a deep understanding, oversight diminishes, and subtle bugs or vulnerabilities can slip through the cracks unnoticed.
While it has its risks, AI-assisted coding is still essential for modern development. Instead of banning it outright, organizations need proper security safeguards and usage guidelines in place to use AI responsibly.
AI-generated code should never go unchecked. From insecure dependencies to IP violations and eroding skills, the risks are real, but manageable with the right strategy. This includes rigorous code review, strong development practices, and modern security monitoring tailored to address AI-era challenges.
Mindgard’s Offensive Security for AI helps security teams stay ahead of these risks by providing powerful AI threat modeling, red teaming, and continuous monitoring tools specifically designed for AI-driven environments. Book a Mindgard demo now to safeguard your AI systems.
Not always. AI-generated code may appear clean on the surface, but it often lacks proper documentation, context, or edge-case handling. Traditional code reviews might miss deeper issues unless reviewers are trained to evaluate AI-specific pitfalls like insecure defaults or opaque logic.
Start with a clear usage policy. Require manual review of all AI-generated code, restrict its use in sensitive systems, and integrate automated security scanners to catch obvious flaws. Pairing AI tools with code linters, dependency checkers, and secure SDLC practices can also mitigate risk.
Yes. Security audits should specifically flag AI-assisted code and examine it for licensing issues, dependency vulnerabilities, and structural weaknesses. Logging AI use and reviewing those areas separately can help teams stay compliant and secure.
