Balancing Speed and Security in the Age of AI-Generated Code

This guest blog was contributed by Mark Lambert, Chief Product Officer at ArmorCode.

Within 90 days, your developers will generate more code than they produced all last year. Most of it will be written by AI, not humans. And unless your security strategy has fundamentally changed in the past six months, you're already losing control.

The numbers are staggering. Microsoft and Google each report that more than 30% of their code is now AI-generated. At ArmorCode, we're seeing 4X productivity gains in specific use cases. Gartner predicts that by 2027, 25% of software defects will stem from lack of human oversight of AI-generated code.

The Quality Paradox

Here's what most security teams misunderstand about AI-generated code: the code quality has improved dramatically over the past two years. AI models now produce functionally correct, well-structured code at rates that would have seemed impossible in 2023. But the security vulnerability rate hasn't improved at the same pace. AI-generated code still contains roughly the same percentage of security issues as human-written code.

The math is straightforward and alarming. If developers are generating 4X more code with the same vulnerability density, organizations are accumulating security debt four times faster. A 10% vulnerability rate applied to 10,000 lines of human code per month meant 1,000 issues. Applied to 40,000 lines of AI-generated code, that's 4,000 issues with the same security team capacity.

This will be a solved problem. AI code generators are beginning to integrate static analysis and vulnerability identification directly into development workflows. But we're not there yet. Until then, organizations need the same rigorous security practices for AI-generated code that they apply to human code. Given the velocity differential, these controls are now more critical than ever.

This velocity-driven accumulation of vulnerabilities manifests in three ways that security teams must address immediately.

Black Box Complexity That Resists Debugging

Developers increasingly ship code they don't fully understand. When a developer accepts an AI suggestion that "works" without comprehending the underlying logic, they've introduced black box complexity. This manifests as code bloat: AI models generate overly complicated solutions and fail to remove unused code, creating unnecessary attack surface.

When production incidents surface, no one can explain why the system behaves the way it does. Enterprise teams struggle to debug critical business logic that came from AI suggestions. Technical debt compounds rapidly. Security audits become exercises in reverse-engineering rather than review.

Mitigating this requires AI literacy: training developers to understand what they're accepting and document the reasoning behind AI-generated solutions.

IP Exposure Through Prompts and Embedded Models

When developers use generative AI tools, the inputs represent an often-overlooked attack surface. Every time a developer pastes code into a prompt asking "why isn't this working?" they're potentially exposing proprietary algorithms, internal API patterns, or business logic.

The risk extends beyond code generation to AI embedded within applications. When developers integrate AI models via frameworks like Model Context Protocol, they're introducing dependencies that require the same scrutiny as any third-party library. If teams aren't using properly configured enterprise tools, these inputs may contribute to training data for public models.

Security teams need to establish clear policies that treat AI tool usage with the same rigor applied to open-source dependencies.

Productivity Debt From Unvalidated Velocity

AI is a force multiplier for developer output. Tasks that previously consumed hours are now complete in minutes. But this creates an organizational mismatch: development velocity has increased dramatically while code review, security testing, and quality assurance remain constant.

The result is productivity debt: a growing backlog of code shipped faster than it can be properly validated, documented, and understood. Test coverage percentages decline. Security findings sit in backlogs longer.

Addressing this requires pairing AI development tools with proportional investment in validation infrastructure. Automated security scanning needs to happen in real-time as code is generated, not days later in CI/CD pipelines.

Building Toward Secure-by-Default AI Assistance

Despite these challenges, enterprise-ready AI development tools increasingly allow organizations to configure guardrails and establish secure-by-default patterns. Some organizations are successfully using AI to reduce security risk by generating consistent, standards-based implementations.

The key difference is investment in infrastructure to validate, monitor, and govern AI usage. This is where exposure management becomes critical. Security teams need visibility into which tools developers are using, what percentage of the codebase originated from AI assistance, and whether AI-generated components correlate with elevated security findings.

At ArmorCode, we're seeing organizations extend their application security posture management to explicitly track AI-generated code as a distinct category of risk, informing prioritization and focusing validation efforts where they're most needed.

Securing What You Can't Stop

Your developers are already using AI code generation tools, whether your security team has approved them or not. The question isn't whether to allow AI in development, it's whether your organization has the visibility and controls to secure what's being generated.

Organizations that successfully harness AI velocity while managing security risk have established clear policies on acceptable tool usage, implemented technical controls that prevent sensitive data from entering prompts, and adapted code review processes to examine AI contributions. Most importantly, they've built visibility into where and how AI is being used across their development lifecycle.

AI-generated code represents a permanent shift in how software gets built. The organizations that thrive will treat AI code generation like any other significant infrastructure change: with thoughtful policies, rigorous validation, and continuous monitoring.

The question for security leaders is whether you have the visibility to know what's being generated and the controls to ensure it meets your security standards.

Mark Lambert is Chief Product Officer at ArmorCode, where he leads product strategy for unified vulnerability management and exposure management platforms serving Fortune 500 enterprises.