AI-written software is quickly becoming part of everyday development. Developers are using AI tools to write code, generate tests, fix bugs, create documentation, and move faster through technical tasks. On the surface, this looks like a major productivity win. But for security teams, the story is more complicated.

Reporting from Cybersecurity Dive highlights a growing concern: companies using AI to write code are creating security risks that many organisations are not fully prepared to handle. The issue is not that security teams reject AI. It is that they need clear controls, audit trails, and access restrictions before trusting it inside critical workflows.

Why AI-written software is creating pressure

The main challenge is speed. AI coding tools allow engineering teams to produce more code in less time. That creates a problem for security teams because they must review more changes, more frequently, often without extra resources. When development accelerates but security review capacity stays the same, bottlenecks and blind spots appear.

According to ProjectDiscovery’s findings reported by Cybersecurity Dive, only 38% of cybersecurity practitioners said they are keeping up well with the increased volume of code they need to review because of AI. Nearly 60% said the task is getting harder. This shows that AI coding is not just a developer productivity issue. It is a security operations issue.

Mid-sized companies may feel this pressure even more because they often have fewer dedicated security resources than larger enterprises. A large company may have separate teams for application security, cloud security, compliance, and incident response. A smaller company may expect one security team to handle everything.

The illusion of safe code

One of the biggest risks with AI-written software is that it often looks polished. AI-generated code can appear clean, professional, and logically structured, even when it contains hidden vulnerabilities. This creates what many security professionals describe as an illusion of correctness.

Developers may trust the output because it compiles or appears to solve the immediate problem. But working code is not always secure code. A function can operate correctly while still exposing secrets, mishandling permissions, introducing weak validation, or relying on risky dependencies.

This is why human review remains essential. AI can assist development, but it should not become a shortcut around security checks. Businesses that rely too heavily on AI-generated output without verification may unknowingly introduce weaknesses into production systems.

The biggest security concerns

Security teams are particularly worried about several categories of risk. One major concern is secrets leakage, where developers may accidentally expose sensitive data through AI tools or generated code. Another concern is unreliable dependencies, where AI may suggest packages or libraries that are outdated, poorly maintained, or risky.

ProjectDiscovery’s report also highlighted business logic vulnerabilities as a major worry. These are design flaws where an application technically works but can be misused in ways the business did not intend. Business logic flaws are especially difficult because they are not always detected by basic automated scans.

For example, an application may allow a user to perform an action in the wrong order, access a function they should not, or manipulate a process in a way that bypasses expected controls. These risks require deep understanding of the application, not just surface-level code checks.

Why audit trails matter

Security teams increasingly want clear audit trails for AI-generated code. They need to know which parts of the codebase were written by humans, which were generated by AI, who approved them, and what testing was performed before deployment.

Without this visibility, accountability becomes difficult. If a vulnerability appears later, the organisation needs to understand how it entered the codebase and whether the review process failed. This matters not only for internal security, but also for compliance, insurance, customer trust, and incident response.

Strong audit trails also help organisations avoid confusion between productivity and safety. AI may make development faster, but speed without visibility can create long-term technical and security debt.

What businesses should do before scaling AI coding

Businesses should not ban AI coding outright, but they should adopt it carefully. The best approach is to create clear guidelines that define where AI coding tools can be used, what data can be shared with them, and what level of review is required before code reaches production.

Companies implementing AI automation and integration should treat security as part of the design process, not an afterthought. If AI is being used to accelerate workflows, the organisation must also accelerate review, testing, and governance.

This may include stronger code review standards, automated security testing, dependency scanning, access controls, secure prompt handling, and documented approval processes. The aim is not to slow teams down unnecessarily. The goal is to make AI-assisted development safe enough to scale.

Why DevSecOps is becoming more important

AI-written software strengthens the case for DevSecOps, where security is built into the development lifecycle instead of being added at the end. When code is generated faster, security checks must move earlier and become more automated.

This means developers, security teams, and business leaders need to work together. Developers need guidance on safe AI usage. Security teams need visibility into AI-assisted workflows. Leaders need to understand that faster delivery must still include risk management.

This is also relevant for businesses building customer-facing platforms, apps, and digital products. If your company is investing in mobile app development services, secure coding and proper review processes are essential, especially when AI tools are part of the build process.

AI can help security teams too

The story is not only negative. AI can also support security teams when used responsibly. AI tools can help summarise vulnerabilities, triage alerts, analyse logs, generate security test cases, and assist with documentation. In some environments, AI can help security teams keep up with the same speed that AI has introduced into development.

GovTech Singapore has also highlighted how AI is reshaping cybersecurity, noting that organisations need stronger visibility and more contextual approaches to risk in an AI-driven threat landscape. This is a useful reminder that AI is both a challenge and an opportunity for defenders.

The important distinction is governance. AI should help security teams prioritise and respond faster, but it should not replace expert judgement. Security decisions still require context, experience, and accountability.

The future of secure AI coding

AI-assisted development is not going away. Developers like these tools because they save time, reduce repetitive work, and help overcome technical blocks. Businesses like them because they can increase output and reduce delivery timelines. Security teams, however, need the right controls before AI-generated code becomes a trusted part of the software supply chain.

The future will likely involve a more formal AI coding governance model. Companies may require AI code labelling, approval workflows, secure tool configurations, code provenance tracking, and policy-based restrictions on what AI systems can access. These practices will become increasingly important as more production code is influenced by AI.

The companies that succeed will not be the ones that blindly adopt AI coding tools, nor the ones that reject them completely. The winners will be organisations that combine AI speed with strong security discipline.

Final thoughts

AI-written software is changing the relationship between development and security. It gives teams the power to build faster, but it also creates new risks that cannot be ignored. Security teams are not standing in the way of progress. They are asking for the controls needed to make progress sustainable.

For business leaders, the lesson is clear. AI coding should be treated as a strategic capability, not a shortcut. With proper governance, audit trails, testing, and human oversight, AI can improve productivity without creating unnecessary security exposure.

As AI becomes more embedded in software development, the strongest organisations will be those that build security into the process from day one.