AI-generated code is accelerating technical debt and review risk

At a glance

What this is: Sonar argues that AI-generated code is accelerating technical debt by increasing the amount of code that must be reviewed, verified, and maintained.

Why it matters: For IAM, NHI, and broader security teams, the lesson is that speed gains from AI only hold if quality gates and continuous verification prevent weak code from becoming production risk.

By the numbers:

• 88% of developers report at least one negative impact of AI on technical debt, according to Sonar.
• 42% of committed code is now AI-generated or assisted, according to Sonar.
• 93% of developers report at least one positive impact of AI on technical debt, according to Sonar.

Context

Technical debt is the accumulated cost of shortcuts in code, testing, architecture, and process that make future change slower and riskier. In AI-assisted development, the governance problem is not only how fast code is produced, but whether teams can still see, verify, and control what reaches production.

The article frames a familiar engineering issue with a new delivery model: AI can help with documentation, testing, and refactoring, yet it can also create unreliable or duplicative code that slips past review. For identity and security practitioners, the intersection is governance, because production code quality determines the strength of every downstream control.

Key questions

Q: What should teams do first when AI-generated code starts increasing technical debt?

A: Start by enforcing automated quality gates on every commit. If AI-assisted code can enter the main branch without verification, teams inherit hidden rework, duplicated logic, and weak test coverage. The first control should be point-of-merge analysis that blocks code failing defined standards, because post-release cleanup is too late to prevent debt from compounding.

Q: Why does AI-generated code increase the risk of outages and defects in software delivery?

A: AI-generated code can introduce bugs, unreadable logic, maintainability problems, and security flaws even when it appears syntactically correct. Those issues become operational risk when teams trust generated output too quickly or review it inconsistently. Automated verification reduces that risk by catching problems early, before they become incidents in production.

Q: How do security teams know if their verification controls are actually working?

A: They work if high-risk requests cannot be completed through a single channel and if helpdesk or approval attempts leave a clear audit trail. Look for reductions in informal overrides, fewer password resets completed without corroboration, and lower success rates for phishing simulations that use synthetic audio or video.

Q: What happens when technical debt is treated as a side issue instead of a governed backlog?

A: It compounds into slower delivery, higher maintenance costs, and less predictable releases. Teams keep paying interest on unresolved shortcuts while feature work becomes harder to ship and harder to trust. The practical consequence is not just extra cleanup, but a shrinking ability to change systems safely over time.

Technical breakdown

Why AI-generated code increases technical debt

Technical debt rises when organisations accept shortcuts today and defer the cost to later releases. AI changes the scale of that trade-off because it can generate large volumes of code quickly, including duplicated logic, verbose structures, and code that appears correct but is not reliable. That increases review burden, raises the probability of hidden defects, and makes maintenance more expensive. The issue is not that AI writes code, but that the organisation may treat the output as trustworthy before it has been verified. Practical implication: treat AI output as untrusted until it passes the same analysis and review gates as human-written code.

Practical implication: treat AI output as untrusted until it passes the same analysis and review gates as human-written code.

How continuous code analysis changes the control model

Continuous code analysis moves technical-debt management from periodic cleanup to an always-on control. By scanning code in CI/CD, teams can detect maintainability, reliability, and security issues before they merge, rather than after they become part of the release baseline. Quality gates add a hard decision point, blocking code that fails defined standards. This matters because AI-generated code can create debt faster than manual review can absorb it. The control objective is not just detection, but preventing low-quality code from compounding. Practical implication: make merge approval conditional on automated verification rather than developer confidence or review speed.

Practical implication: make merge approval conditional on automated verification rather than developer confidence or review speed.

Why remediation has to be managed as a portfolio

Technical debt is not one problem but many, spanning defects, duplicated logic, missing tests, and outdated dependencies. Treating it as a portfolio means classifying items by severity, effort, and business impact, then paying down the highest-interest items first. That approach is more resilient than chasing every issue equally, because it aligns engineering effort with operational risk. AI can help with cleanup work, but only if the backlog and remediation process are already disciplined. Practical implication: score debt items, tie them to release planning, and keep remediation visible in sprint cycles.

Practical implication: score debt items, tie them to release planning, and keep remediation visible in sprint cycles.

NHI Mgmt Group analysis

AI-generated code has become a code governance problem, not just a productivity feature. The article shows that generation speed is now outpacing the organisation's ability to verify quality. That changes the control objective from reviewing finished output to governing what is allowed into the codebase in the first place. Practitioners should treat AI-assisted development as a policy and assurance problem, not a tool-choice debate.

Continuous verification is now the real compensating control for developer and AI-written code. Static review after the fact is too weak when a large share of committed code may be machine-assisted and look plausible on first pass. The meaningful question is whether quality gates, analysis, and merge controls are enforced consistently enough to stop defect accumulation before it becomes operational debt. Practitioners should measure control coverage at the point of commit, not after release.

Technical debt now behaves like a form of security debt when review quality is inconsistent. Unreliable or duplicative code raises defect rates, slows remediation, and expands the attack surface by making systems harder to reason about. For teams running IAM, PAM, or NHI-related workflows in software, that means code quality is part of identity governance because broken automation can expose credentials, weaken enforcement, or bypass intended controls. Practitioners should link code assurance to business risk, not engineering vanity metrics.

AI does not remove debt management work, it redistributes it. The article makes clear that AI helps with documentation, tests, and refactoring while also generating new cleanup demand. That is why the winning operating model is measured, prioritised remediation rather than reactive cleanup. Practitioners should expect the backlog to grow unless verification, prioritisation, and payback are made routine.

Continuous code analysis should be treated as a governance layer for modern software delivery. The article's core lesson is that velocity without verification simply accelerates hidden costs. That makes technical-debt management a standing control function, not an occasional refactor programme. Practitioners should align engineering throughput with assurance thresholds so that speed improvements remain sustainable.

From our research library:

• Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to the State of Secrets in AppSec.
• Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
• Read next: AI Coding Agents Security Guide

What this signals

AI-generated code is creating a verification bottleneck: teams are no longer constrained only by how quickly code can be produced, but by how much of it can be checked before it becomes part of the release baseline. When review capacity is fixed and output grows, verification has to move left into the pipeline or debt will accumulate faster than remediation can clear it.

Code quality is becoming a governance control, not a post-release hygiene task. For programmes that rely on software to enforce IAM, PAM, or NHI workflows, weak code quality can undermine the controls themselves. That makes automated analysis and merge gating part of operational assurance, not optional engineering tooling.

For practitioners

• Enforce quality gates on every commit Block merges unless automated analysis confirms the code meets defined reliability, maintainability, and security thresholds, including code produced with AI assistance.
• Treat technical debt as a managed backlog Classify debt items by severity, effort, and business impact so remediation is scheduled alongside feature work rather than deferred indefinitely.
• Verify AI-generated code before release Apply the same review and testing standards to AI-assisted output as to human-written code, because plausible code can still conceal duplicated logic or unreliable behaviour.
• Track debt in sprint planning Reserve recurring capacity for remediation so refactoring, test coverage, and documentation work do not get displaced by feature pressure.
• Use continuous analysis to surface hidden defects Integrate code scanning into the CI/CD pipeline so maintainability and quality issues are detected while the change is still inexpensive to fix.

Key takeaways

• AI-assisted development can accelerate technical debt when organisations fail to verify code at the same pace they generate it.
• The article links 88% negative developer experiences and 42% AI-generated or assisted committed code to a growing review burden.
• Continuous analysis, merge gates, and disciplined remediation are the controls that turn speed into sustainable delivery instead of hidden rework.

Key terms

• Technical debt: Legacy shortcuts or weak design choices that create future cost, risk, or operational friction. In identity programmes, technical debt often shows up as brittle trust relationships, inconsistent revocation, poor traceability, or controls that only work in one environment and fail in another.
• Quality Gate: An automated release control that blocks code when it fails security, test coverage, or maintainability thresholds. It converts review criteria into deterministic enforcement, which is especially important when code is produced by AI systems that may not self-detect environmental or adversarial weaknesses.
• Continuous Code Verification: Continuous code verification is the practice of checking source code, dependencies, and configuration as changes move through the development pipeline. It shifts assurance left and keeps security, quality, and compliance evidence attached to each change rather than relying on a late manual review.
• AI-Generated Code: Code produced fully or partly by a generative model rather than written entirely by a human developer. It can accelerate delivery, but it also increases the risk of duplicated logic, weak assumptions, and code that appears correct without being reliable.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It helps security practitioners connect assurance, lifecycle control, and operational governance across modern identity programmes.