TL;DR: AI is creating a “great toil shift” in software delivery, with 88% of developers reporting at least one negative impact on technical debt and 53% saying generated code often looks correct while hiding defects, according to Sonar’s State of Code Developer Survey. The security and governance implication is that faster generation without deterministic verification turns code review into a control point, not a postscript.
At a glance
What this is: Sonar says AI code generation is moving developer toil from creation into verification, with most teams reporting both productivity gains and new technical debt risk.
Why it matters: This matters because AI-assisted development changes where quality control has to happen, and IAM, NHI, and security teams should treat verification as a governance layer rather than a downstream cleanup step.
By the numbers:
- 88% of developers report at least one negative impact of AI on technical debt.
- 53% of developers say AI generates code that looked correct but was unreliable.
- 70% of developers are already using static code analysis tools.
- 93% of developers report at least one positive impact of AI on technical debt.
Context
AI code generation is not just a productivity story. It changes the control model for software quality because code can be produced faster than humans can reliably inspect it, which shifts pressure from writing code to verifying whether the code is safe, correct, and maintainable.
That creates a governance problem for engineering leaders, platform teams, and security teams alike: automated generation increases throughput, but it also expands the chance that unreliable logic, duplicated code, and hidden defects slip into production before review catches them. In identity-heavy systems, that same pattern can propagate into authentication, authorisation, and secrets-handling code paths.
Key questions
Q: What should teams do first when AI-generated code starts increasing technical debt?
A: Start by making verification mandatory before merge. Add deterministic tests, static analysis, and policy gates to the delivery path so generated code cannot move forward on confidence alone. Then focus human review on high-risk areas such as authentication, authorisation, and secrets handling, where a plausible-looking defect can create security exposure.
Q: Why do AI coding agents create different governance risks from normal developer tools?
A: AI coding agents combine code execution, context persistence, external tool access, and memory across sessions. That makes them governed identity actors with a wider blast radius than a standard editor or plugin. The risk is not just what they generate, but what they can read, retain, and trigger on behalf of the developer.
Q: What are the signs that verification is failing for AI-assisted development?
A: Look for rising rework, repeated defect fixes, duplicated functions, and review comments that only catch issues after code has already looked ready to ship. If teams cannot distinguish between human-authored and generated code in quality reporting, they also cannot tell where verification is weakest or where technical debt is accumulating.
Q: How should organisations govern AI tools that analyse source code?
A: Set evidence thresholds, require secondary validation for high-impact findings, and define who owns the final decision. Governance should cover prompt scope, output format, reviewer sign-off, and auditability. That keeps AI in an advisory role and prevents noisy model output from becoming an unreviewed security decision.
Technical breakdown
Why generated code creates verification debt
Large language model driven code generation changes the economics of review. Instead of a developer spending time constructing logic line by line, the system can emit plausible code in seconds, but plausibility is not correctness. Verification debt appears when teams accept output that reads well, compiles, and passes superficial checks, yet still contains edge-case failures, insecure assumptions, or duplicated logic. In practice, this means the review burden shifts from authoring to validating, and the strongest signal of control is whether the team can deterministically prove behaviour before merge, not whether the code looked efficient to create.
Practical implication: Treat code review as an evidence-gathering control, not a courtesy step after generation.
How technical debt moves from legacy systems to AI output
The article shows a split in toil patterns. Less frequent AI users still struggle with classic debt sources such as legacy code and poor documentation, while frequent users increasingly spend time correcting AI-created output and managing the debt it introduces. That matters because the source of debt changes, but the operational effect is the same: engineers lose time to rework, clarification, and remediation. The result is a new class of delivery friction where speed gains are partly offset by the cost of validating machine-generated code at scale.
Practical implication: Measure rework and defect correction separately for AI-generated code so you can see where new debt is accumulating.
Why static analysis becomes a control, not a convenience
Static code analysis is useful here because it gives teams deterministic checks that do not depend on a human spotting every flaw in a large volume of generated output. Sonar’s data indicates most developers already use such tools, which suggests the market is moving toward automated verification as a normal part of the pipeline. For security and governance teams, the key point is that analysis tools only work if they are wired into the delivery path early enough to prevent weak code from advancing, and strict enough to block unsafe patterns rather than merely report them after the fact.
Practical implication: Embed rules-based analysis in pre-merge workflows and fail builds on high-risk patterns rather than relying on post-release cleanup.
NHI Mgmt Group analysis
AI code generation is turning verification into the primary control plane for software quality. The article’s central shift is not simply faster delivery, but a changed distribution of risk: output can be generated faster than teams can inspect it. That makes deterministic verification, not human intuition, the control that separates useful acceleration from accumulated technical debt. For practitioners, the priority is to govern what reaches the pipeline, not just what developers type.
Plausible code is a security problem when it compresses the review window. AI-generated output can look finished while still embedding hidden defects, duplicated logic, or unsafe assumptions. That creates false confidence, which is more dangerous than obvious failure because it weakens review discipline at the exact point where assurance is needed. The practical conclusion is that governance must assume plausible output is untrusted until verified.
AI-generated code quality is now part of identity and secrets governance wherever software handles access or trust decisions. In IAM, PAM, NHI, and agentic AI systems, generated code often touches authentication flows, token handling, API permissions, and secrets use. If those paths are not verified deterministically, the defect becomes a governance issue, not just a development issue. Practitioners should treat code-generation controls as upstream identity-risk controls.
Static analysis is becoming a compensating control for the speed asymmetry introduced by generative code. The article shows that verification must scale with generation, otherwise debt simply moves from authoring to remediation. That does not mean static analysis alone is sufficient, but it does mean teams need enforceable rules, not optional guidance, when machine-generated code can enter critical systems. For practitioners, control consistency matters more than developer intent.
Technical debt from AI is a lifecycle issue, not a one-time coding concern. The article’s “great toil shift” shows that effort is not removed, only relocated. That means teams need lifecycle governance for generated code, from creation through review, test coverage, and maintenance. For practitioners, the right response is to manage AI output as an asset with quality obligations, not as disposable productivity gain.
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.
- Read next: Agentic AI Security Guide
What this signals
Verification debt: the real governance issue is no longer whether AI can generate code, but whether teams can prove its behaviour before it enters trusted paths. In software that handles access decisions, secrets, or identity flows, unverified generation turns development speed into control weakness.
Static analysis and policy enforcement need to move earlier in the pipeline because post-merge review cannot reliably recover assurance once flawed logic has spread across the codebase. When AI output accelerates, quality gates have to become more deterministic, not more advisory.
For practitioners
- Implement deterministic pre-merge verification Require static analysis, policy checks, and test gates before AI-generated code can merge into protected branches.
- Separate AI-generated code from human-authored code in review Tag generated commits or files so reviewers can apply stricter scrutiny to authentication, authorisation, and secrets-handling paths.
- Track rework from generated code Measure defect fixes, duplicated code removal, and post-merge rewrites as a separate quality metric for AI-assisted development.
- Protect identity-sensitive code paths first Prioritise verification for modules that handle tokens, credentials, permissions, and access decisions because defects there create governance risk.
Key takeaways
- AI-generated code is shifting engineering toil from authoring into verification, which makes deterministic controls central to delivery quality.
- The biggest risk is plausible-looking code that hides defects and creates false confidence in review.
- Teams that govern AI-assisted development well will verify critical paths first, especially where access, credentials, and trust decisions are involved.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATT&CK addresses the attack and risk surface, while OWASP ASVS, NIST CSF 2.0 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP ASVS | V15 — Secure Coding and Architecture | Generated code risk is fundamentally a secure-coding and architecture verification issue. |
| Recommendation — Use V15 to require review and testing of AI-generated logic before it reaches production. | ||
| NIST CSF 2.0 | PR.DS-10 — Information is protected during storage | The article’s quality-control concern maps to protecting the integrity of software artefacts and outputs. |
| Recommendation — Apply PR.DS-10 to preserve integrity controls over generated code and build artefacts. | ||
| CIS Controls v8 | CIS-16 — Application Software Security | Static analysis and gated review are core application security controls for generated code. |
| Recommendation — Implement CIS-16 to enforce secure coding review and automated analysis in the delivery pipeline. | ||
| MITRE ATT&CK | TA0009 — Collection | The article is about code output quality, not adversary behaviour, so ATT&CK is only lightly relevant via software abuse patterns. |
| Recommendation — Map dangerous generated-code patterns to ATT&CK-style detection logic where code injection or misuse is suspected. | ||
Key terms
- Verification debt: The accumulated gap between the identities an organisation manages and the confidence it can still have in validating them at the moment of access. It grows when verification, entitlement review, and offboarding do not keep pace with identity sprawl.
- Static Source Code Analysis: Static source code analysis is the inspection of code without running it, used to identify defects, insecure patterns, and policy violations early in development. It examines source, bytecode, or binaries at rest, which makes it useful for finding secrets, injection paths, and misconfigurations before release.
- Manual Code Review: Manual code review is the process of having developers inspect source code by hand to judge correctness, readability, design, and business logic. It works best where context matters and the reviewer needs to understand intent, architecture, or maintainability. This approach is collaborative but slower and more prone to human oversight than automation.
- 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.
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 and identity practitioners build the governance discipline needed to control trust at the point of issuance and review.
Published by the NHIMG editorial team on August 20, 2026.
Updated on October 12, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org