If recent industry forecasts are correct, 2026 will be the year of artificial intelligence (AI)-driven technical debt.

According to research company Forrester^[1], the tech debt for 75% of organisations will increase to a moderate or high level during this year, due to the rapid expansion of AI usage across a range of areas including software development.

Indeed, the software development community is experiencing a near ubiquitous usage of AI coding tools as teams face pressure to generate more output in less time. While the huge efficiency gains from the tools help significantly, teams too often fail to incorporate adequate safety controls and practices into AI deployments.

The resulting risks leave their organisations exposed, and developers will struggle to backtrack in tracing and identifying where – and how – a security gap occurred. All of which leads to excessive detection and remediation time that companies cannot afford.

The challenge already exists

This situation is not hypothetical as the problems are already being experienced. Research^[2] shows that one in five organisations have already experienced a serious security incident that can be directly tied to AI-generated code.

Nearly two-thirds^[3] of coding solutions produced by large language models (LLMs) turn out to be either incorrect or vulnerable and around half of the correct solutions are insecure which means LLMs cannot yet create deployment-ready code.

In research completed by Secure Code Warrior^[4], it was found that AI continues to encounter difficulties with subjective, context-based risk factors related to authentication, access control and proper configurations.

Unfortunately, the subsequent accumulating tech debt will not come with a quick and easy fix. The need for speed is likely to bring significant consequences with onerous reworks required to correct mistakes.

Traditional tech debt is created when individuals take shortcuts and, for developers, the increasingly blind dependence on AI is swiftly intensifying the situation.

The challenge is heightened further because around 50% of developers do not use the AI tools provided by IT^[5]. This ‘Shadow AI’ trend further diminishes transparency in the software development lifecycle (SDLC) and raises the risks of significant compromises.

The long-term costs will prove severe as backtracking and reworking code takes time and money. Also, an overreliance on AI reduces developers’ pattern-recognition capabilities and overall skill sets, especially for juniors who need to master the fundamentals.

A fresh approach

Organisations should respond to these challenges by treating AI assistants like junior developers: full of productive and creative potential, but in need of careful oversight. This should serve as an indispensable component of an overall risk management strategy that blends observability, verified developer security skills and benchmarking through the following recommended practices:

• Creating clear rules:
  Guardrails benefit development teams as they seek to observe and identify patterns when reviewing, testing and reworking AI-assisted code for inconsistencies and errors. Team members must commit to standard rule sets and the execution of thorough code review as a non-negotiable part of their jobs, while understanding that their human expertise serves as the first line of defence.
• Providing continuous upskilling:
  With the goal being optimal code review, and teams readily able to discover and fix flaws as they appear, organisations should support hands-on training opportunities that are in line with the Secure by Design Initiative from the Cybersecurity and Infrastructure Security Agency (CISA)^[6]. Secure by Design treats cyber defence as a core business requirement rather than a mere technical feature.

• Redefining AI tool assessment techniques:
  While many tools can crank out usable code quickly, they lack the nuance needed to comprehend specific cyber defence standards, conventions and policies. Because of this, developers should adjust assessments so every LLM is examined using quantitative metrics, real-world performance in pilot programs and alignment to their organisation’s unique requirements.

Industry leaders are pushing for comprehensive assessments that produce so‑called “trust scores”. These are composite metrics that integrate tool usage, vulnerability data and secure‑coding proficiency to quantify how products and teams influence software‑development lifecycle (SDLC) risk.

In the SDLC, shortcuts are not an option. Developers should treat artificial intelligence as a monitored collaborator rather than an autonomous agent; absent that discipline, organisations risk accumulating crippling technical debt.

Consequently, firms must partner with engineering teams to introduce new rules, controls, metrics, assessments and training. Those that do will be best positioned to limit technical debt and mitigate risk while capturing the productivity gains AI can deliver.

[1] https://www.forrester.com/press-newsroom/forrester-predictions-2025-tech-security/

[2] https://www.aikido.dev/state-of-ai-security-development-2026

[3] https://baxbench.com/

[4] https://www.securecodewarrior.com/article/ai-coding-assistants-a-guide-to-security-safe-navigation-for-the-next-generation-of-developers

[5] https://www.harness.io/state-of-software-delivery

[6] https://www.cisa.gov/securebydesign