If we heed the warnings of industry forecasts, 2026 will be the year of artificial intelligence (AI)-driven technical debt: The tech debt for 75 percent of companies will rise to a “moderate” or “high” level of severity this year due to the rapid expansion of AI, according to Forrester.
This extends to the software development community, which is seeing a near-ubiquitous presence of AI-coding assistants as teams face pressures to generate more output in less time. While the huge spike in efficiencies greatly helps them, these teams too often fail to incorporate adequate safety controls and practices into AI deployments. The resulting risks leave their organizations 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.
This isn’t the stuff of hypothetical musings either. The problem is already here: One in five organizations have suffered from a serious security incident directly tied to AI-generated code. Nearly two-thirds of coding solutions produced by large language models (LLMs) turn out to be either incorrect or vulnerable – and roughly one-half of the correct solutions are insecure – meaning LLMs cannot yet create deployment-ready code. In our own research, we’ve found that AI continues to encounter difficulties with subjective, context-based risk factors related to authentication, access control and proper configurations.
The subsequent, accumulating tech debt will not come with a quick and easy fix. The need for speed will bring weighty consequences in the near future, with onerous reworks required to correct mistakes. Traditional tech debt is created when individuals take shortcuts. For developers, the increasingly blind dependence on AI is swiftly intensifying the situation.
It doesn’t help that one-half of them do not use AI assistants approved and provided by IT, thus elevating shadow AI to further diminish transparency in the software development lifecycle (SDLC) and raising the risks of significant compromises. The long-term costs will prove severe: Backtracking, and even reworking takes time and money. Security issues tarnish brand reputation and customer loyalty. In the aftermath of a major incident, accountability comes into play: stakeholders won’t look to blame the tools (and they can’t be held accountable) – they’ll take a hard look at the organization and the teams using the tools.
What’s more, an overreliance on AI reduces pattern retention capabilities and the overall skill sets of developers, especially junior ones who need to master the fundamentals.
So how should organizations and teams respond? Ironically, by treating AI assistants like those 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:
Establishing 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, with the understanding that their human expertise serves as the first line of defense. This will help them stay grounded while distinguishing AI’s value points (greater efficiencies and capacity for breakthroughs) from its potential for harm (failure points and unnecessary risk).
Investing in continuous upskilling and learning. In the interest of optimal code review – with teams readily able to discover and fix flaws as they appear – organizations should support hands-on training opportunities that are in line with the Secure by Design initiative from the Cybersecurity and Infrastructure Security Agency (CISA). Simply stated, Secure by Design treats cyber defense as a core business requirement rather than a mere technical feature or, worse, an afterthought.
The most useful training will include hands-on sessions with real-life scenarios developers routinely encounter. As a result, organizations can implement benchmarking to gauge individual members’ security maturity, and identify where gaps exist that must be addressed.
Redefining AI tool assessments. No tool is the same. Many will crank out usable code quickly, but without the nuance needed to comprehend specific cyber defense 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 organization’s unique requirements.
In the best of possible worlds, comprehensive assessments will lead to what we can call “trust scores” which combine the evaluation of tool usage, vulnerability data and secure coding skills to reveal how these products and teams are impacting SDLC risk.
In the SDLC, there should be no shortcuts. Developers must view AI as a collaborator to be closely monitored, rather than an autonomous entity to be unleashed. Without such a mindset, crippling tech debt is inevitable.
That’s why organizations have to work with teams to implement new rules, controls, metrics, assessments and upskilling. With this, they will best position themselves to minimize tech debt and mitigate risk, while taking advantage of all of the benefits that AI brings.
Related: Vibe Coding’s Real Problem Isn’t Bugs—It’s Judgment
