According to recent research, 82% of organizations now carry security debt, an increase from the previous year. Security debt can be defined as vulnerabilities that accumulate and remain unremediated over time.
Part of the reason for this increase is that two trends are accelerating simultaneously. AI coding assistants are being used across engineering teams faster than security can keep up, and regulators are also putting increasing pressure on companies to provide evidence of software security.
However, these two factors won’t resolve themselves, and left unaddressed, could become an organization’s next incident report.
The Debt Not Seen on a Balance Sheet
A difference often exists between the security standard an organization intends to maintain and the one that actually gets deployed. Part of the reason for this is that security debt builds up gradually, such as through missed reviews, unpatched systems, and shortcuts taken under deadline pressure that don’t get fixed.
In the past, this accumulation grew at a manageable and more predictable pace. Development teams moved fast, security teams pushed back where they could, and the balance, even though not perfect, still held. With the advent of AI-assisted development, that’s no longer the case.
For example, code that once took days to write now only takes hours. Features that previously needed senior engineers can now be implemented by junior developers with an AI copilot. Essentially, entire microservices can be generated, tested, and deployed within a single sprint.
This acceleration, though valuable, is creating a security debt crisis that most organizations haven’t fully come to terms with yet.
Why AI-Generated Code Carries Hidden Risk
There’s no doubt that AI coding assistants are valuable and improve developer productivity. The problem is that they generate plausible-looking code rather than secure code.
These models are trained on vast repositories of public code, including years’ worth of Stack Overflow answers, GitHub projects, and tutorials that contain insecure patterns.
When a developer asks an AI assistant, for example, to write an authentication function, the model doesn’t have a proper understanding of the threat model. It produces something that works but doesn’t take security into account.
Studies have already shown that nearly half of AI-generated code contains security vulnerabilities. The critical issue is that AI code is being written faster than any organization can review it carefully.
The Cost of Moving Fast
Shipping quickly has become the defining metric of a well-performing engineering team. Faster time-to-market and more features are obviously valuable, but this kind of speed without security review is just another way to accumulate debt.
Manual code reviews now need to process a higher volume of changes, and static analysis tools give more findings because there is more code to scan. When this happens, developers may dismiss alerts they don’t fully understand or don’t have time to remediate, as they’re under pressure to maintain delivery speed. It leads to security backlogs and is seen as something to clean up later, rather than an immediate risk.
This dynamic becomes self-reinforcing because the faster development teams move, the more unresolved issues accumulate. As complexity grows, reversing past decisions becomes increasingly expensive, and AI-generated logic becomes embedded in systems where design rationale was never clearly articulated.
Security debt, in this context, is no longer a collection of isolated vulnerabilities, but something systemic at both the code and architectural levels.
The CISO’s Dilemma
Slowing development isn’t really an option, nor is prohibiting AI use. Scaling manual review linearly with code generation is unsustainable.
At the same time, regulatory and governance expectations are intensifying. Frameworks and regulations such as NIS2 require organizations to demonstrate control over software risk, not only document policies.
The traditional model, where security operates downstream as a specialized review function, does not scale in an AI-accelerated environment. Security capability must shift closer to the point where code is written and systems are designed. It must become embedded within everyday engineering decisions.
Rethinking Security Debt as a Skills Problem
The instinct when facing security debt is often to add more tooling, such as scanners, SBOMs, vulnerability management platforms, SAST, and DAST tools. These are still important, but developers need to know why a vulnerability is dangerous.
If they don’t have security training, they might dismiss scanner alerts, copy suggested remediations without understanding, or introduce the same issue in a different form in the next sprint.
Essentially, the root cause of AI-accelerated security debt is a deficit in security knowledge at the point of development. When developers understand secure coding principles not as abstract compliance requirements but as skills they’ve practiced, they start applying that insight to AI-generated code.
At the same time, teams that are practiced in threat modeling will find it easier to see how new AI-generated components can change system boundaries, introduce new trust relationships, or expand exposure to third parties.
This doesn’t happen through annual compliance training or reading documents, but rather through hands-on practice with realistic scenarios, the kind of learning that builds intuition, not just awareness.
Building a Security Debt Reduction Strategy
Organizations that are adapting effectively tend to focus on several reinforcing principles.
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Make security debt measurable: Track the number of security issues introduced and resolved in each sprint, and share the trend with engineering leadership. Security debt should be part of planning conversations.
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Establish AI code review standards: Have clear expectations for reviewing AI-generated code before it’s merged, even if that’s only a list covering authentication, input handling, data exposure, and dependency risks.
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Prioritize the highest-risk surfaces: Focus remediation efforts on code where AI-generated logic carries the greatest risk. For instance, code that interacts with authentication, authorization, sensitive data, external integrations, and public-facing API.
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Invest in developer security education: Aside from awareness campaigns, provide structured, hands-on training that requires developers to find and fix vulnerabilities in realistic scenarios.
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Integrate security into the AI workflow: Encourage developers to prompt AI assistants about security implications during code generation and establish internal guidance for producing more security-conscious output.
Moving Security Closer to the Code
If AI-accelerated security debt arises from a lack of secure coding skills and inconsistent risk evaluation during design, the response must address both aspects.
SecureFlag enables teams to build applied secure coding skills through hands-on, lab-based learning experiences in real development environments.
Developers engage directly with working code by identifying vulnerabilities, exploiting them in controlled settings, and mitigating them, all in the languages and frameworks they use daily.
At the architectural level, ThreatCanvas supports collaborative, structured threat modeling, so teams can identify systemic risk before it becomes embedded in production systems. Organizations reduce the likelihood that AI-accelerated feature development introduces unseen architectural weaknesses.
These capabilities help organizations ensure that as development speed increases, security capability scales with it, and security debt is reduced.
