How AI coding adoption can create security blind spots [Q&A]

Perhaps more than in any other area, coding has seen huge changes from the introduction of AI tools. But when code is created by AI it can allow vulnerabilities to creep in

Magnus Tagtstrom is corporate VP, AI transformations at Iterate.ai, we spoke to him to learn more about the problem and what can be done to address it.

BN: As enterprises rush to adopt AI coding tools for their speed benefits, what security blind spots are organizations missing as they accelerate development with AI assistance?

MT: Most are still underestimating just how fast their own development environments have changed. They’re moving from human-speed coding to AI-speed generation, and that shift has created a gap wide enough for serious vulnerabilities to slip through. When a coding assistant writes thousands of lines in minutes, every new function, dependency, or API call becomes a potential exploit surface.

But the biggest blind spot is the mindset that most AI coding tools get treated like autocomplete, as something that helps finish a line of code faster. In reality, they’re autonomous systems capable of making architectural decisions. That means the traditional assumption (that humans review every meaningful code change!) no longer holds. Enterprises are discovering too late that the new attack vectors aren’t in the code they reviewed, but in the code the AI generated between reviews.

BN: How has the shift from hundreds of lines of code per day to tens of thousands per minute fundamentally changed the threat landscape for enterprise software?

MT: It’s multiplied both the size and the speed of potential vulnerabilities. When code creation accelerates by orders of magnitude, security debt grows just as fast. A single flaw can now propagate across multiple microservices or pipelines before a human ever even sees it.

Welcome to the era of velocity-driven risk. The traditional bottleneck in software was always generation. Now, it’s validation. Security processes built around post-commit analysis can’t catch up when the system can push complete modules every few minutes. The result is what I call ‘cascading vulnerabilities’, as issues replicate through interconnected systems faster than any security review cycle can contain.

BN: Traditional code review processes were designed for human-speed development, but what happens when that same review approach meets AI-velocity coding?

MT: They collapse under their own latency. Traditional workflows assume time exists between code generation and deployment, but AI removes that buffer. Code reviews become perpetual backlog. Organizations either delay releases (defeating the entire point of AI productivity) or they push unverified code to production. But neither path is sustainable.

The answer isn’t slowing AI down, but bringing validation up to AI speed. That means embedding real-time security analysis into the generation workflow itself. So instead of reviewing after the fact, security checks should happen in parallel with creation. It’s a problem we’ve set out to solve with AgentOne, which orchestrates multiple specialized agents that generate and validate code simultaneously. AI should never work alone, it should collaborate with other agents that continuously test, validate, and enforce standards in real time.

BN: You mentioned many enterprises are treating AI coding assistants like enhanced autocomplete tools. What’s the fundamental security misconception behind that approach?

MT: When enterprises view AI coding tools as autocomplete, they overlook the fact that these systems can silently import vulnerabilities or leak internal logic to public models. They choose libraries, generate authentication flows, and handle sensitive data (all autonomously).

The misconception comes from assuming these assistants are simply productivity accelerators whereas they are active participants in software architecture. That’s a major governance challenge because the AI is often operating without full context, especially when it’s hosted externally. Short of proper guardrails, that’s like hiring a contractor who can code faster than your entire team but never passed a background check.

BN: Then how should enterprise teams adapt their processes and tooling when development velocity increases by orders of magnitude through AI assistance?

MT: They need to think in terms of orchestration, not automation. Traditional pipelines are linear: generate, review, test, deploy. AI-enabled pipelines must be parallel: generate, test, and validate simultaneously.

The first step is architectural, by embedding real-time validation agents into every stage of code generation. These agents handle static analysis, memory-leak detection, OWASP compliance checks, and architectural alignment continuously, not sequentially. Second, teams should adopt systems that preserve long-form context (millions of tokens, not thousands) so the AI understands how a new line of code affects the entire architecture.

Most importantly, leadership must realign incentives. The new definition of developer velocity should be secure velocity. You need speed that scales, but without increasing exposure.

BN: What are the specific vulnerabilities that emerge when AI generates code without real-time security validation, and why can’t traditional static analysis catch them?

MT: AI-generated code tends to create chains of minor logic flaws that interact in unexpected ways. You might have safe individual components that, when combined, expose data or permissions through subtle dependency interactions. Traditional static analysis was never designed to see across those boundaries.

There’s also the context problem. Static analysis looks at code in isolation, but AI-generated systems often depend on context (e.g. how a service interacts with others, what data it handles, or which authentication pattern it follows). Without continuous architectural awareness, analysis tools can miss the exact points where vulnerabilities manifest. That’s why real-time, in-workflow validation is critical. It sees the code as it’s being built, with full situational context.

BN: Do you see AI-generated code becoming the primary attack vector for enterprise breaches, and how can organizations prepare for this shift?

MT: Yes, I view that as inevitable. The attack surface is expanding faster than security automation can adapt. We’ll soon see breach post-mortems where the root cause isn’t a human developer’s mistake but an AI-generated function that no one ever reviewed.

Preparation starts with acknowledging every AI assistant is effectively a new team member whose work must be continuously verified. Enterprises need to adopt AI governance frameworks that define how and when generated code is used, what validation occurs automatically, and who maintains accountability.

The defensive strategy will mirror the offensive trend: security teams will use AI agents that operate as fast as development AIs do, continuously probing and validating systems in real time. The next generation of cybersecurity will be AI vs. AI, running at the same velocity.

BN: So what role should regulatory frameworks play in governing AI-assisted development, especially for areas like critical infrastructure and financial services?

MT: Regulation will have to evolve from focusing on data privacy to focusing on model governance. We’ll need frameworks that certify not just what data was used to train a model, but how that model makes architectural decisions during development.

In critical sectors, regulations should mandate transparency in AI coding workflows (proof that every generated component passed automated validation and human oversight). Financial and healthcare systems, clearly, can’t rely on black-box code generators. Regulators will also need to recognize the value of local and on-prem AI systems, where enterprises maintain full control of their data and development process.

The right balance of regulation won’t slow AI down but will make AI-driven innovation far more sustainable. When security and governance scale with velocity, enterprises can embrace AI development confidently rather than fear its risks.

Image credit: Aleksandar Ilic/Dreamstime.com