Generative artificial intelligence (AI) is empowering developers to build, innovate and deploy faster than ever before. But this velocity has a hidden cost — one that doesn’t show up on product roadmaps. It’s a new and expanding class of risk, flaring up in the unseen connections that power intelligent systems — their application programming interfaces (APIs). And it’s growing faster than most traditional governance models can handle.

This isn’t just a theoretical problem — it’s a practical reality that security leaders are grappling with today, according to Akash Agrawal, VP of DevOps & DevSecOps at LambdaTest, an AI-native cloud testing platform providing highly available, instant infrastructure. “With generative AI, developers can now create APIs in a matter of minutes — but this rapid acceleration often bypasses critical safeguards. And what we’re seeing is a surge of auto-generated APIs lacking proper documentation, deviating from organizational standards and overlooking long-term maintainability and security.” 

The result isn’t just messy code — it’s technical debt at scale, embedded deep into the architecture before anyone has time to catch it. It’s an urgent call for a new leadership framework for responsible implementation.  

To move forward safely, Agrawal suggests that leaders need a strategy that matches the speed of AI itself — one built on a foundation of visibility and proactive governance. 

Notable security leaders, engineers and researchers share their insights in this article, offering a practical framework for addressing the real-world security challenges emerging from rapid AI advancements across every layer of the modern software stack. 

An API Landscape in Flux 

Before building a new strategy, it is critical to understand that the rapid adoption of AI is not just adding another layer to existing tech stacks — it is creating a new, more complex and often chaotic reality for how software is built and connected. This change can be best described as an ‘API explosion’. The landscape isn’t just escalating; it’s becoming fragmented, according to Daniil Mazepin, senior engineering manager at Teya. And this fragmentation, he points out, creates a new kind of risk that is much harder to manage than traditional tech debt. 

With AI systems entering the mix — models calling APIs, APIs wrapping models and auto-generated integration layers — the landscape isn’t just growing, it’s splintering. And what’s especially concerning is how often API creation now becomes a side effect of experimentation. Someone spins up a service ‘just to test a model’ and that endpoint quietly becomes a dependency. The result? APIs that no one fully owns, documents or reviews — yet they end up in critical paths. This creates not just tech debt, but technical ambiguity, which is far harder to detect and clean up. 

“This shift requires leaders to rethink their risk models and reshape their governance conversations. The discussion needs to evolve beyond tracking ‘technical debt’ to identifying and mitigating ‘technical ambiguity’ — the business risk posed by critical, unowned and undocumented API dependencies created as a side effect of AI experimentation,” underscores Yaroslav Panasyuk, engineering manager at Agoda. “And while this pressure is palpable, the wave is not hitting every organization with the same force. For many, the most significant changes are still emerging, creating a crucial window for proactive preparation. This highlights that while the crisis is not yet acute for everyone, it is certainly coming.” 

“In my experience,” Panasyuk shares, “the rapid adoption of generative AI technologies hasn’t yet caused a large-scale explosion in API growth, but we’ve started to observe early signs of this trend. Currently, most AI-generated code is still concentrated in internal application logic rather than exposed API endpoints. While the issue isn’t yet critical, we foresee significant challenges emerging soon.” 

This serves as a stark reminder that waiting for the ‘explosion’ to become a crisis is not a viable option. Leaders should use this time to proactively embed automated governance and real-time monitoring strategies into their development pipelines, ensuring that they are prepared to manage the anticipated increase in API proliferation responsibly. 

Vibe Coding Paradox 

This splintering of the API landscape is a result of AI fundamentally changing the process of creation itself. The traditional barriers to entry are gone. It no longer takes deep institutional knowledge to add a simple API to a mature codebase. This new, faster and more intuitive style of development has a trendy name — ‘vibe coding’. However, this new era is characterized by a paradox where the very speed and ease that empower creation have given rise to a new class of hidden risks. “The focus on momentum often comes at the expense of structure, creating blind spots that can undermine quality and security,” comments Benji Kalman, co-founder and VP of Engineering at Root, describing how this shift has changed everything — right down to how the company tests its own work. 

Kalman further explains, “AI-powered coding has torn down a large part of this barrier, at least superficially. One of the biggest selling points of AI-powered code for engineers is the fact that it is able to write reasonable tests for existing code. However, this issue is further compounded when we heavily rely on AI agents to generate the tests. If the code is already broken or poor to begin with, then a simple prompt to create a test to validate it will simply spit out a test that validates the broken implementation — and the cycle here is a downward one.” 

Leaders must mandate a ‘human-in-the-loop’ verification process for all AI-generated code — especially for APIs. This includes a crucial policy — code generated by an AI cannot be tested and validated solely by another AI. A human engineer must be responsible for the integrity of the final test plan to break the ‘downward spiral’ of quality. 

But even with human oversight, the nature of the generated code itself presents its own challenges, as Ravi Shankar Goli, lead principal software engineering manager at Microsoft, points out. “Humans are exceptionally good at generalization, and a good developer writes less code with proper aggregation and generalization. I see Vibe code generates a lot of duplicate code. If one is careless, we may end up with redundant code, which may lead to operational and maintenance problems. Additionally, the generated code may not follow company standards — such as insecure patterns and hardcoded keys — and may inject vulnerabilities into the code. This may lead to large technical debt if one takes shortcuts.” 

Your development and platform teams must be equipped with automated tooling that specifically scans AI-generated code for redundancy and insecure patterns before it can be merged. This serves as an essential guardrail, ensuring that even as developers move fast, they are protected from common AI pitfalls, such as hardcoded secrets or duplicative logic, which create long-term maintenance burdens. 

The Shadow APIs Behind AI Systems 

The result of this high-speed, low-oversight environment is the emergence of a new and dangerous class of risk that requires specific and urgent attention from every technology leader. These are not your traditional shadow IT problems, they are ‘Shadow AI APIs’ — endpoints and connections created on the fly during experimentation, often without documentation, oversight or basic security hygiene. The danger lies in how they function. 

Ori Goldberg, chief technology officer and co-founder of Pynt, explains that these APIs are more than simple connectors — they are active and integral parts of the AI execution path. “In an AI-driven stack, APIs are not just integration points; they are execution paths, decision triggers and data access portals. It could be a large language model (LLM) endpoint that returns customer-specific responses or a service exposing model configurations. Every API is a potential liability if left untracked. Shadow APIs, short-term test endpoints and undocumented model access routes are becoming the new normal. Without continuous discovery and validation, these endpoints silently expand the attack surface.” 

This reframing is critical for every Chief Information Security Officer and VP of Engineering. Your security team’s definition of the corporate attack surface must be immediately expanded to include these new AI-driven execution paths and data access portals — treating each one with the same scrutiny as a public-facing application. This is especially true with AI systems becoming increasingly complex and autonomous. Agrawal warns that the next wave of security threats will directly target these interconnected, agentic systems. 

Speaking from his engineering experience at LambdaTest and leading the security for KaneAI, an end-to-end software testing agent, Agrawal explains — “As AI systems become more complex, using multiple LLMs, they are highly susceptible to prompt injection attacks, even when agents do not publicly share all communications. It’s like a chain reaction within multi-model systems because you’re getting an infection spreading from one model to another. This is why the zero-trust policy existed even before AI, and we’ll soon see something similar emerge as a best practice for AI models and agents.” 

To counter this, Agrawal strongly recommends that security leaders prioritize developing new, specialized red-teaming skills focused on testing AI for prompt injection and similar LLM vulnerabilities that can trigger these chain reactions. It’s no longer enough to secure the perimeter of your applications — you must begin architecting a zero-trust framework that validates and controls the interactions between AI agents and models within your systems. 

Step Zero: The Universal Agreement on API Visibility 

While the challenges are significant and the threats are new, a clear and unanimous consensus has emerged from leaders across the industry. Before you can automate governance, build a future-proof strategy or even begin to manage risks, you must take a single, foundational step. This is the prerequisite for any responsible AI initiative, and it all comes down to a simple principle — you cannot govern, manage or secure what you cannot see. 

“This is why a real-time, accurate inventory of every API touching your systems, data and models is non-negotiable,” says Joshua Scarpino, chief information security officer at TrustEngine. “This isn’t just a best practice; it’s the absolute starting point.” Visibility is step zero. Without it, you’re flying blind. 

Before you can govern AI, you need a real-time, accurate inventory of every API touching your systems, data and models. “You can’t enforce policies, detect anomalous behavior or assess compliance risks… If I had to give one piece of advice — treat every API as an enterprise asset, not a developer product,” Scarpino suggests. Because API discovery and security need to run at the same speed as AI innovation. Otherwise, you’re leaving yourself open to unmanaged risk. 

As an immediate first step, leaders must invest in and deploy a continuous API discovery and visibility tool across their entire technology stack — from on-prem servers to multi-cloud environments. Agrawal also stresses that this cannot be a static, once-a-quarter scan; it must be a living, breathing inventory that keeps pace with the speed of AI-driven development. This is the only way to close the gap between your security policies and the reality of your API sprawl. 

Building a Future-Proof Strategy 

Achieving visibility is the critical first step and not the final destination. A true inventory of your APIs is the map that allows you to navigate — but you still need a plan for the journey. A modern, resilient strategy for the AI era requires building new capabilities on top of that foundation of visibility, ensuring that governance is not a reactive afterthought but an automated and proactive part of your development culture. 

The most effective way to do this is to embed governance directly into the workflows your developers already use, says Panasyuk of Agoda. He recommends — “A future-proof API strategy in the AI era involves proactively embedding automated governance and compliance checks directly within the development process. This includes real-time validation of architectural standards, security protocols and performance metrics throughout continuous integration and delivery pipelines. Such visibility enables early detection and remediation of potential issues, maintaining system stability, compliance and adaptability to evolving technological landscapes.” 

This approach requires platform and DevOps teams to begin treating API governance as code — integrating automated checks for security, standards and compliance into the same continuous integration/continuous deployment (CI/CD) pipelines used to build and deploy software. But this in-workflow automation must be part of a larger, more holistic plan. 

According to Balkrishna Patil, technology transformation manager at Ernst & Young, organizations must treat AI systems with the same rigor as any other enterprise software component. “A future-proof strategy requires managing the API life cycle of AI endpoints, implementing observability and security monitoring right from the beginning and ensuring alignment with data governance and access control policies. It features integrated policy enforcement, encompassing rate limits, prompt filtering and lineage tracking.” 

To put this into practice, Patil suggests that leaders should form a dedicated, cross-functional ‘AI Governance Task Force’ including representatives from security, platform engineering and product teams. This group’s mandate is to create and enforce a unified strategy for the entire life cycle of AI-driven services — from initial design to decommissioning. This human-led governance is also your best defense against new, emerging threats that automated tools may not yet recognize. 

Cezar Grzelak, chief science officer at Versos AI, astutely warns that the very nature of how AI consumes information is creating a new attack vector. “There are realistic risks of malicious APIs being published that mimic real APIs from legitimate vendors. Those might get built and exposed purely to be picked up by LLMs during vibe coding sessions. We are very likely to see an explosion of activities similar to SEO but targeted at influencing API choice decisions made by LLMs.” 

Organizations must educate their developers on this new threat of ‘API SEO’ and malicious mimicry. It is essential to establish a formal vetting and approval process for any new third-party or external APIs before they can be incorporated into AI-driven projects, ensuring that your organization’s tools are not inadvertently relying on a compromised dependency. 

Forward-Looking Leadership 

Responsible innovation is possible, but it requires a deliberate strategy — where governance and security are architected to run at the same speed as AI itself. This demands not only new tools but also new skills and a new mindset. 

Goli of Microsoft advises that you must prepare your teams for a different kind of threat. “I don’t see any change in the need for code compliance and security checks. Ensure proper automation is in place to identify the vulnerabilities early, and most importantly, new red-team skills are necessary to test these generative AI applications.” 

The imperative is to invest in your people. Your security and engineering teams must be upskilled and empowered to think like adversaries in this new landscape — testing and validating AI systems for weaknesses that traditional security practices would miss. This shift in skills is part of a larger evolution in technology leadership. In an era where APIs are the connective tissue of your business and AI is the intelligence driving it, the role of the security leader must also transform. 

It is no longer enough to be a gatekeeper of defense, exposits Randolph Barr, chief information security officer at Cequence Security. “Security leaders must evolve into business resilience leaders. The role is no longer just about defense — it’s about enabling responsible AI adoption that balances speed, trust and long-term risk management in an API-first world.” 

Embracing a strategy of proactive visibility and automated governance is not a cost center, Barr asserts. “But it is the foundational investment in business resilience, enabling your organization to harness the immense power of AI safely and confidently.” The leaders who recognize that governing their APIs is governing their AI will be the ones who innovate responsibly and build a lasting competitive advantage.