Security leaders have long prioritized endpoints, networks, and identities. That focus remains necessary, but it is no longer sufficient.
Recent data points to a concrete shift in attacker behavior. APIs now account for 43% of newly added vulnerabilities in CISA’s Known Exploited Vulnerabilities catalog. Additionally, 36% of published AI vulnerabilities (and the same share of exploited AI vulnerabilities) involve APIs. These figures reflect a broader change in how risk is distributed across modern infrastructure. APIs have moved from integration layer to primary attack surface, and AI adoption is accelerating that shift.
APIs Have Become a Primary Entry Point
APIs were designed to enable speed and interoperability. They allow organizations to scale services, connect systems, and deliver capabilities efficiently. Those same properties make them attractive targets.
Unlike traditional web applications, APIs expose business logic and data directly. They are built for machine-to-machine communication, which reduces friction but also reduces visibility into anomalous behavior.
Consider a common example: an account-lookup API that returns more data than intended due to misconfigured access controls. An attacker can enumerate users or extract information at scale without triggering conventional detection mechanisms. The interface was designed for efficiency; the security controls were not applied consistently.
The growing share of exploited vulnerabilities tied to APIs reflects this dynamic. Attackers are pursuing interfaces that provide direct access to data and functionality, particularly when those interfaces are inconsistently secured or poorly inventoried.
Scale compounds the problem. Many organizations operate hundreds or thousands of APIs, including legacy endpoints and undocumented shadow APIs that are not tracked or monitored. Maintaining consistent security controls across that landscape is operationally difficult, and the gaps are often unknown until they are exploited.
AI Is Built on APIs, and Inherits Their Risks
AI capabilities are almost exclusively delivered through APIs. Large language models (LLMs), retrieval systems, and enterprise AI integrations all depend on APIs to expose and consume their functionality. As a result, AI security is not separate from API security. It is a direct extension of it.
The data supports this conclusion. More than a third of AI-related vulnerabilities involve APIs, whether through weak authentication, insufficient input validation, or excessive permissions. In many cases, these are familiar API security failures that carry greater consequences in AI contexts.
When an API provides access to a model that generates outputs, makes recommendations, or triggers downstream actions, the potential impact of a security gap increases. Inadequate input validation can enable prompt injection — an attacker manipulates inputs to alter how the model interprets instructions. Overly permissive access controls can expose sensitive data the model was not intended to surface. Poorly designed interfaces can allow systematic probing of model behavior.
These risks are not hypothetical. They are measurable, and they are present in production environments today.
Also Read: CIO Influence Interview With Jake Mosey, Chief Product Officer at Recast
Agentic AI Introduces New Complexity
Agentic AI systems are built to act, often by interacting with multiple external services through APIs in sequence. This creates interdependencies that traditional security models are not designed to evaluate.
One area of growing concern involves coordination protocols that allow AI systems to orchestrate across tools and data sources. These architectures expand capability, but they also expand the attack surface in ways that are difficult to enumerate and monitor. Vulnerabilities tied to these types of multi-system interactions are increasing, and they already represent a meaningful share of identified AI risks.
The failure mode is not always obvious. A single misconfigured permission or unvalidated input can allow an attacker to influence an agent’s behavior, causing it to retrieve data it should not access, or trigger actions the system’s designers did not intend. The more capable and interconnected these agents become, the broader the downstream consequences of any single weakness.
Why Traditional Approaches Fall Short
Most security programs were designed before APIs became the dominant integration pattern, and well before AI systems became operational infrastructure.
Perimeter-based defenses assume a clear boundary between internal and external systems. APIs erode that boundary by design. Traditional vulnerability management focuses on known software flaws. It does not reliably detect issues in API logic, authentication configuration, or data exposure patterns. Scanning for known CVEs does not surface an API that returns excessive data to any authenticated caller.
AI introduces additional blind spots. Security teams often lack complete visibility into how models are integrated, what data they access, or how they interact with other services. Without that visibility, risk cannot be accurately assessed or managed.
Moving Toward an API-Centric Security Model
Closing these gaps requires a concrete shift in how organizations approach risk.
-
Inventory.
Start with comprehensive visibility into the API ecosystem. This includes managed APIs, legacy endpoints, and shadow APIs that have accumulated outside formal processes. You cannot secure what you cannot see.
-
Controls.
Apply security standards consistently across all APIs: authentication, authorization, input validation, and behavioral monitoring. Logic-level vulnerabilities require methods beyond automated scanning. Manual review and targeted testing remain necessary.
-
AI-specific evaluation.
Treat AI systems as part of the API risk surface. Understand how models are accessed, what permissions they have been granted, and how they interact with other services. Evaluate explicitly for prompt injection exposure and unintended agent behavior.
-
Continuous adaptation.
Both API ecosystems and AI capabilities are evolving quickly. Static security assessments and point-in-time reviews will not keep pace. Security programs need ongoing mechanisms to detect new exposure as systems change.
A Shift in Mindset
The underlying shift is straightforward: APIs define much of today’s attack surface, and AI systems are built on top of that surface. For security practitioners, this is not an abstract strategic concern; it is an operational reality that requires expanding the scope of existing programs. Organizations that establish consistent controls across their API ecosystems, and that account for the specific risks introduced by AI dependencies will be better positioned to manage the exposure that comes with both.
Treating APIs as first-class assets is no longer optional. It is the baseline for effective security.
About Wallarm
Wallarm is the API security platform that is the fastest, easiest, and most effective way to stop API attacks. Customers choose Wallarm to protect their applications and AI agents because the platform delivers a complete inventory of APIs, patented AI/ML-based abuse detection, real-time blocking, and an API SOC-as-a-service.
Catch more CIO Insights: The New Business of QA: How Continuous Delivery and AI Will Reshape 2026
[To share your insights with us, please write to psen@itechseries.com ]
