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Security teams are under growing pressure as APIs become the dominant interface for modern applications and integrations. According to the 2025 State of the API Report, 82% of organizations have adopted an API first approach, yet a majority of developers also cite unauthorized API activity and sensitive data exposure as top security concerns. 51% worry about unauthorized agent calls and 49% about sensitive data access via API consumers such as AI agents.
At the same time, API security incidents are widespread. A recent industry study found that 84% of security professionals experienced at least one API security incident in the past year, with average remediation costs approaching $600,000 in the United States alone.
Industry research from Gartner and others highlights that API abuse is now one of the most frequently exploited vectors across web applications, as exposed APIs expand attack surfaces and outpace traditional security controls. In this environment, many organizations have turned to artificial intelligence and machine learning to augment WAF and WAAP protections at scale.
However, the operational realities of AI in these systems are often misunderstood. AI models in WAFs operate on limited contextual data at the edge, and they make probabilistic judgments based on traffic patterns rather than observing execution outcomes inside runtime environments. That constraint means AI driven controls can struggle with abuse that looks normal at the request layer but causes harm once the API processes it.
Before relying on machine learning as a primary security control, it is necessary to understand exactly where these models provide value, where they fall short, and why grounding AI driven decision making in runtime visibility is essential for defending APIs in production.
What AI Is Actually Used For in WAFs Today
In most WAF and WAAP platforms, artificial intelligence plays a limited but clearly defined role. Machine learning models are primarily used to observe traffic characteristics over time and establish statistical expectations for how an application or API is accessed. The output of these models is not intent or understanding, but probability.
A common use case is traffic profiling and baseline formation. Requests are grouped based on attributes such as request frequency, header composition, payload size, geographic distribution, and authentication state. From these observations, systems derive expected ranges of behavior and identify deviations that fall outside learned norms.
Machine learning is also widely used for anomaly scoring. Instead of matching requests solely against predefined signatures, models assign relative risk scores based on how closely a request aligns with historical patterns. This allows WAFs to surface previously unseen variations without requiring explicit rules for every scenario.
Another practical application is alert management. By correlating repeated patterns across large volumes of traffic, ML systems can suppress duplicate alerts and elevate signals that appear statistically uncommon. For many security teams, this reduction in alert noise is the most immediate operational benefit of AI driven controls.
Some platforms extend machine learning into adaptive rate limiting. Rather than enforcing fixed thresholds, models adjust limits dynamically based on observed client behavior and historical usage. This approach can help distinguish between legitimate surges in traffic and simple volumetric abuse.
Across all of these use cases, the underlying constraint is consistent. The models operate almost entirely on request level metadata captured at the edge. They treat requests as discrete events, without visibility into how those requests are processed once they enter the application stack.
As a result, AI in WAFs is effective at identifying surface level irregularities, but it does not observe execution paths, object access, or data handling inside APIs. Business logic abuse, object level authorization failures, and sensitive data exposure often appear indistinguishable from normal traffic at the request layer. This boundary defines both the strength and the limitation of AI driven WAF protection.
Where Machine Learning Helps
Machine learning provides the most value in WAF and WAAP environments when the problem being addressed is statistical rather than semantic. At scale, these systems are effective at identifying deviations in traffic behavior that would be difficult to detect through static rules alone.
One area where ML consistently helps is in managing variability. Modern applications experience fluctuating traffic patterns driven by releases, integrations, geographic expansion, and automated clients. Machine learning models adapt more readily to these shifts than fixed thresholds, allowing security teams to avoid constant manual tuning.
ML also performs well when dealing with volumetric patterns. Sudden changes in request frequency, abnormal fan out across endpoints, or unexpected concentration of traffic from particular client profiles are all scenarios where anomaly detection can surface issues quickly. In these cases, the goal is not to understand intent, but to recognize that the shape of traffic has changed in a way that warrants attention.
Another benefit lies in operational efficiency. By clustering similar events and prioritizing outliers, machine learning reduces the cognitive load on security teams. Analysts spend less time triaging repetitive alerts and more time investigating signals that represent genuine deviation from baseline behavior.
In these contexts, machine learning functions as a force multiplier. It improves scalability, reduces noise, and helps surface unexpected patterns across large datasets. Crucially, it does so without needing to understand the underlying business logic of the application.
Where AI Breaks Down in API Security
API abuse rarely presents itself as statistically abnormal traffic. Many of the most damaging API attacks are low volume, authenticated, and structurally indistinguishable from legitimate requests. From the perspective of an edge based model, they often look entirely normal.
Business logic abuse is a clear example. Requests follow valid schemas, respect authentication requirements, and arrive at expected rates. The malicious behavior emerges only when those requests are combined in specific sequences or applied to unintended objects. Machine learning models that operate on individual requests have no visibility into this intent.
Object level authorization failures present a similar challenge. When an attacker accesses resources belonging to other users by manipulating identifiers, the requests themselves often fall well within learned baselines. Without insight into which objects are accessed and whether access should be permitted, AI driven systems cannot reliably distinguish abuse from normal usage.
Sensitive data exposure further highlights the limitation. AI models at the edge do not observe what data an API returns, how it is processed downstream, or whether regulated information is involved. As a result, data exfiltration through legitimate looking APIs can persist undetected even when AI driven protections are in place.
These failures are not the result of insufficient model sophistication. They stem from a structural constraint. AI systems deployed in WAFs reason over request patterns, not execution outcomes. They infer risk from how traffic looks, not from what the application actually does in response.
As APIs increasingly carry business critical logic and sensitive data, this disconnect becomes more pronounced. Understanding where machine learning stops being effective is essential before treating AI driven WAF controls as a comprehensive API security solution.
AI at the Edge vs AI at Runtime
Most AI driven security controls in WAF and WAAP platforms operate at the edge. Their models observe incoming requests before those requests are processed by the application. This positioning offers scale and low latency, but it also defines what the models can and cannot see.
At the edge, AI evaluates request structure, frequency, headers, payload characteristics, and client attributes. It can infer whether traffic deviates from historical patterns, but it cannot observe what happens after the request is accepted. Execution paths, internal service calls, object access decisions, and data handling remain outside its field of view.
Runtime systems operate in a different plane. They observe how APIs behave once requests are processed, which code paths are executed, which objects are accessed, and what data is read or written. This perspective reveals impact rather than probability.
The distinction matters because many API security failures are not visible at the request layer. An edge based model may correctly classify traffic as normal while the application performs unintended actions. From a security standpoint, the risk materializes during execution, not during inspection.
AI at the edge is therefore well suited for broad pattern detection and traffic shaping. Runtime observation is necessary to understand consequences. Treating these approaches as interchangeable leads to misplaced confidence in AI driven controls.
New Risks Introduced by AI Driven WAFs
AI driven WAFs introduce operational risks that are often overlooked when focusing solely on detection capability. One such risk is model drift. As applications evolve, traffic patterns change, and models trained on historical data may gradually lose accuracy. Without strong feedback mechanisms, this drift can result in increased false positives or missed abuse.
Another risk is adversarial adaptation. Attackers can probe AI driven systems over time, learning which behaviors trigger enforcement and which pass unnoticed. In environments where AI decisions are opaque, this feedback loop often favors the attacker rather than the defender.
There is also a risk of overconfidence. The presence of AI can create an assumption that complex abuse will be detected automatically. This assumption may lead teams to deprioritize deeper inspection of API behavior, even though AI models lack visibility into execution context.
Finally, enforcement decisions made without execution evidence can have business impact. False positives affecting legitimate API consumers can disrupt integrations, break client workflows, and erode trust. When teams cannot explain why traffic was blocked, remediation becomes difficult.
These risks do not suggest that AI should be removed from WAFs. They highlight the need for clear boundaries around what AI can reliably decide on its own.
Why AI Must Be Grounded in Runtime Visibility
AI driven controls are strongest when their decisions can be validated against what actually happens inside an application. Without runtime visibility, AI judgments remain probabilistic and difficult to defend during incident response, audits, or post breach analysis.
Runtime visibility provides the missing context that edge based models lack. It shows which APIs exist in production, how they are invoked, which objects they access, and what data they handle. This information allows security teams to correlate AI detected anomalies with real execution outcomes.
This is where runtime API security platforms such as Levo become relevant. Levo’s API Inventory continuously discovers live APIs based on observed execution rather than static specifications, helping ensure AI systems operate with an accurate understanding of attack surface. Sensitive Data Discovery identifies where regulated or personal data is actually processed by APIs, grounding risk assessments in data reality rather than inference. API Monitoring connects incoming requests to downstream execution paths, enabling teams to see which anomalies resulted in meaningful state changes or data access.
When AI driven WAF alerts are paired with runtime evidence, security decisions become more defensible. Models can be tuned based on observed impact, false positives can be validated against execution data, and enforcement can focus on activity that produces real risk.
In this model, AI remains an important component, but it is no longer asked to reason beyond its visibility.
Practical Guidance for Using AI in API Security
AI should be treated as an augmentation layer in API security, not as an authority. Its value lies in scale and pattern recognition, not in understanding business logic or data sensitivity. Security teams evaluating AI driven WAF or WAAP capabilities should begin by clearly defining what decisions are appropriate to automate and which require additional context.
AI performs best when applied to problems of volume and variance. Use it to manage traffic baselines, identify abnormal request patterns, and reduce alert noise across large API estates. Avoid relying on it to determine intent, authorization correctness, or data exposure risk without corroborating evidence.
Runtime visibility should be considered a prerequisite for trusting AI driven decisions. Without insight into execution paths and data handling, it is difficult to validate whether an anomaly represents actual risk or benign variation. Teams should ensure that AI alerts can be traced to concrete outcomes, such as object access, state changes, or sensitive data interaction.
When assessing vendors, security leaders should ask how AI models are trained, what signals they observe, and how decisions are validated over time. Claims of autonomous protection should be examined closely, particularly in environments dominated by authenticated API traffic and complex business workflows.
Finally, AI driven controls should be continuously reviewed. Models must be recalibrated as applications evolve, and enforcement decisions should be tested against real production behavior. AI reduces effort, but it does not remove the need for oversight.
Conclusion
AI has a meaningful role in modern WAF and WAAP platforms, particularly in managing scale and variability. Its strengths lie in identifying statistical irregularities across large volumes of traffic and helping security teams operate more efficiently.
At the same time, AI does not observe how APIs execute, which objects they access, or what data they expose. Treating AI driven controls as comprehensive API security solutions obscures this limitation and increases risk.
Effective API security requires a clear separation between pattern detection and impact assessment. AI can support the former, but the latter depends on runtime visibility into application behavior. Grounding AI driven decisions in the execution context allows enterprises to benefit from machine learning without surrendering control or accountability.
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