DAST vs SAST: When to use which for API Security

Application security programs are under pressure as APIs become the primary means by which software delivers business value. APIs now dominate modern systems, driving more than 83% of internet traffic as internal and external integrations proliferate across mobile, SaaS, microservices, and automation use cases. This growth has been accompanied by rising security risk: 57% of enterprises report API-related data breaches in the past two years, while nearly all enterprises (99%) experience API-related security issues annually.

Traditional approaches to application security testing were designed for a different era. Static tools like SAST (Static Application Security Testing) focus on identifying flaws in source code before deployment, while dynamic tools such as DAST (Dynamic Application Security Testing) simulate attacks against running applications. Both remain valuable but were not built to address the velocity and complexity of API-first, microservices, and cloud-native architectures.

From a business perspective, this gap shows up as risk and inefficiency. Security teams invest heavily in tooling but still miss vulnerabilities that surface only in production APIs. Developers receive findings that are difficult to validate or prioritize. Executives see rising incident response costs, delayed releases, and recurring audit findings despite increased security spending. In many cases, vulnerabilities persist not because teams lack tools, but because tools are applied at the wrong stage of development or without context.

Understanding when to use SAST, when to use DAST, and where each approach falls short for API security has become essential for modern enterprises.

What is Dynamic Application Security Testing (DAST)?

Dynamic Application Security Testing, commonly referred to as DAST, is a security testing approach that analyzes an application while it is running. Instead of reviewing source code, DAST interacts with the application from the outside, simulating how an attacker would probe it in real conditions. Tests are performed against a live application or deployed environment by sending requests and observing responses to identify exploitable behavior.

DAST evaluates how applications handle inputs, authentication, authorization, and data exposure during execution. Because it operates at runtime, it can identify issues that only appear once an application is deployed, such as misconfigurations, broken access controls, injection vulnerabilities, and unintended data leakage. This makes DAST particularly useful for validating the real security posture of applications and APIs as they behave in practice.

From a security leadership perspective, DAST provides visibility into what is actually exploitable rather than what might be vulnerable in theory. It reflects the attacker’s point of view and helps organizations understand how applications respond under real usage conditions. However, because DAST depends on deployed environments and observable behavior, its effectiveness is closely tied to test coverage, authentication handling, and the realism of the payloads used.

How does DAST Security work?

Dynamic Application Security Testing evaluates an application by interacting with it while it is running. Rather than analyzing source code, DAST observes how the application responds to real requests in a deployed environment. This allows it to identify security weaknesses that only surface during execution, including configuration issues, access control failures, and data exposure risks.

At a high level, DAST operates as an external actor. It sends requests to the application in the same way a legitimate user or attacker would, then analyzes responses to understand whether security controls are enforced correctly. This makes DAST particularly relevant for API security, where behavior often depends on runtime state, identity, and workflow context.

Step one: Target identification

DAST begins by identifying exposed application surfaces such as web interfaces or API endpoints. For APIs, this may involve enumerating endpoints through specifications, traffic observation, or authentication based access.

Step two: Authentication and session handling

To test protected functionality, DAST must authenticate as a valid user or service. This includes managing credentials, tokens, sessions, and API keys so that testing reflects real access paths rather than only unauthenticated edge cases.

Step three: Request and payload execution

The DAST engine sends crafted requests to the application. These requests include both valid inputs and malicious variations designed to test how the application handles input validation, authorization decisions, and error conditions.

Step four: Response analysis

Responses are analyzed for indicators of vulnerability. This includes unexpected data exposure, improper authorization, error messages that reveal internal details, or behavior that deviates from expected access controls.

Step five: Vulnerability identification

Based on observed behavior, DAST identifies issues that are exploitable in practice. Findings are tied to specific requests and responses, allowing teams to understand what failed and under what conditions.

Architecture

A typical DAST architecture comprises several foundational components that work together during testing.

DAST engine

The core engine orchestrates testing. It manages request execution, authentication state, and test sequencing against the running application.

Endpoint and surface discovery

This component identifies reachable application or API surfaces. For APIs, this may rely on specifications, runtime traffic, or manual configuration, depending on the tool.

Payload and test logic engine

The payload engine generates test inputs based on parameters, data types, and observed behavior. Its effectiveness determines whether DAST can move beyond simple scanning and uncover logic and authorization flaws.

Response analysis and correlation

This component evaluates application responses and correlates them with requests to determine whether a security issue exists. It distinguishes between expected failures and true vulnerabilities.

Reporting and triage layer

Finally, findings are documented with supporting request and response context so teams can validate and remediate issues efficiently.

What is Static Application Security Testing (SAST)?

Static Application Security Testing, commonly known as SAST, is a security testing approach that analyzes application source code, bytecode, or binaries to identify potential vulnerabilities without executing the application. SAST is performed early in the development lifecycle, typically during coding or build stages, before the application is deployed.

By examining code paths and logic, SAST aims to detect classes of vulnerabilities such as insecure data handling, improper input validation, hard coded secrets, and unsafe function usage. Because it does not require a running application, SAST can be integrated directly into development workflows and continuous integration pipelines.

From a leadership perspective, SAST provides early visibility into security issues and helps teams address problems before they become expensive to fix. However, its findings are based on static analysis and inferred execution paths, which means results often require validation to determine real world exploitability.

How does SAST Security work?

Static Application Security Testing works by analyzing application code to identify patterns that may indicate security weaknesses. Rather than observing runtime behavior, SAST evaluates how code is written, how data flows through the application, and whether secure coding practices are followed.

SAST tools treat the application as a collection of instructions and logic paths. They attempt to reason about how the application could behave at runtime by analyzing control flow and data flow. This makes SAST well suited for identifying structural issues in code, but less effective at understanding how the application behaves under real usage conditions.

Step one: Code ingestion

SAST begins by ingesting source code, bytecode, or compiled binaries from the application. This may occur within an integrated development environment, during a build process, or as part of a continuous integration pipeline.

Step two: Code parsing and modeling

The tool parses the code to understand its structure, including functions, variables, libraries, and frameworks. It builds an internal model of the application that captures control and data flow across components.

Step three: Rule and pattern analysis

SAST applies a set of predefined rules and heuristics to the code model. These rules are designed to identify insecure coding patterns, unsafe functions, improper error handling, and potential injection points.

Step four: Data flow and taint analysis

To identify more complex issues, SAST tracks how data moves through the application. This includes analyzing whether untrusted input can reach sensitive operations without proper validation or sanitization.

Step five: Finding generation

Based on its analysis, SAST produces findings that indicate where vulnerabilities may exist in the code. These findings often include file names, line numbers, and descriptions of the potential issue.

Architecture

A typical SAST architecture consists of several foundational components that enable static analysis at scale.

Code ingestion and integration layer

This component connects SAST tools to source code repositories, build systems, or developer environments. It ensures that the correct code version is analyzed consistently.

Parser and language analysis engine

The parser understands programming languages, frameworks, and syntax. Its accuracy directly affects how well the tool can model application behavior and detect issues.

Rule and policy engine

This engine applies security rules and coding standards to the parsed code. Rules may be based on common vulnerability classes, compliance requirements, or internal secure coding guidelines.

Data flow and control flow analyzer

This component attempts to trace how data moves through the application and how execution paths are constructed. It enables detection of issues that span multiple functions or files.

Reporting and developer feedback layer

Findings are reported back to developers with contextual information such as file location and remediation guidance. This layer determines how actionable results are for engineering teams.

DAST vs SAST

When security leaders evaluate DAST vs SAST, the discussion is often framed as an either-or choice. In reality, understanding the difference between SAST and DAST helps organizations apply each technique where it delivers the most value. The table below compares static and dynamic security testing across practical dimensions, with examples grounded in executive level decision making.

Types of vulnerabilities detected by DAST vs SAST

One of the most common sources of confusion in DAST vs SAST discussions is the assumption that both tools detect the same classes of vulnerabilities. In practice, static and dynamic security testing surface very different risk signals because they observe applications at various stages and from different perspectives.

Understanding the difference between SAST and DAST in terms of vulnerability coverage helps security leaders decide where each approach fits in an API security program.

Vulnerabilities Commonly Detected by DAST

Dynamic Application Security Testing identifies vulnerabilities that are observable only when an application is running. Because DAST interacts with live systems, it is well suited for detecting issues tied to configuration, access control, and real data exposure.

DAST commonly detects:

• Broken authentication and authorization issues, including missing or improperly enforced access controls
• Insecure direct object references and object level authorization flaws
• Injection vulnerabilities that manifest through runtime input handling
• Excessive data exposure through APIs
• Security misconfigurations in deployed environments
• Workflow based business logic abuse that spans multiple API calls

For API security, this is critical. Many of the highest impact risks emerge only when identity, role, and runtime state are involved. This is why the difference between SAST and DAST scans becomes a practical risk question rather than a theoretical one. DAST confirms which vulnerabilities are actually exploitable.

Vulnerabilities Commonly Detected by SAST

Static Application Security Testing focuses on weaknesses that can be identified by analyzing code structure and logic. Because it does not require a running application, SAST excels at identifying coding issues early in the development lifecycle.

SAST commonly detects:

• Insecure coding patterns and unsafe function usage
• Missing input validation or output encoding
• Hard coded secrets or credentials
• Use of vulnerable libraries or deprecated APIs
• Improper error handling logic
• Potential injection points inferred from data flow analysis

SAST is effective for improving code quality and preventing known classes of vulnerabilities from entering production. However, it cannot determine whether a flagged issue is reachable or exploitable in a real deployment. This limitation is central to SAST and DAST testing tradeoffs.

Why Neither Is Sufficient Alone for APIs

For APIs, especially those supporting modern distributed systems, neither approach provides complete coverage on its own. SAST identifies potential risk in code but lacks runtime context. DAST validates real world exploitability but depends on exercised paths and authenticated access.

This is why security teams often deploy multiple SAST vs DAST tools across the lifecycle. Used together, they provide broader visibility into both insecure code and exploitable behavior. Used in isolation, each leaves blind spots that attackers can exploit.

Why DAST and SAST are not enough for API Security. 

Static and Dynamic Application Security Testing were both designed for an earlier generation of applications. Their models assume monolithic architectures, user driven interfaces, and relatively stable execution paths. Modern API first environments operate very differently, and those differences expose fundamental limitations in how SAST and DAST function.

DAST Was Designed for Web Interfaces, Not APIs

DAST approaches application security from the outside, interacting with live systems to identify exploitable behavior. This works well when applications expose visible interfaces that can be navigated and exercised predictably. APIs do not behave this way. They are machine to machine interfaces that require precise request structures, strict authentication, and contextual sequencing across multiple calls.

Traditional DAST techniques rely heavily on crawling and enumeration to discover attack surfaces. APIs cannot be reliably crawled. Endpoints remain hidden unless invoked with the correct methods, headers, payload formats, and credentials. As a result, large portions of the API surface are never tested, even though they may be exposed and reachable in production.

Even when endpoints are discovered, DAST lacks sufficient context to test APIs deeply. It struggles to handle complex authentication flows, role based access control, and multi step workflows that define how APIs are actually used. This makes it ineffective at detecting some of the most common and damaging API vulnerabilities, including broken object level authorization and business logic abuse.

SAST Was Designed for Code Analysis, Not Runtime API Risk

SAST focuses on identifying insecure patterns in source code before applications are deployed. This is valuable for improving code quality and preventing known classes of vulnerabilities early. However, API security risk is rarely confined to a single line of code.

APIs enforce security through distributed logic that spans services, identity systems, configuration layers, and runtime state. Authorization decisions often depend on tokens, roles, and data relationships that only exist during execution. Static analysis cannot observe these conditions, so it cannot reliably detect most API specific vulnerabilities.

In practice, SAST produces large volumes of findings that require manual interpretation. Many flagged issues never translate into real world risk, while vulnerabilities tied to API workflows, data exposure, and access control remain invisible. This imbalance creates noise, slows remediation, and reduces confidence in results.

API Security Requires Context That Neither Tool Provides

APIs are systems of interaction rather than isolated components. Security failures emerge across sequences of calls, identity transitions, and data flows that evolve continuously. Neither static and dynamic security testing, as traditionally implemented, was built to model this reality.

DAST lacks the depth of discovery and contextual awareness required to fully exercise modern APIs without additional inventory, identity, and runtime context. SAST lacks visibility into runtime behavior and real access paths. When applied independently, both approaches produce false positives, false negatives, and dangerous blind spots in API security testing.

This is why relying on DAST vs SAST alone is insufficient for modern API security. These tools still play essential roles, but they must be complemented by API native approaches that understand runtime behavior, identity, and data movement. Without that context, organizations are left testing fragments of their attack surface while real risk continues to grow.

Benefits of DAST and SAST Protection 

While neither approach is sufficient on its own for modern API security, both Static and Dynamic Application Security Testing provide meaningful benefits when used as intended. Understanding the strengths of static and dynamic security testing helps organizations apply the proper control at the right stage of the software lifecycle.

Benefits of SAST Protection

SAST delivers value by identifying security issues early, before applications are deployed and exposed to real users. By analyzing source code and application logic, SAST helps organizations reduce risk at its point of origin.

Key benefits of SAST include:

1. Early detection of security weaknesses: Identifying issues during development allows teams to fix problems when changes are least disruptive and remediation costs are lowest.
2. Improved code quality and security hygiene: SAST reinforces secure coding practices by consistently flagging unsafe patterns, helping development teams learn and improve over time.
3. Comprehensive coverage of code paths: Because SAST analyzes the entire codebase, it can highlight risky logic in paths that may be difficult to reach during runtime testing but could still be exposed under certain conditions.
4. Strong alignment with CI workflows: SAST integrates naturally into build pipelines, enabling continuous feedback without requiring deployed environments or live traffic.

From a leadership perspective, SAST acts as a preventive control. It reduces the likelihood that known weaknesses enter production, even though it cannot confirm whether a finding is exploitable in practice. This distinction is central to understanding the difference between SAST and DAST.

Benefits of DAST Testing

DAST complements static analysis by validating how applications behave in production. By interacting with live systems, DAST provides insight into real world exposure rather than theoretical risk.

Key benefits of DAST include:

• Visibility into runtime security behavior: DAST evaluates how authentication, authorization, and data handling work in deployed environments, where configuration and context matter most.
• Confirmation of exploitability: Because findings are based on observed responses, DAST helps teams prioritize issues that represent actual risk rather than potential weaknesses.
• Lower noise for operational teams: Compared to static analysis, DAST findings tend to be easier to validate because they demonstrate how an application can be abused.
• Independence from source code access: DAST can be applied to third party, partner, or acquired APIs where code is unavailable, making it valuable in complex ecosystems.

For security leaders, this practical validation is often what clarifies what is the difference between SAST and DAST scan when deciding how to allocate remediation effort.

Why These Benefits Are Complementary

The DAST vs SAST discussion is not just about choosing one over the other. Each approach answers a different question. SAST identifies potential code vulnerabilities. DAST shows whether those weaknesses can be exploited in a live environment.

Used together, SAST and DAST provides broader coverage than either technique alone. However, as modern architectures become increasingly API driven, both approaches must be supplemented with API native visibility and context to avoid blind spots.

When to use which: SAST vs DAST Testing

Deciding between SAST and DAST is not about picking a better tool. It is about understanding what question you are trying to answer at a given point in the software lifecycle. Both approaches provide value, but only when applied in the right context and with realistic expectations.

Deciding between SAST and DAST is not about choosing a better tool. It is about understanding what security question needs to be answered at a given point in the software lifecycle. Both approaches provide value, but only when applied in the right context and with realistic expectations.

When SAST Is the Right Choice

SAST is most effective when the goal is to reduce risk early and improve code quality before applications are deployed. It works best during development and build stages where source code is readily available and changes are frequent.

For example, a product organization building new APIs backed by microservices may want to ensure developers are not introducing common weaknesses such as hard coded secrets, unsafe libraries, or missing input validation. Running SAST in CI pipelines helps catch these issues early, before they propagate across services and environments.

From a leadership perspective, SAST functions as a preventive control. It helps enforce secure coding standards and reduces the likelihood that known vulnerability classes enter production. However, this approach provides no visibility into how APIs behave at runtime, how authentication tokens are enforced, or how authorization decisions play out across multiple API calls. SAST answers whether code may be risky, not whether an API is actually exploitable.

When DAST Is the Right Choice

DAST is best suited for validating real world exposure in deployed environments. It becomes useful once applications or APIs are running and handling actual requests.

Consider an organization that has already deployed APIs supporting customer transactions. Leadership may want assurance that authentication, authorization, and data handling controls work as intended under real operating conditions. DAST provides this perspective by interacting with live systems and observing how they respond to crafted requests.

For CISOs, DAST often confirms whether vulnerabilities represent true business risk. However, traditional DAST tools struggle to discover APIs comprehensively, manage complex authentication flows, and test role based or multi step workflows effectively. As a result, DAST may miss critical API vulnerabilities simply because it cannot see or exercise the full attack surface.

When Each Approach Breaks Down for APIs

Problems arise when SAST or DAST are used outside their strengths. Organizations that rely solely on SAST may believe APIs are secure because code scans pass, even though authorization flaws and data exposure issues exist in production. Conversely, teams that rely only on DAST may believe they are thoroughly testing live APIs, while large portions of the API surface remain undiscovered or untested due to lack of context.

This mismatch often creates a false sense of security. Executives see tools running and reports generated, yet real risk persists because neither approach was designed to model API behavior holistically.

What This Means for Security Leaders

In modern API driven environments, the right question is not whether to use SAST or DAST, but when and how to use each. SAST is most effective for early prevention and developer guidance. DAST is most effective for validating runtime exposure. Neither approach alone can provide complete API security coverage.

This is why many organizations are rethinking how static and dynamic security testing fit into broader API security strategies. The objective is not to replace these tools, but to recognize their limits and avoid treating them as comprehensive solutions for API risk.

Challenges of using SAST and DAST and their solutions 

While SAST and DAST remain foundational techniques in application security, applying them to API first environments introduces real operational challenges. These challenges stem from how static and dynamic security testing were originally designed and how modern APIs behave in practice.

Challenge: Limited Visibility Into the API Attack Surface

One of the core challenges with both SAST and DAST is incomplete visibility into APIs. Static analysis evaluates code but can miss APIs that are dynamically generated, indirectly invoked, or poorly documented. Dynamic testing relies on discovering reachable interfaces, but APIs cannot be reliably crawled or inferred without precise context.

As a result, large portions of the API surface may remain untested, even when security teams believe coverage is comprehensive.

Solution

Organizations need reliable API discovery and inventory to complement static and dynamic security testing. Without a complete view of what APIs exist, even the most mature SAST vs DAST tools will only assess a subset of actual risk.

Challenge: High Noise and Inaccurate Signal

SAST often generates large volumes of findings based on inferred execution paths. Many of these issues never materialize into real world vulnerabilities. At the same time, DAST can miss vulnerabilities simply because certain API paths, roles, or workflows are not exercised during testing.

This imbalance leads to both false positives and false negatives, making it difficult for teams to prioritize remediation effectively.

Solution

Findings generated by static and dynamic security testing should be contextualized and prioritized based on runtime relevance, data sensitivity, and exposure. Treating scan output as directional rather than definitive helps security teams focus on issues that meaningfully reduce risk.

Challenge: Weak Coverage of Authentication and Authorization

Modern APIs depend on complex identity mechanisms such as OAuth, JWTs, and short lived tokens. SAST cannot model how these controls behave at runtime. Traditional DAST tools often struggle to manage tokens, roles, and session state automatically.

As a result, some of the most exploited API vulnerabilities, including broken object level authorization, frequently go undetected.

Solution

Effective API security testing requires automated handling of authentication and identity context. Without this capability, the difference between SAST and DAST becomes less relevant than the shared blind spots both approaches leave behind.

Challenge: Inability to Model Business Logic

Many API attacks exploit sequences of valid requests rather than single endpoints. SAST evaluates code in isolation. DAST often tests endpoints independently. Neither approach understands how APIs are intended to be used together.

Solution

Security teams should recognize that the difference between SAST and DAST lies in what each can observe, not in their ability to understand business intent. Workflow awareness and runtime context are required to address this class of risk.

Best Practices to follow for SAST and DAST Testing

Although SAST and DAST are not sufficient on their own for API security, they remain valuable when applied correctly. Following best practices helps organizations maximize the value of static and dynamic security testing while avoiding common pitfalls.

Use SAST as an Early Preventive Control

SAST is most effective when used early in the development lifecycle. Integrating SAST into CI pipelines helps identify insecure coding patterns before deployment and reinforces secure coding standards across teams.

Best practice is to treat SAST findings as indicators of potential risk rather than confirmed vulnerabilities. This framing reduces friction with developers and keeps remediation focused.

Use DAST to Validate Runtime Exposure

DAST is best applied once applications or APIs are deployed. It helps teams understand how systems behave under real conditions, including how authentication, authorization, and data handling controls are enforced.

For APIs, DAST must be configured with proper authentication and realistic payloads. Without this, asking what is the difference between SAST and DAST scan becomes theoretical rather than actionable.

Avoid Relying on Either Approach in Isolation

One of the most common mistakes in DAST vs SAST programs is treating either technique as comprehensive. SAST without runtime validation can create false confidence. DAST without visibility and context leaves significant portions of the API surface untested.

Best practice is to align SAST and DAST with distinct objectives. SAST reduces the introduction of insecure code. DAST validates exposure after deployment.

Prioritize Findings Based on Business Impact

Not all vulnerabilities carry the same level of risk. Findings should be prioritized based on sensitive data exposure, customer impact, and regulatory relevance rather than raw severity scores.

This approach helps security leaders move beyond scan results and toward meaningful risk reduction.

Why API Native Security Testing Is Required Beyond SAST and DAST

SAST and DAST were created to secure traditional web applications built around user interfaces, predictable request flows, and relatively static execution paths. In those environments, analyzing source code or probing visible endpoints was often sufficient to uncover meaningful risk. Modern applications no longer operate this way.

Today’s systems are API first by design. Business logic is distributed across services, identity is enforced dynamically, and critical decisions happen at runtime rather than in code alone. APIs expose functionality through machine to machine interactions that are invisible to crawlers, tightly bound to authentication context, and highly sensitive to request structure and sequencing. This shift fundamentally changes what security testing must account for.

Static analysis struggles because APIs rarely enforce security through isolated code blocks. Authorization decisions depend on runtime identity, token scopes, object relationships, and data ownership that static inspection cannot observe. As a result, SAST produces findings that may never be reachable in practice, while missing vulnerabilities that only appear when APIs are exercised with real identities and data. This leads to excessive noise and a growing gap between reported risk and actual exposure.

Dynamic testing faces a different but equally limiting challenge. DAST interacts with live systems, but it was built around discovery models that assume visible entry points and navigable flows. APIs do not expose themselves that way. Endpoints remain hidden without precise methods, payloads, and authentication. Even when endpoints are discovered, traditional DAST tools lack the contextual awareness required to test role based access control, multi call workflows, and object level authorization consistently. The result is incomplete coverage and false confidence.

In practice, this means organizations experience both false positives and false negatives at scale. Security teams spend time chasing issues that are not exploitable, while real API vulnerabilities tied to access paths, workflows, and sensitive data flows remain undetected. This is not a tooling failure so much as a design mismatch. Neither static nor dynamic security testing was built to understand APIs as living systems of interaction.

API native security testing addresses this gap by starting from how APIs actually behave rather than how they are written or documented. It requires continuous discovery, runtime informed testing, identity awareness, and workflow context. Without these capabilities, even well executed SAST and DAST programs leave material risk unaddressed.

This is why modern API security strategies must extend beyond traditional approaches. The next section examines how Levo applies an API native security testing model designed to address the structural limitations of SAST and DAST in API first environments while supporting continuous delivery.

Achieve Complete API Security beyond DAST and SAST with Levo

SAST and DAST were created to secure legacy, interface driven web applications. Their assumptions about visibility, execution paths, and user interaction no longer hold in API first environments where business logic is distributed, authentication is dynamic, and risk emerges through machine to machine interaction. As APIs become the backbone of modern systems, relying on static and dynamic testing alone results in fragmented coverage and growing security blind spots.

In practice, this limitation shows up as noise and uncertainty. Static analysis flags issues that never manifest in production while missing vulnerabilities tied to runtime identity and data flow. Dynamic testing validates live behavior but struggles to discover APIs, handle authentication automatically, or exercise workflows that require role changes and parameter mutation. The result is a testing program that generates false positives, misses critical API vulnerabilities, and fails to scale with deployment velocity.

Levo was built specifically to address these structural gaps through an API native security testing model. Instead of adapting legacy scanners to APIs, Levo starts with how APIs actually behave in production. It continuously discovers APIs from live traffic, builds accurate documentation automatically, and maps sensitive data flows and access paths across services. This ensures security testing is grounded in reality rather than assumptions or outdated specifications.

Levo’s API Security Testing module uses this runtime context to generate precise, schema aware test payloads for every endpoint. Authentication is handled automatically across OAuth, JWT, API keys, and mutual TLS, removing manual setup and eliminating coverage gaps. Tests are executed continuously across environments and focus on vulnerabilities that matter most for APIs, including broken object level authorization, privilege escalation, data exposure, and business logic abuse.

Unlike traditional approaches, Levo validates only what is actually exploitable. Findings are tied to real requests, real identities, and real data paths, dramatically reducing noise and accelerating remediation. Security teams gain confidence that what is flagged represents genuine risk, while development teams receive actionable insights that fit naturally into CI and CD workflows.

By moving beyond the constraints of SAST and DAST, Levo enables organizations to secure APIs with the same speed and flexibility used to build them. API security becomes continuous, contextual, and scalable, allowing teams to ship faster without trading velocity for risk.

Conclusion

Outcomes, not activity, ultimately determine the effectiveness of API security testing. The goals are clear: continuously improve security posture, prevent breaches before they occur, and protect sensitive data as APIs evolve. Levo achieves these goals by focusing on signal over noise. Its API Security Testing capability surfaces only low volume, high confidence vulnerabilities that are tied to real requests, real identities, and real data paths, enabling teams to remediate issues that materially reduce risk rather than chasing theoretical findings.

What differentiates Levo is that security testing does not operate in isolation. It is supported by continuous API Inventory and discovery so teams always know what APIs exist, paired with living API Documentation that reflects actual runtime behavior rather than outdated specifications. Sensitive Data Discovery ensures testing and prioritization are anchored to where regulated and high impact data flows, while Vulnerabilities Reporting provides actionable context that accelerates remediation and accountability.

Beyond testing, Levo delivers value across the entire software development lifecycle. Continuous API Monitoring ensures security posture does not degrade as APIs change, while API Detection identifies emerging threats and abnormal behavior in live traffic. When exploitation attempts occur, API Protection enforces precise, inline controls to block malicious activity without disrupting legitimate usage.

For organizations seeking deeper automation and operational efficiency, Levo’s MCP Server enables programmable access to security context, allowing teams and AI agents to query insights, trigger remediation workflows, and validate fixes directly within engineering and security processes.

In practice, Levo moves API security beyond point in time testing in development or staging. It supports visibility, governance, testing, detection, and protection throughout the entire API lifecycle. This allows security teams to focus on reducing real breach risk and protecting sensitive data, while engineering teams continue to scale API driven systems without slowing delivery.