Over the last decade, enterprises modernized far faster than their security stacks. Architectures shifted from monolithic web apps to distributed, API first systems. Release cycles accelerated from quarterly deployments to daily pushes. Customer experiences grew interconnected across mobile apps, partner ecosystems, and third party services.
Yet despite this modernization, most organizations are still relying on legacy AppSec tools such as SAST, SCA, WAFs, and traditional DAST. These were built for a very different era of software.
The results are showing.
Industry research now confirms that:
- API attacks have risen more than 400% year over year.
- Over 90% of organizations using APIs experienced at least one API security incident in a single year.
- And analysts consistently warn that a significant portion of enterprise API traffic, especially machine to machine and internal east–west communication, is invisible to legacy, crawler based security tools.
This is why CEOs and CISOs consistently tell me the same thing: “We invested heavily in AppSec, yet every serious incident still seems to come from an API.”
The reason is structural. DAST was created for the world of user driven, browser rendered applications such as forms, pages, and visible endpoints that a scanner could crawl.
Modern APIs are none of those things. They are ephemeral, event driven, authenticated through complex token flows, deeply interconnected, and often undocumented. They carry the organization’s most sensitive data and change more frequently than any human process can track.
Legacy security tools, including traditional DAST, simply were not designed for this scale or complexity.
If you are responsible for revenue protection, digital trust, or regulatory exposure, the goal is simple. You should have a realistic understanding of what DAST can and cannot solve in an API first enterprise.
What Is DAST Testing?
Dynamic Application Security Testing (DAST) is a method of testing a running application from the outside by sending real requests and analyzing responses for signs of vulnerabilities. Think of it as a friendly attacker in your pipeline. It does not read your source code. Instead, it behaves the way a user or attacker would, hitting exposed interfaces and looking for unexpected behavior.
How DAST works
DAST works by testing the application from the outside, the same way a real user or attacker would interact with it. It does not look at the internal code. Instead, it sends different types of requests to the running application and watches how it responds.
In practice, a DAST scanner:
- Points at a live running target. This is usually a staging or pre production environment, but many teams also run DAST in production because it needs an active endpoint with real traffic behaviour.
- Crawls or spiders the surface it can access.
- Generates test payloads and sends them to forms, parameters, headers, and paths.
- Observes responses for patterns that indicate security issues such as error messages, injections that succeed, broken authentication flows, or misconfigurations.
Because it tests the app in a live runtime, it is very good at catching issues that only appear when all components are wired together.
Why DAST testing is important
DAST matters because it validates how your application behaves in the real world, not how it behaves in theory. Even when code reviews, SAST scans, and unit tests all pass cleanly, the running application can still expose vulnerabilities created by configuration drift, integration issues, environment mismatches, or unexpected runtime behaviour.
Here is why this matters from a business standpoint:
1. Runtime issues are now a major source of exposures
Static scans cannot see misconfigurations, expired secrets, broken auth flows, or data exposure issues that emerge only when an app is running. Recent analysis of more than 40 million security exposures found that 80% were caused by misconfigurations, not flawed code.
2. Attackers increasingly target APIs and runtime surfaces
APIs have become the dominant interface for modern applications. According to Imperva’s State of API Security report, API calls now make up nearly 71% of all web traffic. When most customer interactions flow through APIs, runtime testing becomes essential.
3. A huge portion of internet traffic is automated
Today, 49.6% of all internet traffic is generated by bots, including malicious automation, scrapers, credential stuffing tools, and misuse frameworks. DAST is one of the only testing approaches that simulates this attacker like behaviour in a safe way before real adversaries do.
4. DAST validates that your deployed build is safe
Even if your pipelines are clean, runtime failures can still slip through:
- a misconfigured gateway
- an incorrect IAM role
- a stale token validator
- a feature flag that exposes an internal route
DAST ensures these issues surface before customers or attackers encounter them.
5. DAST produces audit ready, regulator ready evidence
Compliance frameworks such as PCI DSS, SOC 2, ISO 27001, and GDPR increasingly expect proof of dynamic testing on externally accessible apps and APIs. DAST provides repeatable, time stamped, verifiable results that auditors trust.
6. DAST prevents bad releases without slowing development
Because DAST interacts with the application externally, it works across monolithic, microservice, serverless, and hybrid environments without requiring code changes. Teams can run automated scans across dev, staging, and prerelease environments with minimal friction.
DAST vs SAST
Executives often ask, “We already have SAST. Why do we need DAST?”
The short answer is that they look at different realities.
- SAST inspects source code or binaries. It is “inside looking out,” great for early in the SDLC, and strong at identifying insecure patterns before code runs.
- DAST inspects behavior of the running app. It is “outside looking in,” and strong at catching misconfigurations, broken authentication, exposed debug routes, and other runtime behaviors that SAST will never see.
How DAST and SAST work together
SAST examines your source code to catch insecure patterns early, while DAST validates how the application behaves when everything is wired together. Used together, they cover both sides of the equation, design time flaws and runtime failures.
They complement each other well, but neither was built to address the full spectrum of API specific risks, which require context, identity awareness, and an understanding of how APIs behave across real workflows. SAST cannot see authentication flows, multi step logic, or real payloads. DAST cannot discover most internal or low traffic APIs, and it often struggles with modern identity and authorization.
Types of vulnerabilities DAST typically detects
DAST excels at identifying issues that surface only when an application is running, the same issues attackers probe for through real traffic and interactions. The categories below represent where DAST historically performs well:
- Injection vulnerabilities: Issues like SQL injection, command injection, and some forms of XML External Entity (XXE) attacks, where untrusted inputs influence backend execution.
- Cross Site Scripting (XSS): User supplied input that results in malicious scripts running in the browser.
- Broken or weak authentication flows: Incorrect session handling, missing validation steps, or login mechanisms that attackers can bypass.
- Security misconfigurations: Exposed admin panels, verbose error messages, unsafe headers, weak TLS configurations, and other server side mistakes visible at the edge.
- Sensitive data exposure: Instances where confidential information is returned in responses due to weak filtering, insecure defaults, or misconfigured endpoints.
- Unvalidated redirects and forwards: Redirects that can be manipulated to send users to malicious destinations.
Because these weaknesses manifest during runtime, DAST remains effective at catching them. However, most modern API risks like broken object level authorization (BOLA), multi step workflow abuse, internal or low traffic endpoints, and business logic attacks do not appear through standard web interactions. This is why DAST is necessary but no longer sufficient for securing API first architectures.
Top DAST Tools
There are many ways to categorize DAST tools. For an executive, it is more useful to think in terms of what problem they are designed to solve. Below is a high level view of well known platforms that extends dynamic testing to modern APIs, not just web pages.
1. Levo.ai
Levo.ai is a full lifecycle API security platform designed for modern, API first architectures. It provides continuous API discovery, automated documentation, runtime threat detection and protection along with API native security testing grounded in real production behavior. Unlike tools focused solely on edge defenses or runtime signals, Levo covers the entire SDLC, from development and staging to production, enabling teams to build, test, and ship secure APIs with higher confidence and significantly lower operational overhead.
Key Features
- Continuous API discovery across internal, external, shadow, zombie, and third party APIs
- Automatic, traffic aligned API documentation with detailed specifications
- API security testing with payloads generated from real runtime behavior
- Runtime monitoring for access control risks, sensitive data exposure, and configuration drift
- API governance, sensitive data detection, and posture enforcement
- YAML and Python based custom policy creation
- On prem, hybrid, and SaaS deployment options
Pros
- Comprehensive API coverage with surface level, internal, east west, and low traffic services automatically detected
- Automated documentation ensures specifications are always current and aligned with production behavior
- API native testing identifies complex authorization, authentication, and business logic issues
- Low false positives due to payloads crafted from real user and system behavior
- Flexible deployment options, including full on prem for regulated industries
- Reduces operational burden by combining discovery, documentation, testing, and monitoring into a unified workflow
Cons
- Best suited for organizations with meaningful API footprints; smaller or web only environments may not require the full breadth of capabilities
- Requires collaboration between development, platform, and security teams to operationalise findings effectively
- Adoption may require a shift from periodic scanning to continuous, API centric security practices
Pricing
Footprint based pricing. Customers typically realize a lower total cost of ownership due to reduced egress costs and the elimination of multiple tools for discovery, documentation, testing, and monitoring.
G2 Rating: 4.9 / 5
It has a strong early feedback for coverage depth, ease of deployment, and accuracy of findings.
2. Invicti
Invicti (formerly Netsparker) is a long standing enterprise DAST platform designed primarily for traditional web applications. It offers strong vulnerability scanning for websites and some API surfaces, with capabilities like proof based verification and hybrid DAST + IAST support.
Key Features
- Traditional DAST engine with proven crawling and vulnerability detection
- IAST style instrumentation to improve accuracy for web application scanning
- Support for importing OpenAPI specifications and API definitions for targeted scans
- CI/CD integrations for automated test execution
Pros
- Mature and widely adopted across enterprises with large web application portfolios
- Strong detection for common injection, misconfiguration, and surface level issues
- Proof based verification helps reduce false positives in classic web scenarios
- Reliable option for organizations still operating monolithic or web heavy stacks
Cons
- API coverage gaps: discovery depends on manual OpenAPI imports, API Hub uploads, or threshold based NTA logs; low traffic, partner, internal, or feature flagged routes often never enter inventory
- High operational overhead: maintaining imports, running multiple scan modules, operating on prem agents, and coordinating upgrades adds high ongoing cost and DevSecOps effort
- Potential privacy exposure: NTA driven traffic capture and full spec ingestion can expose sensitive payloads or proprietary API details if internal logs aren’t locked down
- Restricted depth of attack simulation: cannot emulate chained or stateful API flows required to detect many OWASP API Top 10 issues
Read More: Top 10 Inviciti Alternatives
Pricing
Enterprise pricing based on application volume, on prem components, and scan modules. Maintenance and infrastructure overhead can add to the overall TCO for larger deployments.
G2 Rating: 4.6 / 5
It reflects strong web application scanning capabilities but with consistent customer feedback on configuration effort and operational complexity.
3. Cequence
Cequence is positioned as an API protection platform with a strong focus on detecting automated threats, bot activity, and fraud attempts at the edge. Its architecture is centered around in line inspection of production traffic, making it effective for real time blocking scenarios.
Key Features
- Real time bot and fraud detection based on traffic pattern analysis
- Edge based enforcement for REST and GraphQL APIs
- In line request inspection and blocking capabilities
- Policy driven protection workflows
- Integration with API gateways and web security layers
Pros
- Strong fit for organizations prioritizing bot mitigation and fraud control at the production layer
- Real time blocking can reduce the volume of automated abuse before it reaches backend systems
- Complements existing WAF and edge security tooling for externally exposed APIs
- Useful for teams needing additional protection against automated scraping, credential stuffing, and abuse patterns
Cons
- Narrow focus on runtime issues: optimized for bot and fraud detection rather than full lifecycle API security or enabling faster, more secure software delivery
- High operational cost: in line traffic processing and mirroring increase infrastructure and egress overhead
- Privacy implications: all production traffic is processed in Cequence’s SaaS environment, including sensitive request payloads
- Limited API visibility: edge based architecture cannot observe internal, low traffic, service to service, or non REST APIs
- Restricted discovery: discovery is confined to REST/GraphQL traffic observed at the edge; gRPC, SOAP, and internal APIs remain invisible
Pricing
Traffic driven pricing, with costs increasing based on the volume of production traffic processed and mirrored for analysis.
G2 Rating: 4.6 / 5
It has favorable reviews for bot mitigation and operational protection, and mixed feedback regarding API discovery depth and deployment complexity.
4. Orca Security
Orca Security is a cloud security posture and workload visibility platform designed to provide runtime insights into misconfigurations, vulnerabilities, and risks across cloud environments. While it offers surface level API discovery and basic sensitive data mapping, its capabilities remain focused on production visibility rather than API security across the SDLC.
Key Features
- Agentless cloud posture visibility
- External API endpoint identification
- Sensitive data mapping at the endpoint level
- Misconfiguration monitoring across cloud environments
Pros
- Appropriate for organizations prioritizing general cloud visibility and misconfiguration management
- Simple onboarding for cloud only environments due to agentless architecture
- Helps consolidate cloud posture insights into a central dashboard
- Can complement existing CSPM or runtime protection tools
Cons
- No impact on application delivery: does not improve development velocity, software delivery timelines, or revenue driven API initiatives
- SaaS processing of full data: sensitive information is likely processed in their environment, raising privacy considerations
- Deployment complexity: SaaS based data ingestion often requires additional approvals and governance checks
- Production only focus: visibility is constrained to runtime monitoring without pre production or shift left capabilities
- No on prem deployment: unavailable for regulated or highly sensitive environments requiring full data control
Pricing
Pricing scales with cloud footprint and the volume of data processed. Costs may rise due to full data ingestion and SaaS based analysis.
G2 Rating: 4.6 / 5
It’s known for cloud posture and runtime visibility, with limited relevance for organizations seeking full lifecycle API security.
Read more: Top 10 Orca Security Alternatives
5. Cloudflare
Cloudflare is primarily a WAF/WAAP and edge security platform focused on protecting applications and APIs at the production layer. It provides strong capabilities for traffic filtering, rate limiting, and bot mitigation, but its value remains centered around runtime defenses rather than improving API security across the SDLC.
Key Features
- WAAP and WAF functionality for production traffic
- Rate limiting, bot mitigation, and edge enforcement
- Basic API endpoint discovery based on observed traffic
- Performance and availability monitoring
Pros
- Well suited for organizations needing edge layer protection for external facing APIs and applications
- Strong global network helps deliver low latency filtering and runtime enforcement
- Useful for mitigating volumetric attacks, abusive traffic patterns, and common web exploits
- Integrates naturally into existing Cloudflare managed application delivery stacks
Cons
- No impact on development velocity or delivery timelines: focuses only on production protection rather than supporting earlier stages of the SDLC
- Privacy and operational risk: in line placement means Cloudflare can block real traffic, which may affect customer experience and revenue
- Narrow primary use case: functions as a WAF/WAAP rather than a full lifecycle API security platform
- No remediation workflows: lacks reproducible payloads, developer mapping, or automated fix guidance
- Production only scope: unable to shift left into development, staging, or pre production workflows
Pricing
Pricing scales with traffic volume, enforcement features, and WAAP policies. Costs rise due to full traffic inspection and in line processing requirements.
G2 Rating: 4.5 / 5
It is known for WAAP and global edge delivery with limited applicability for teams seeking deep API security coverage across the SDLC.
Why DAST Is Not Enough for API Security
If DAST is so useful, why are API related breaches still climbing?
The answer is structural. DAST was designed for a world of user driven web applications with visible forms and pages, not for dense networks of internal, partner and machine to machine APIs.
Based on experience and the evidence, there are several recurring gaps.
1. DAST cannot reliably see your full API surface
DAST is a black box approach. It looks at what it can see from the outside. That works when you have pages, links and forms that hint at behavior. It fails when most of your critical actions live behind JavaScript, mobile clients, internal services and partner gateways.
Modern APIs:
- Are often triggered by specific application states or events
- May only be reachable with exact combinations of headers and tokens
- Rarely expose a crawlable interface that a DAST tool can traverse
Without an accurate API inventory, a DAST scanner is guessing. Levo’s own research and broader surveys show that more than three quarters of enterprises do not maintain a complete inventory of internal, external and partner APIs, even though frameworks like OWASP API Security Top 10 and PCI DSS now expect it.
2. DAST depends on documentation that is usually incomplete
For API testing, many teams give their DAST tool a Swagger or OpenAPI file and hope for the best.
In reality:
- Parameters are missing
- Accepted values are not documented
- Real world workflows are not captured
That means the scanner is effectively fuzzing blind, without understanding how the API actually behaves, which payloads are realistic, or which scenarios reflect real risk. Surveys indicate that well over half of enterprises admit their API documentation is incomplete. One missing parameter in a spec can be the difference between detecting a data exposure and leaving a profitable attack path untouched.
3. DAST struggles with modern authentication and real user context
APIs today rely on layered identity:
- OAuth flows and short lived access tokens
- JWTs with claims and scopes
- mTLS between services
- API keys with per consumer rate limits
Managing this manually for a DAST tool is brittle. Tokens expire, flows change, and multi tenant scenarios are hard to encode. As Levo’s own API testing research highlights, traditional scanners often fail or time out at this stage, leading security teams to narrow their test scope just to “get the scan to run”.
Even when authentication works, most DAST tools do not vary user identities or object identifiers in a way that exposes authorization flaws such as BOLA (Broken Object Level authorization). They test valid calls with valid identities and declare success.
Attackers do not.
A simple example:
If an API “get account balance” only ever gets called with the correct account holder’s token and ID, a DAST scanner will never discover that swapping the ID for another customer’s ID also returns valid data. Levo’s blog walks through this exact case and shows how this workflow level vulnerability slips past traditional scanners.
4. DAST is not built for business logic testing at API scale
Some business logic flaws simply cannot be uncovered through generic payloads:
- Abuse of multi step workflows (fund transfers, approvals, policy changes)
- Rate limit bypasses combined with data enrichment
- Chained calls across internal and external APIs
These require understanding how your APIs are actually used, which sequences matter, and how identities and data move across them. Traditional DAST is not instrumented to observe that behavior at runtime.
5. DAST does not solve API sprawl and drift
Finally, even if a DAST tool performs a reasonably good scan today, it does not:
- Continuously detect new APIs created by teams
- Alert you when an API starts handling new sensitive data
- Show you when a “safe” internal API becomes internet facing through configuration drift
Those are posture and observability problems, not just testing problems. As Gartner and others note, the majority of enterprise APIs will soon not even be actively managed, which means many of them will never touch a DAST scanner at all.
The Benefits of Using DAST
Even with its blind spots for modern APIs, DAST remains a foundational part of any mature application security program. It plays a critical role in validating how your systems behave in the real world, something static analysis alone cannot provide.
You still want DAST to:
- Validate web applications and basic APIs for OWASP Top 10 vulnerabilities
It reliably catches the classic issues that continue to drive a large percentage of web layer breaches. - Identify misconfigurations and regressions before a release
Runtime validation ensures that last mile changes, infrastructure drift, or integration issues do not slip unnoticed into production. - Provide audit ready evidence for compliance programs
Frameworks such as PCI DSS, SOC 2, ISO 27001, and GDPR increasingly expect proof of dynamic testing, DAST provides verifiable reports that satisfy auditors and internal risk teams. - Support penetration testers by mapping exposed surfaces
It gives security teams and red teams a quick, automated snapshot of low hanging issues and externally reachable endpoints. - Augment SAST and SCA with runtime behavior checks
Where SAST and SCA look inward at code and dependencies, DAST confirms that the running application actually behaves the way your architecture and policies intended.
Challenges With Using DAST Testing
DAST continues to play an important role in identifying surface level issues, but its traditional model creates structural blind spots when applied to modern, API first architectures.
Below are the most common challenges organizations face and the practical ways to mitigate them without slowing delivery or overwhelming security teams.
Challenge 1: Incomplete API Coverage
DAST tools rely on what they can crawl or what developers explicitly document. In API driven systems, large portions of the estate such as internal services, low traffic routes, partner integrations, ephemeral endpoints, and shadow APIs, remain invisible. Leadership assumes “everything is being scanned” when, in reality, coverage is partial at best.
Solutions:
- Establish a runtime driven API inventory as a prerequisite for testing
- Discover APIs from traffic, infrastructure, and code, not just from OpenAPI files
- Classify unmanaged or unknown APIs as a core risk category in governance reports
Challenge 2: Dependence on Outdated or Incomplete Documentation
DAST effectiveness depends heavily on documentation accuracy, yet API specs frequently drift. Developers rarely have the time to maintain them, and even well managed specs fail to capture real production behavior, actual payloads, or multi step flows.
Solutions:
- Generate API specifications automatically from runtime behavior
- Continuously reconcile documentation with real request and response patterns
- Feed this live view into testing workflows rather than relying on static files
Challenge 3: Limited Authentication and User Context Awareness
Many DAST tools authenticate once, test a few endpoints, and mark the environment as secure. They lack an understanding of how identities behave across tenants, roles, or business functions. As a result, authorization flaws, broken object level controls (BOLA), and privilege escalation issues remain undetected.
Solutions:
- Use testing approaches that automatically learn authentication flows and refresh tokens
- Derive user context directly from runtime observability
- Test for role switching, tenant boundary violations, over privileged keys, and other OWASP API Top 10 patterns
Challenge 4: Testing Happens in Snapshots, Not Continuously
Traditional scanning occurs on a schedule, monthly, quarterly, or before major releases. Modern APIs evolve weekly, sometimes daily. By the time a report is generated, the underlying architecture has changed, and new risks have already entered production.
Solutions:
- Shift from campaign based scanning to continuous testing in CI and runtime
- Prioritize testing based on API sensitivity, data exposure, and business impact
- Integrate findings directly into developer workflows to reduce remediation cycles
Challenge 5: Testing Results Are Disconnected From Runtime Reality
Executives often ask:
- “Which APIs are actually being used?”
- “Which vulnerable endpoints are customer facing?”
- “Which issues matter most right now?”
DAST cannot answer these because it operates without a runtime context. Everything appears high risk or low risk in isolation, with no tie back to real usage.
Solutions:
- Combine testing data with runtime insights so every finding is linked to actual traffic
- Prioritize vulnerabilities based on exposure, sensitivity, and user impact
- Use this combined view to produce meaningful, business level reporting for leadership
Implement Complete API Security Beyond DAST With Levo
Levo moves API security beyond traditional DAST limitations by identifying only real, exploitable vulnerabilities backed by live runtime context. Instead of generating thousands of generic payloads and speculative alerts, Levo builds its tests directly from actual authentication flows, real data paths, and observed user behavior. This ensures every finding is meaningful, reproducible, and grounded in the way your APIs truly operate in production.
Because the platform avoids noise and false positives, engineering and security teams spend their time fixing what matters rather than triaging irrelevant alerts. The result is a measurable improvement in security posture, fewer incidents escaping into production, and a dramatically lower amount of effort required for pre production security testing. Faster remediation cycles, less manual overhead, and clearer prioritization become the default operating rhythm.
Beyond testing, Levo provides end to end API security coverage across the entire SDLC, from early development through production. The platform continuously discovers APIs, keeps documentation up to date, identifies sensitive data flows, tests APIs with context aware payloads, monitors runtime behavior, detects anomalies, and enforces protection policies. This unified approach replaces fragmented point tools and gives teams a consistent, lifecycle wide control plane for securing every API.
Levo’s full suite of capabilities includes:
- API Inventory for complete visibility across internal, external, shadow, zombie, and third party APIs
- API Documentation automatically generated and continuously updated from real runtime traffic
- Sensitive Data Discovery to identify and trace PII, PHI, financial data, and secrets across all APIs
- API Security Testing & Vulnerability Management with context aware, low noise findings tied to real behavior
- API Monitoring for continuous oversight of changes, anomalies, and drift in production
- API Threat Detection to surface misuse, suspicious activity, and access irregularities
- API Threat Protection to enforce guardrails and block malicious traffic without disrupting legitimate usage
Together, these capabilities allow organizations to move beyond point in time DAST checks and adopt a continuous, reliable, and scalable API security program that supports growth, accelerates delivery, and reduces operational burden.
Conclusion
DAST is still a critical piece of modern security programs. It gives you outside in validation that your web applications are not leaking obvious vulnerabilities into production. It complements SAST and other shift left practices and remains valuable for auditor conversations.
But APIs have changed the game. They now handle the majority of your traffic, carry the most sensitive data and represent the most attractive attack surface for adversaries. DAST was never designed to understand that world. It cannot maintain an accurate API inventory, cannot reliably manage API authentication at scale, and struggles to test the business logic and authorization paths that actually cause most modern breaches.
So the right move is not to walk away from DAST, but to stop asking it to do a job it cannot do.
For most enterprises, the path forward looks like this:
- Keep DAST for what it is good at: web app edge validation and regression checks
- Layer a runtime driven, API aware platform like Levo to cover the API estate end to end
- Treat API visibility, continuous testing and runtime posture as core engineering concerns, not side projects
If you get those fundamentals right, you will not just “add another tool.” You will give your organization a realistic, defensible way to support aggressive digital growth without letting APIs become your biggest blind spot.
DAST will always matter, but it cannot secure the APIs driving your business. Book your demo to see how Levo gives you full lifecycle API security that closes every blind spot.
