Best AI Security Testing Platforms: Top 10 in 2026

“AI security testing platforms unify static, dynamic, and AI-specific security testing into a single system that operates across the software development lifecycle. They use AI to reduce false positives, prioritize exploitable risks, and generate actionable remediation guidance.”

What Is an AI Security Testing Platform? 

An AI security testing platform is a unified solution that evaluates the security of modern applications using artificial intelligence to improve accuracy, coverage, and prioritization. It combines multiple testing approaches, such as static application security testing (SAST), software composition analysis (SCA), infrastructure as code (IaC) scanning, and container security – into a single system that operates across the software development lifecycle.

These platforms do more than run isolated scans. They correlate findings across code, open-source dependencies, configurations, and container images to identify issues that are truly exploitable. AI models help reduce false positives, adapt detection logic to specific codebases, and generate practical remediation guidance that developers can apply quickly.

Many platforms also embed agentic AI assistants directly into the IDE. These assistants provide real-time feedback as developers write code, enforce organizational security policies, and suggest secure patterns. This shifts security earlier in the lifecycle and reduces the cost of fixing defects.

Integration with CI/CD pipelines ensures that testing runs automatically on commits, pull requests, and builds. AI helps prioritize high-risk findings and optimize scan performance to avoid slowing delivery. At the portfolio level, centralized dashboards and AI-driven risk analytics give security leaders visibility into trends, exploitability, and remediation progress across all applications.

This is part of a series of articles about AI cybersecurity.

Methodology: How we Shortlisted the Platforms

We selected the ai appsec platforms below based on practical enterprise application security testing criteria: breadth of coverage across the SDLC (code, dependencies, IaC, containers, APIs, and runtime validation where relevant), quality of prioritization (context, exploitability, deduplication), strength of developer workflow integration (IDE and CI/CD), governance and auditability, and maturity of AI-era capabilities for securing AI-generated code and AI integrations.

AI Security Testing Platforms at a Glance

Here is a quick look at the leading options for AI security testing platforms. Click the name of each product or scroll down for a full review of these tools, their strengths, and key considerations.

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Who Needs AI Security Testing Platforms? 

AI security testing platforms are essential for any organization developing or deploying AI-powered applications, especially when those applications interact with sensitive data, users, or external systems. They are particularly valuable to teams operating at high velocity with AI-generated code, where traditional security approaches fall short of identifying runtime risks and vulnerabilities specific to modern architectures.

• AppSec teams benefit from the ability to validate static analysis findings and identify runtime-only issues, such as misconfigurations or business logic flaws. By integrating security testing into the CI/CD pipeline, they can continuously evaluate the exploitability of AI-generated or AI-assisted code and prioritize issues that pose the greatest risk.
• Developers and development leaders need visibility into how AI-generated code behaves under real conditions. With AI accelerating software creation, dynamic validation ensures that logic flaws, injection vulnerabilities, or access control issues are caught and fixed early. By mapping findings directly to code and providing contextual guidance, these platforms make it easier for teams to act on issues without disrupting velocity.
• DevOps and platform engineering teams use AI security testing to automate checks in release workflows without slowing delivery. These platforms support modern API-first and microservices-based architectures, making it easier to scan apps and services as they evolve. Correlated findings help reduce alert fatigue and enable targeted remediation before code reaches production.
• CISOs and security leaders rely on these platforms to ensure that AI-infused applications are resilient against real-world threats. They use the results to assess risk exposure, meet dynamic testing requirements for compliance, and demonstrate due diligence in securing critical systems—especially as AI accelerates delivery cycles and increases potential attack surfaces.

As AI development becomes mainstream, AI security testing platforms help all stakeholders keep pace with innovation while managing the unique risks introduced by automated code and learning systems.

Key Capabilities of AI Security Testing Platforms 

AI-Driven Code Scanning and Remediation (SAST, SCA, IaC, Containers)

Modern AI-driven security testing platforms now deliver comprehensive scanning across multiple layers of application technology, going beyond traditional tools. These platforms unify static application security testing (SAST), software composition analysis (SCA), infrastructure as code (IaC) scanning, and container security into a single, cloud-native solution that continuously evaluates code, dependencies, configurations, and container images across the software development lifecycle. By consolidating these capabilities into one workflow, teams can eliminate tool sprawl and gain centralized visibility into vulnerabilities from the first line of code through deployment.

In addition to broad scanning coverage, AI enhancements help reduce false positives and generate more actionable results. Intelligent analysis correlates findings across engines to highlight truly exploitable issues rather than low-impact noise, enabling security and development teams to focus efforts where they matter most. Furthermore, AI-assisted query building and tailored scanning logic help adapt vulnerability detection to specific codebases, while remediation suggestions can include secure code patterns or fixes that accelerate resolution and improve overall application security posture.

LLM application security and AI model security testing

AI security testing platforms increasingly need to test more than traditional code flaws. For LLM-powered apps and agentic workflows, platforms should help teams identify and validate AI-specific risks such as prompt injection, sensitive data leakage, insecure model integrations, and overly-permissive tool or plugin access.

At the model layer, AI model security testing focuses on adversarial manipulation and supply-chain style risks (e.g., untrusted model components, poisoned data or weights, unsafe autonomy). The most effective platforms combine code-based discovery of AI integrations with policy guardrails and testing workflows that keep AI-enabled features safe to deploy.

Agentic AI Secure Coding Assistants in the IDE

Agentic AI assistants embedded directly into developers’ integrated development environments (IDEs) bring real-time security guidance into everyday coding workflows. Instead of waiting for security reviews or pipeline scans, developers receive immediate feedback on potential vulnerabilities, configuration issues, and open-source risks as they write code. These assistants can identify security weaknesses, explain root causes, and propose actionable fixes, helping developers build more secure software without leaving their preferred tools or interrupting productivity.

Unlike traditional AI code helpers that simply suggest code completions, agentic assistants are designed to act on structured policy context and organizational standards. They enforce security requirements, embed best practices, and can automatically remediate issues where appropriate, helping bridge the gap between security and development. This integration reduces remediation turn-around time, minimizes back-and-forth between teams, and ensures that secure coding practices are adopted early in the development cycle rather than retrofitted later. 

AI-Powered DevSecOps and CI/CD Integration

Integrating security testing into DevSecOps and CI/CD pipelines is critical for shifting left and ensuring that vulnerabilities are caught as early as possible in the development lifecycle. AI-enhanced security platforms provide seamless integrations with common version control systems, build tools, and pipeline orchestrators, enabling automated scanning at each stage of development and deployment. These integrations bring security into the fabric of the SDLC, triggering tests on code commits, pull requests, or build events without manual intervention.

By embedding security into continuous delivery workflows, teams can enforce policies, block risky code from progressing, and automate compliance checks without slowing velocity. AI contributes by optimizing test execution, prioritizing high-impact findings, and reducing alert fatigue through intelligent filtering and context-aware analysis. This close alignment of development, security, and operations accelerates delivery while maintaining robust security posture and ensuring that risks are identified and addressed before they reach production.

Portfolio-Level Risk Analytics and Prioritization

At the enterprise level, visibility into security posture across multiple applications and repositories is essential for managing risk effectively. Advanced security platforms offer unified dashboards that aggregate findings from all scanning engines, providing security leaders with a comprehensive view of vulnerability trends, remediation progress, and risk exposure across the entire portfolio. By consolidating data into a central control plane, teams can enforce consistent policies and track security metrics at scale.

AI-driven analytics help prioritize vulnerabilities based on exploitability, impact, and contextual risk, enabling teams to focus limited resources on the most critical threats. Correlation of findings across static scans, open-source analysis, and configuration checks reduces noise and highlights issues that pose real danger to applications and infrastructure. With clear risk scoring and trend insights, decision makers can allocate effort strategically, demonstrate security improvements to stakeholders, and continuously optimize the organization’s overall security posture. 

How AI Security Testing Platforms Support DevSecOps and Secure AI-Generated Code 

AI security testing platforms support DevSecOps by embedding security throughout the AI development lifecycle – from code generation to deployment governance.

Inner Loop: Developer Feedback in the IDE

In the inner loop, AI security platforms integrate directly into developer environments like IDEs. Tools provide real-time feedback on AI-generated or AI-integrated code, surfacing vulnerabilities such as insecure API calls, prompt injection risks, or data leakage patterns as code is written. Some platforms offer in-line suggestions to remediate issues before the code ever leaves the developer’s machine. This accelerates secure coding without interrupting the development workflow.

Middle Loop: CI/CD Pipeline Enforcement

In the middle loop, these platforms act as security policy agents within CI/CD pipelines. They automatically scan code, models, and configurations for known and AI-specific vulnerabilities as part of build and test stages. This includes detecting hardcoded secrets, misconfigured AI model endpoints, and unsafe data handling in preprocessing scripts. By enforcing security policies during automated testing and deployment, they prevent risky artifacts from reaching production.

Execution layer (post-commit): In mature programs, AI security testing doesn’t stop at detection. Platforms increasingly push triage decisions and reviewable remediation into pull requests – so developers can fix issues where code is reviewed and merged, with clear policy controls and auditable approvals.

Outer Loop: Governance and Risk Insights Across Portfolios

In the outer loop, AI security testing platforms provide centralized visibility and risk management across multiple teams, projects, and models. Dashboards consolidate insights from IDE plugins, pipeline scans, and runtime testing to track trends in vulnerabilities, model exposure, and compliance status. These portfolio-level analytics help security teams prioritize efforts, enforce governance policies at scale, and ensure alignment with evolving regulatory requirements.

Notable AI Security Testing Platforms

Holistic AI Security Testing Platforms

1. Checkmarx One Platform

Best for: Enterprises that want a unified, AI-ready AppSec platform to secure large portfolios (multiple teams/repos) and keep up with AI-assisted development without tool sprawl.

Key strengths: End-to-end, cloud-native coverage (SAST/SCA/IaC/API/DAST/container) correlated through ASPM-style risk analytics, with agentic AI assistance in IDE/CI/CD—plus PR-native triage decisions and reviewable remediation options.

Things to consider: You’ll get the most value after connecting your major repos/pipelines and defining policy/approval guardrails, so plan a phased rollout and integration coverage.

Checkmarx One is the unified, cloud-native application security platform for enterprises that need to secure code, applications, and AI-driven development at scale. It brings SAST, SCA, IaC, API, DAST, container, and supply chain security together with ASPM and the Checkmarx One Assist family of agentic AI agents, delivering correlated risk insights and developer-centric remediation from the IDE to production. With a single platform and data model, customers reduce tool sprawl, improve risk visibility, and help developers ship secure software faster.

Key features include:

• Unify fragmented AppSec tools: Consolidate multiple scanning and point solutions into one platform with a shared data model. 
• Gain a single view of application risk: Correlate findings across code, open source, infrastructure, APIs, containers, and supply chain. 
• Accelerate remediation: Use agentic AI assistants to provide contextual fixes, prioritization, and guidance where teams work. 
• Support enterprise governance and reporting: Align AppSec metrics and posture with business-critical applications and regulatory requirements. 
• Enable shift everywhere: Embed security across IDEs, CI/CD pipelines, cloud, and runtime, aligned to modern DevSecOps practices.

Key differentiators include:

• Truly unified platform: Checkmarx One was designed as a single platform, not cobbled together, with consistent UX, policy, and analytics. 
• Agentic AI built in: Developer, Policy, and Insights Assist agents provide AI-driven support from inner to outer loops. 
• Enterprise-proven at scale: Selected by leading global brands and recognized in analyst evaluations across SAST, ASPM, and software supply chain security. 
• Developer-first, security-strong: Combines deep security capabilities with workflows and integrations that keep developers productive. 
• Code-to-cloud coverage: Connects source to runtime perspectives, ensuring risk decisions are based on full context.

2. Snyk Code

Best for: Developer-centric teams that want fast SAST feedback directly in IDEs and pull requests.

Key strengths: Strong IDE integration and AI-assisted remediation through DeepCode AI and Snyk Agent Fix.

Things to consider: Some users report false positives, slower CI scans, and higher pricing compared with alternatives.

Snyk Code is a static application security testing solution for developers. It provides real-time scanning in IDEs and pull requests, combined with AI-driven auto-fix capabilities. The platform uses a proprietary, self-hosted AI engine and a large security knowledge base to deliver contextual explanations and automated remediation without requiring builds.

Key features include:

• IDE and PR scanning: Delivers in-line results in IDEs and pull requests with complete automatic scans and build-free analysis.
• AI-powered auto-fixes: Uses Snyk Agent Fix and DeepCode AI to generate pre-validated fixes, enabling one-click remediation for supported issues.
• Self-hosted AI engine: Runs a custom constraint-based analysis engine designed for data privacy and fast code analysis.
• Extensive language and ecosystem coverage: Supports popular languages, IDEs, and CI/CD tools, with source coverage including widely used LLM libraries.
• Context-aware prioritization: Leverages application context and risk signals to prioritize new, deployed, or publicly exposed code issues and reduce noise.

Limitations as reported by users on G2:

• False positives: Users report recurring false positives after projects have been scanned for months, requiring extra validation and slowing triage and remediation workflows.
• Slow scans in pipelines: Reviewers note scans can take several minutes for medium repositories, which can delay CI/CD pipelines and increase feedback latency for developers.
• Complex configuration and alert management: Users describe configuration as hard to manage, with policy overrides and difficulty tracking alerts across projects, especially as organizations and repos scale.
• Interface and product fragmentation: Some users say the interface needs improvement, and note Snyk’s DAST experience runs in a separate interface rather than being integrated.
• Support and reliability issues: Reviews mention software bugs that trigger false positives and scanning problems, alongside slow or unhelpful customer support responses when issues block usage.
• Pricing concerns: Several reviewers call Snyk expensive, which can complicate adoption decisions when teams need additional features such as reachability analysis available only on paid tiers. 
  

3. Aikido Security

Best for: Small to mid-sized engineering teams looking for an all-in-one AppSec platform with automated pentesting and remediation.

Key strengths: Broad security coverage across code, cloud, containers, APIs, and runtime with automated remediation capabilities.

Things to consider: Reporting depth, enterprise analytics, and integrations may be less mature than more established platforms.

Aikido Security provides a unified platform that combines application security, cloud security, offensive testing, and runtime protection. The platform integrates SAST, SCA, IaC scanning, container security, DAST, AI pentesting, and runtime defense into a central system, with automated remediation and alert prioritization features.

Key features include:

• Unified AppSec and cloud coverage: Covers SAST, SCA, IaC, container scanning, cloud posture management, API scanning, and runtime protection within one platform.
• AI-driven pentesting: Uses AI agents to perform continuous pentests and generate audit-grade reports.
• AI AutoFix and bulk remediation: Generates reviewable pull requests to fix issues across code, dependencies, infrastructure, and containers, including bulk fixes.
• Alert prioritization and deduplication: Groups related alerts, deprioritizes non-impactful findings based on context, and supports custom rule tuning.
• Extensive integrations: Integrates with IDEs, CI/CD systems, Git platforms, task managers, compliance tools, and messaging platforms to embed security into developer workflows. 

Limitations as reported by users on G2:

• Missing security assessment depth: Users say the platform lacks essential security assessment and reporting capabilities for security engineering teams, limiting usefulness for analysts needing deeper validation workflows.
• Reporting and analytics gaps: Reviewers note advanced reporting, historical trend views, and broader analytics feel less mature, particularly for larger or regulated environments that need detailed evidence.
• Integration breadth: Some users want deeper integrations with other tools in their security stack, and broader ecosystem connectivity beyond the default coding platform connections.
• Pricing for smaller teams: Multiple reviewers describe pricing as aggressive or expensive for startups and small developers, sometimes relying on the free tier to manage costs.
• False positives and language coverage: Users mention meaningful false positives in AI-backed features and ask for more language support inside the Visual Studio Code extension. 

Notable AI-Powered SAST Solutions

4. Checkmarx SAST

Best for: Enterprise AppSec and DevSecOps teams that need fast, accurate SAST across large codebases and languages – embedded in IDE and CI/CD workflows.

Key strengths: Next-gen, speed-optimized SAST engine with AI-driven guidance, broad language coverage, and platform correlation to reduce noise and prioritize real risk.

Things to consider: Highest value comes with platform-level correlation and policy workflows; pair with DAST/API validation for runtime behavior and auth flows.

Checkmarx SAST is the next-generation static application security testing engine at the heart of the Checkmarx One platform. It combines high-precision analysis, language coverage, and a new speed-optimized engine with agentic AI that helps developers and AppSec teams find and fix vulnerabilities earlier in the SDLC. Integrated deeply into IDEs, CI/CD pipelines, and ASPM, Checkmarx SAST supports inner, middle, and outer loop Agentic AI use cases so enterprises can scale secure coding without sacrificing velocity. 

This industry-leading tool (Forrester SAST Wave 2025 leader) is a core component of  the Checkmarx One platform.

Key features include:

• Detect vulnerabilities at the code level: Identify security flaws such as injection, XSS, insecure auth, and data exposure across languages and frameworks. 
• Shift security left into developer workflows: Run fast SAST checks in the IDE and CI so issues are caught before merge or release. 
• Support compliance and secure SDLC requirements: Provide evidence of secure coding practices for regulators, auditors, and customers. 
• Accelerate remediation with AI-driven guidance: Help developers understand and fix issues quickly using Checkmarx Agentic AI assistants.

Key differentiators include:

• New SAST engine optimized for speed and accuracy: Designed to deliver rapid feedback for developers while maintaining deep analysis quality. 
• Checkmarx One Developer Assist: AI-driven remediation and coaching for developers across IDE, PR, and pipeline contexts. 
• Proven enterprise scale: Validated in independent performance and effectiveness benchmarks and used by leading global enterprises. 
• Tight integration with ASPM and other Checkmarx capabilities: SAST findings are correlated with SCA, DAST, IaC, and API results in a unified risk view. 
• Flexible deployment and integration: Supports a wide variety of languages, frameworks, and DevOps toolchains.

5. Mend SAST

Best for: Organizations that want fast static analysis with AI-assisted remediation while keeping source code local.

Key strengths: Hybrid architecture with local scanning, strong remediation automation, and noise reduction through commit-based findings.

Things to consider: SAST capabilities are still evolving compared with long-established tools, and integrations may require additional effort.

Mend SAST is a static application security testing solution to detect vulnerabilities in proprietary code with an emphasis on speed and AI-assisted remediation. It uses a hybrid architecture where code is scanned locally while prioritization, triage, and remediation workflows are handled in the cloud, keeping source code on premises.

Key features include:

• Agentic SAST support for AI code assistants: Feeds vulnerability data into AI coding assistants to automatically remediate flaws in both human- and AI-generated code before commit.
• AI-powered remediation: Provides automated code fixes with accuracy claims compared to competitors, enabling developers to remediate without manual rewrite.
• Noise reduction: Highlights only new findings from the latest commit to reduce alert fatigue and improve precision and recall.
• Fast scanning performance: Delivers results significantly faster than traditional SAST tools to support frequent commits.
• Hybrid deployment model: Scans locally while performing prioritization and reporting in the cloud to protect source code confidentiality. 

Limitations as reported by users on G2:

• Pricing and packaging: Users describe Mend.io as pricey and ask for clearer pricing separation per product, questioning value when only specific capabilities like SAST or SCA are needed.
• Integration challenges: Reviewers report integrations can be “not quite” effective, with implementation sometimes challenging and costly, especially when connecting to existing toolchains or on-prem environments.
• Limited cloud integration: Users mention cloud integration is limited, which can restrict how findings and workflows connect across cloud environments compared with expectations for a cloud-native DevSecOps setup.
• SAST maturity: Some reviews note Mend’s SAST capabilities are newer and still maturing, which can lead teams to rely on other tools for complete static analysis coverage.
• Scanner configuration and noise: Users report false positives and issues with scanner updates and configuration, creating extra work to tune rules and keep results stable over time.

6. Cycode SAST

Best for: DevSecOps teams seeking continuous code scanning integrated into cloud-native development pipelines.

Key strengths: Risk Intelligence Graph for contextual prioritization and strong integration with developer tooling and SCM systems.

Things to consider: Some users report workflow complexity and limited integrations with certain cloud services.

Cycode SAST is part of the Cycode application security platform and is designed to provide continuous, developer-friendly static code analysis. It emphasizes integration with DevOps workflows, AI-driven remediation context, and risk-based prioritization to reduce false positives and improve accuracy.

Key features include:

• Continuous SAST scanning: Scans every code change to support high-velocity DevOps environments.
• AI-driven remediation context: Uses AI-suggested fixes and contextual insights powered by the Risk Intelligence Graph to improve traceability from code to cloud.
• Customizable detection logic: Offers built-in and custom rules per language to tailor detection to organizational requirements.
• Developer workflow integration: Supports pull request scanning and integration into CI/CD pipelines and source control systems.
• Broad language and SCM support: Covers major languages and integrates with platforms such as GitHub, GitLab, Bitbucket, Azure DevOps, and others.  

Limitations as reported by users on G2:

• Cloud service integrations: A reviewer notes Cycode lacks integrations with many AWS services, making it harder to connect code findings to the specific systems hosting applications.
• Manual re-scan for violations: Users mention some violations require manual re-scans, which can add operational overhead when teams need continuous monitoring across many repositories.
• Limited feedback during graph queries: Reviews ask for clearer error or progress messages when queries against large knowledge graphs are running, to reduce confusion during analysis and navigation.
• Workflow complexity: One reviewer describes the tool as easy to understand but “a little bit complicated” to work with extensively, suggesting a learning curve for deeper use.

Source: Cycode SAST

Notable AI-Powered DAST Solutions

7. Checkmarx DAST

Best for: Teams that need runtime validation for modern web apps and APIs – ncluding authenticated areas – without slowing delivery.
Key strengths: Easy onboarding (tunneling + authentication), strong web/API coverage, correlated prioritization with static and posture signals, and remediation guidance linked back to code.
Things to consider: Requires well-defined scan scope and reliable auth configuration; deep scans can be resource intensive, so teams should tune profiles and schedules for repeatable CI/CD use.

Checkmarx DAST provides dynamic application security testing for modern web and API-based applications as part of the Checkmarx One platform. It simulates real-world attacks against running applications and services to validate vulnerabilities and misconfigurations that static analysis alone cannot see. By tightly integrating DAST results with SAST, SCA, API Security, and ASPM, Checkmarx helps security and development teams quickly understand exploitability, prioritize fixes, and foster fearless innovation without sacrificing safety. 

Checkmarx DAST meets developers where they work, making dynamic testing as agile as AI-driven development itself. When you’re building applications at AI speed, runtime validation becomes more critical than ever. eCheckmarx DAST does exactly that by testing applications for business logic flaws, authentication bypasses, API security issues, and misconfigurations.

Key features include:

• Validate vulnerabilities in running applications: Confirm which code and configuration issues are exploitable in real environments. 
• Find runtime-only issues: Detect authentication, session, input validation, and configuration flaws that appear only at runtime. 
• Support API and web app coverage: Test web front-ends and APIs in the same platform. 
• Reduce noise with correlated findings: Combine DAST and SAST results to prioritize high-confidence and high-impact issues. 
• Demonstrate security of key applications: Provide evidence to stakeholders and auditors that critical apps have been tested dynamically. 
• Secure AI-generated code: Protect against rising vulnerabilities introduced by AI-generated code, and automatically test undocumented APIs.

Key differentiators include:

• Easy onboarding: Including effortless tunnelling and authentication.

• Unified SAST and DAST: DAST is closely integrated with Checkmarx SAST and other testing engines to reduce duplication and improve prioritization. 
• Modern app and API focus: Designed for contemporary web architectures and API-centric systems. 
• Platform-native analytics and reporting: DAST findings are available in ASPM and reporting alongside other risk signals. 
• Developer-friendly remediation: DAST results are linked back to code and surfaced with guidance and AI explanations. 
• Flexible deployment: Supports a range of environments from test to pre-production, aligned with CI/CD automation.

8. Invicti DAST

Best for: Security teams that require validated vulnerability results with minimal false positives.

Key strengths: Proof-based scanning that safely exploits vulnerabilities to confirm findings and reduce manual verification.

Things to consider: Scans and configuration can be complex, and some users report slower performance and API scanning challenges.

Invicti DAST is a dynamic application security testing solution built on a proof-based scanning approach. It validates vulnerabilities by safely exploiting them and presenting evidence, with the goal of reducing false positives and increasing trust in findings. The platform integrates with CI/CD tools and supports enterprise-scale deployments.

Key features include:

• Proof-based scanning: Confirms many vulnerabilities by safely exploiting them and providing evidence to validate findings
• Predictive risk scoring: Ranks web assets before scanning to prioritize high-impact risks.
• High accuracy claims: Reports verified results with a stated 99.98% accuracy to reduce triage effort.
• CI/CD-native integrations: Integrates with tools such as Jenkins, GitHub, GitLab, and Azure DevOps to automate scanning and workflows.
• Web and API coverage: Scans dynamic applications, APIs, single-page applications, and authenticated areas, including shadow APIs.  

Limitations as reported by users on G2:

• Customer support responsiveness: Users report technical support can be slow and sometimes unable to provide workable solutions, even after initial troubleshooting calls or screen-sharing sessions.
• Slow scans and performance: Reviews mention scans can take a while, and some users experience slow performance during scanning and setup, which reduces efficiency for regular testing cycles.
• API scanning difficulties: Multiple reviewers cite issues scanning API endpoints, including cases where API scanning could not be made to work as expected, limiting coverage for API-heavy applications.
• Complex settings and navigation: Users say initial setup and configuration can be challenging, with nested settings menus that make it harder to find scan tuning and integration options.
• Limited remediation detail: Some reviews note findings lack detailed, actionable remediation guidance such as contextual fix instructions or code snippets, requiring extra effort to translate issues into patches.
• Notification and collaboration gaps: A reviewer asks for integrations with Teams or email to share results, suggesting built-in notification and collaboration options may not meet all workflows.

Source: Invicti DAST

9. Acunetix

Best for: Teams focused on automated web application and API security testing with proof-based validation.

Key strengths: Large vulnerability database, automated verification of findings, and strong integration with development tools.

Things to consider: Licensing and pricing may be restrictive for smaller teams, and deep scans can be resource intensive.

Acunetix is a DAST solution designed for automated web application and API security testing. It uses AI-based predictive risk scoring and proof-based validation to help teams prioritize and confirm vulnerabilities while integrating into development workflows.

Key features include:

• Predictive risk scoring: Uses an AI model and multiple parameters to assign risk scores to web-facing assets before scanning.
• Extensive vulnerability detection: Detects thousands of vulnerabilities, including OWASP Top 10 issues and out-of-band vulnerabilities.
• Proof-based validation: Automatically verifies vulnerabilities with high stated accuracy to reduce noise.
• Precise remediation guidance: Identifies exact code locations and provides remediation details to support developer fixes.
• CI/CD and tool integrations: Integrates with tools such as GitHub, Jira, Jenkins, GitLab, Azure Boards, and others to embed scanning into development processes. 

Limitations as reported by users on G2:

• Pricing and accessibility: Users describe Acunetix as expensive for smaller teams, with pricing and renewal increases limiting accessibility for projects that only need periodic or narrow-scope scanning.
• Complex setup and configuration: Reviewers report initial setup and scan configuration can be complex for first-time users, requiring technical know-how and patience to tune authentication, profiles, and integrations.
• Resource-intensive and slow scans: Users note deep scans can be resource-intensive and time-consuming, especially for large applications, and may slow systems or disrupt normal workflows during scanning.
• Support and reliability concerns: Reviews mention poor or unresponsive support for detailed technical issues, alongside reports of outages or accounts going offline around renewals, delaying scanning activities.
• Licensing constraints: Users criticize the “target” URL licensing model, noting that scanned URLs can become locked to a license and may be hard to replace until renewal.
• Detection quality and noise: Some reviews cite inconsistent vulnerability detection, large volumes of findings, and occasional false positives, requiring manual filtering and experience to focus remediation on real issues.

Source:Acunetix

How to Choose AI Security Testing Platforms 

Selecting the right AI security testing platform requires aligning features with development workflows, security maturity, and the nature of AI-generated code in your environment. The following considerations can guide decision-making:

• Support for secure AI-generated code: Look for platforms that can detect vulnerabilities specific to AI-generated code, such as unsafe API usage, prompt injection, or insecure model integrations. Advanced tools offer features tailored to securing AI-generated applications and protecting against risks introduced by LLM-driven development.
• IDE and CI/CD integration: Prioritize platforms that integrate into both developer environments and CI/CD pipelines. This ensures coverage across the DevSecOps lifecycle, enabling early detection in the IDE and automated enforcement during builds and deployments.
• Speed and accuracy: Evaluate scanning performance and result quality. Platforms such as Checkmarx emphasize ultra-fast scanning with reduced false positives and negatives, which supports agile development without overloading developers with irrelevant alerts.
• AI-driven remediation support: Consider whether the platform offers AI-assisted remediation. Tools with automated fix suggestions or contextual recommendations can accelerate secure coding by guiding developers directly to actionable solutions.
• Language and framework coverage: Ensure the platform supports the languages, libraries, and frameworks in your stack, including those used in AI/ML development. This is especially important when using frameworks like TensorFlow, PyTorch, or tools that interact with APIs from providers like OpenAI or Hugging Face.
• Dynamic testing capabilities: In addition to static code analysis, check for dynamic testing features (DAST) that simulate runtime behavior and test live APIs and endpoints. Platforms that offer both SAST and DAST enable broader coverage and surface different classes of vulnerabilities.
• Governance and compliance mapping: Look for platforms that provide built-in governance support. Features that map vulnerabilities to regulatory frameworks, track remediation actions, and offer audit-ready reporting can streamline compliance, especially for teams operating in regulated sectors.
• Risk-based prioritization: Tools that consolidate findings into risk scores can help teams prioritize high-impact vulnerabilities. This avoids alert fatigue and ensures that remediation efforts focus on threats most likely to affect production environments or compliance status.

Checkmarx One Assist is uniquely positioned to meet these criteria, aligning with development practices and security needs, strengthening security posture without slowing down innovation in the AI era.

Conclusion

AI-based security testing is becoming a core part of DevSecOps because software is now built faster, changed more often, and increasingly influenced by AI-generated code. Security teams need tools that can keep up with that pace without flooding developers with low-value alerts. By combining static, dynamic, and configuration testing with AI-driven prioritization and remediation, these platforms help organizations find real risk earlier, validate what matters, and fix issues before they reach production.

Checkmarx stands out as a platform that applies agentic AI across the full software delivery lifecycle, rather than in a single scanner or isolated assistant. With Checkmarx One Assist, Checkmarx SAST, and Checkmarx DAST working as part of the same platform, teams get secure coding guidance in the IDE, automated policy enforcement in CI/CD, and portfolio-level risk insight for leadership. That unified approach makes Checkmarx a strong choice for organizations that want to scale secure AI-driven development without adding friction to engineering workflows.