Organizations deploying AI applications face a purchase decision: extend existing AppSec tooling (SAST, DAST, WAF) with AI-specific capabilities or deploy purpose-built AI application security platforms. The choice determines whether AI security testing integrates with current workflows or requires separate processes, separate findings management, and separate compliance reporting.
AI applications create attack surfaces that traditional AppSec tools miss. Prompt injection attacks manipulate model behavior through crafted inputs. Model extraction attacks steal training data or model parameters through systematic queries. Supply chain attacks compromise base models, fine-tuning datasets, or retrieval-augmented generation (RAG) data sources. Each attack type requires specialized testing approaches that general-purpose security scanners cannot provide.
Three Vendor Categories
Traditional AppSec vendors add AI security modules to existing SAST, DAST, and runtime protection platforms. These solutions integrate with current security workflows but can miss AI-specific attack patterns that require model-aware testing approaches. The integration advantage means findings appear in existing security dashboards and compliance reports. The coverage limitation means novel AI attacks might go undetected if the vendor prioritizes backward compatibility over comprehensive AI threat modeling.
Purpose-built AI security platforms focus exclusively on AI application testing and runtime protection. These platforms typically provide deeper coverage of AI-specific attack patterns but require separate deployment, separate findings management, and separate integration with CI/CD pipelines. The depth advantage means more sophisticated testing of model extraction, indirect prompt injection, and supply chain attacks. The integration cost means additional overhead for security teams already managing multiple toolsets.
AI governance platforms with security modules combine AI risk management with security testing capabilities. These platforms address both policy compliance and technical security testing but can dilute focus on either capability. Organizations need security testing may find governance features unnecessary overhead. Organizations that need both governance and security testing may prefer integrated platforms that provide unified risk reporting across policy and technical controls.
Evaluation Criteria
Platform evaluation requires testing actual AI security capabilities, not just feature lists or demo scenarios. The criteria below separate platforms that provide comprehensive AI threat coverage from those that address only prompt injection or other limited attack categories.
AI Threat Coverage — OWASP Top 10 for Large Language Model Applications defines the vulnerability taxonomy platforms must demonstrate coverage against — including prompt injection (LLM01), insecure output handling (LLM02), training data poisoning (LLM03), supply chain vulnerabilities (LLM05), excessive agency (LLM06), and insecure plugin design (LLM07). Platforms covering only a subset leave documented vulnerability classes uninstrumented (Source: owasp.org). Evaluate whether platforms instrument all four attack surface clusters: direct model interaction, indirect attacks through retrieved content, model and data extraction, and supply chain integrity.
Prompt Injection Testing Depth — indirect injection testing validates whether the application distinguishes user instructions from content originating in external data sources. Require platforms to demonstrate test cases that manipulate model behavior through poisoned retrieval results, not just malicious user prompts.
Model Extraction and Information Disclosure Testing — multi-turn extraction campaigns reconstruct training data or model architecture through conversation chains that appear benign individually. Test whether platforms detect extraction attempts that span multiple sessions or use conversation context to reconstruct protected information.
Supply Chain Assessment — runtime testing alone misses compromised models or poisoned training data that affect all application behavior. Require documentation of what supply chain artifacts the platform needs to assess model provenance and data integrity before deployment.
RAG and Retrieval Context Testing — RAG applications face unique risks when external content sources contain malicious instructions that manipulate model behavior during retrieval and generation. Platforms that test only direct user input miss indirect attacks that compromise RAG data sources. Evaluate whether platforms provide dedicated test suites for retrieval context manipulation separate from direct prompt injection testing.
Continuous Testing and CI/CD Integration — point-in-time testing cannot prevent regression when models or applications change. Test whether platforms provide configurable pass/fail criteria that block deployment when security thresholds are exceeded across model updates and fine-tuning iterations.
Findings Reporting Quality — MITRE ATLAS provides the AI-specific TTP library that platforms should map findings to — including AML.T0051 (LLM Prompt Injection), AML.T0040 (model inversion attack), and AML.T0020 (training data poisoning) (Source: atlas.mitre.org). Require structured findings export that supports compliance evidence generation and integrates with existing vulnerability management workflows.
Integration With Existing AppSec Stack — complementary integration means AI security findings appear alongside traditional AppSec findings in unified dashboards and compliance reports. Test whether platforms export findings to existing SIEM, vulnerability management, and compliance reporting systems without requiring separate workflows.
Evaluation Table
| Criterion |
What Good Looks Like |
Red Flag |
How to Test in PoC |
| AI Threat Coverage |
All four attack surfaces covered: direct, RAG, extraction, supply chain |
Prompt injection only; no supply chain or extraction coverage |
Submit tests across all OWASP LLM Top 10 vulnerability categories |
| Prompt Injection Testing Depth |
Indirect injection through retrieved documents tested separately from direct injection |
Only direct user-input injection tested; no retrieval context manipulation |
Poison retrieval content; verify detection separate from direct injection |
| Model Extraction and Information Disclosure Testing |
Multi-turn extraction campaigns and system prompt recovery tested |
Single-turn queries only; no sustained extraction simulation |
Run multi-session extraction; verify cross-session correlation and detection |
| Supply Chain Assessment |
Pre-deployment supply chain validation with model provenance documentation |
Runtime-only testing; no supply chain assessment capability |
Request provenance report for test model before runtime testing begins |
| RAG and Retrieval Context Testing |
Dedicated retrieval context manipulation test suite with vector store poisoning |
No distinction between direct and indirect injection coverage |
Compromise test vector store; verify retrieval-specific detection |
| Continuous Testing and CI/CD Integration |
Native CI/CD integration with configurable pass/fail deployment gates |
Point-in-time testing only; no pipeline integration |
Gate a deployment on test failure; verify pipeline blocking triggers |
| Findings Reporting Quality |
Findings mapped to OWASP LLM Top 10 and MITRE ATLAS with structured export |
Narrative report only; no framework mapping or structured export |
Export findings; verify ATLAS technique and OWASP category mapping |
| Integration With Existing AppSec Stack |
Documented SAST/DAST integration and SIEM event export |
Standalone deployment; no integration with existing security tooling |
Configure SIEM export; verify findings appear in existing dashboards |
Vendor Questions
These questions expose capability gaps that demos and marketing materials typically do not address:
"How does your platform test for indirect prompt injection through retrieved documents, and how do you distinguish between direct user input injection and retrieval context manipulation in your findings reports?" This question identifies whether the platform distinguishes RAG-specific attack vectors from direct prompt injection. Platforms that conflate both categories miss indirect attacks through external content sources.
"What specific techniques does your platform use to detect multi-turn model extraction attempts, and how do you measure the completeness of extracted information across conversation sessions?" This question reveals whether the platform detects sustained extraction campaigns, not just single-session probing attempts.
"What supply chain artifacts does your platform require to assess base model provenance and training data integrity, and what specific supply chain attacks can you detect before runtime testing begins?" This question determines whether the platform addresses supply chain risks before deployment begins. Platforms limited to runtime access miss model integrity issues that originate in the training pipeline.
"How does your CI/CD integration work mechanically — what specific API endpoints or webhook events does it use, and how do test failures translate into deployment gates?" This question identifies whether CI/CD integration is a supported capability or requires custom scripting. Native integration provides documented endpoints and blocking mechanisms without configuration overhead.
"How do your findings map to OWASP LLM Top 10 categories and MITRE ATLAS techniques, and what structured data formats do you support for compliance evidence generation?" This question reveals whether the platform produces compliance-ready findings or requires manual framework mapping. Platforms that export structured findings against NIST AI RMF Map 5.2 and Measure 2.5 categories satisfy evidence requirements without additional analysis (Source: airc.nist.gov).
PoC Test Cases
Proof-of-concept testing must validate actual platform capabilities against realistic attack scenarios, not vendor-provided demo environments:
RAG Poisoning Scenario: Deploy a test RAG application with a compromised document in the vector store containing malicious instructions. Query the application with legitimate questions that trigger retrieval of the poisoned content. Verify whether the platform detects the indirect injection attempt and distinguishes it from direct prompt injection in findings reports. This test reveals whether the platform understands retrieval-based attack vectors.
Multi-Session Extraction Campaign: Configure a test model with sensitive information in its training data or system prompt. Execute a series of queries across multiple sessions that individually appear benign but collectively extract protected information. Test whether the platform correlates extraction attempts across sessions and detects coordinated information gathering that single-query testing would miss.
Supply Chain Integrity Assessment: Provide the platform with a test model that has known provenance issues — unclear base model source, suspicious fine-tuning data, or compromised dependency libraries. Verify whether the platform identifies supply chain risks before runtime testing and what specific artifacts it requires for comprehensive assessment.
CI/CD Pipeline Integration: Configure the platform to block deployment when security test thresholds are exceeded. Introduce a test AI application with known vulnerabilities and verify that the deployment pipeline fails appropriately. Test whether the integration provides clear failure modes and actionable remediation guidance for development teams.
Cross-Framework Compliance Mapping: Export findings from the platform and verify mapping to OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF requirements. Test whether the exported data supports compliance reporting without requiring manual categorization or additional analysis tools.
Integration with Existing Security Stack
AI application security platforms must integrate with existing identity and access management, vulnerability management, and incident response workflows to provide unified security operations:
Identity Integration Requirements: Verify that the platform can authenticate against existing identity providers (Azure AD, Okta, AWS SSO) and respect role-based access controls for security findings and administrative functions. AI security testing often requires elevated permissions to access model artifacts and training data. The platform must integrate with existing identity governance to enforce least-privilege access to sensitive AI components.
SIEM and SOAR Integration: Test whether the platform exports security events in standard formats (CEF, STIX, JSON) that existing SIEM platforms can ingest without custom parsing rules. AI security events must correlate with traditional application security events to provide unified threat detection. Platforms that require separate event processing create blind spots in security monitoring.
Vulnerability Management Workflow: Evaluate whether AI security findings integrate with existing vulnerability management platforms (ServiceNow, Jira, Remedy) using standard ticketing APIs and vulnerability scoring frameworks. AI vulnerabilities must follow established remediation workflows to ensure consistent response times and accountability.
Compliance Reporting Integration: Test whether findings export supports existing GRC platforms and audit requirements without requiring separate compliance processes. Organizations subject to SOC 2, ISO 27001, or industry-specific regulations need AI security evidence integrated with broader compliance programs.
The integration decision determines operational overhead and long-term maintainability. Platforms that require separate security processes increase the likelihood that AI security testing becomes an afterthought rather than integrated security practice. Choose platforms that extend existing workflows rather than creating parallel security operations that compete for attention and resources.