Zero Trust For AI Maturity Assessment
by @krishnakumarmahadevan-cmd
Evaluates Zero Trust maturity across 6 principles for both Conventional (Infrastructure) and AI Era (Data & Model) dimensions.
clawhub install toolweb-zero-trust-for-ai-maturity-assessmentπ About This Skill
name: Zero Trust for AI Maturity Assessment description: Evaluates Zero Trust maturity across 6 principles for both Conventional (Infrastructure) and AI Era (Data & Model) dimensions.
Overview
The Zero Trust for AI Maturity Assessment tool provides organizations with a comprehensive evaluation framework for implementing Zero Trust security principles in both traditional infrastructure and AI/ML environments. This tool assesses maturity across six critical Zero Trust principles: Verification, Least Privilege, Assume Breach, Microsegmentation, Continuous Monitoring, and Supply Chain Security.
The assessment is uniquely designed to evaluate two distinct dimensions: Conventional (Infrastructure) and AI Era (Data & Model). This dual-dimension approach recognizes that AI systems introduce novel security challenges requiring tailored Zero Trust strategies beyond traditional network and access controls. Organizations can benchmark their security posture, identify gaps, and prioritize remediation efforts with data-driven insights.
Ideal users include security architects, CISOs, AI/ML team leaders, and enterprise security teams seeking to implement Zero Trust frameworks that account for both legacy infrastructure and emerging AI workloads. The tool supports compliance initiatives, risk assessments, and strategic security planning.
Usage
Example Request:
{
"sessionId": "sess-abc123def456",
"userId": 1001,
"timestamp": "2024-01-15T10:30:00Z",
"assessmentData": {
"sessionId": "sess-abc123def456",
"timestamp": "2024-01-15T10:30:00Z",
"verification_conventional": {
"mfa_enabled": true,
"mfa_coverage_percent": 95,
"device_verification": "enabled"
},
"verification_ai": {
"model_provenance_tracking": true,
"data_lineage_implemented": true
},
"least_privilege_conventional": {
"rbac_implemented": true,
"principle_of_least_privilege_score": 78
},
"least_privilege_ai": {
"model_access_controls": true,
"training_data_access_restricted": true
},
"assume_breach_conventional": {
"segmentation_level": "advanced",
"incident_response_plan": "documented"
},
"assume_breach_ai": {
"model_poisoning_detection": true,
"adversarial_testing_frequency": "quarterly"
},
"microsegmentation_conventional": {
"network_segments": 12,
"segment_isolation_score": 85
},
"microsegmentation_ai": {
"model_isolation": true,
"inference_sandbox": "enabled"
},
"continuous_monitoring_conventional": {
"siem_deployed": true,
"log_retention_days": 365
},
"continuous_monitoring_ai": {
"model_drift_monitoring": true,
"inference_anomaly_detection": "enabled"
},
"supply_chain_conventional": {
"vendor_assessment_process": "defined",
"sbom_requirement": true
},
"supply_chain_ai": {
"model_source_verification": true,
"training_data_provenance_verified": true
}
}
}
Example Response:
{
"status": "success",
"assessment_id": "ztai-2024-001",
"sessionId": "sess-abc123def456",
"timestamp": "2024-01-15T10:30:45Z",
"overall_maturity_score": 82,
"conventional_maturity_score": 84,
"ai_maturity_score": 80,
"principle_scores": {
"verification": {
"conventional": 95,
"ai": 88
},
"least_privilege": {
"conventional": 78,
"ai": 82
},
"assume_breach": {
"conventional": 80,
"ai": 75
},
"microsegmentation": {
"conventional": 85,
"ai": 78
},
"continuous_monitoring": {
"conventional": 88,
"ai": 85
},
"supply_chain": {
"conventional": 72,
"ai": 68
}
},
"recommendations": [
{
"principle": "supply_chain_ai",
"priority": "high",
"action": "Implement model source verification and training data provenance tracking"
},
{
"principle": "assume_breach_ai",
"priority": "medium",
"action": "Increase adversarial testing frequency and enhance model poisoning detection"
}
]
}
Endpoints
GET /
Health Check Endpoint
Performs a basic health check to verify the API is operational.
Parameters: None
Response:
POST /api/zt-ai/assess
Assess Zero Trust for AI Maturity
Generates a comprehensive Zero Trust for AI maturity assessment based on provided assessment data across both conventional and AI dimensions.
Parameters:
| Name | Type | Required | Location | Description | |------|------|----------|----------|-------------| | assessmentData | AssessmentData object | Yes | Body | Assessment data containing maturity indicators for all 6 principles across conventional and AI dimensions | | sessionId | string | Yes | Body | Unique identifier for the assessment session | | userId | integer or null | No | Body | Optional user identifier associated with the assessment | | timestamp | string | Yes | Body | ISO 8601 formatted timestamp of assessment submission |
AssessmentData Fields:
| Field | Type | Required | Description | |-------|------|----------|-------------| | verification_conventional | object | No | Conventional infrastructure verification controls (MFA, device verification, etc.) | | verification_ai | object | No | AI-era verification controls (model provenance, data lineage, etc.) | | least_privilege_conventional | object | No | Conventional least privilege implementation (RBAC, permission scoping) | | least_privilege_ai | object | No | AI-era least privilege (model access, training data access) | | assume_breach_conventional | object | No | Conventional breach assumption controls (segmentation, incident response) | | assume_breach_ai | object | No | AI-era breach assumption (model poisoning detection, adversarial testing) | | microsegmentation_conventional | object | No | Conventional network microsegmentation details | | microsegmentation_ai | object | No | AI-era microsegmentation (model isolation, inference sandbox) | | continuous_monitoring_conventional | object | No | Conventional monitoring capabilities (SIEM, log retention) | | continuous_monitoring_ai | object | No | AI-era monitoring (model drift, inference anomaly detection) | | supply_chain_conventional | object | No | Conventional supply chain security (vendor assessment, SBOM) | | supply_chain_ai | object | No | AI-era supply chain security (model verification, data provenance) | | sessionId | string | Yes | Unique identifier for the assessment session | | timestamp | string | Yes | ISO 8601 formatted timestamp |
Response:
Error Responses:
GET /api/zt-ai/principles
Get All Zero Trust Principle Definitions
Retrieves detailed definitions and guidelines for all six Zero Trust principles as they apply to both conventional and AI dimensions.
Parameters: None
Response:
GET /api/zt-ai/domain/{domain_key}
Get Domain Details
Retrieves detailed information, assessment guidance, and benchmark data for a specific Zero Trust domain.
Parameters:
| Name | Type | Required | Location | Description | |------|------|----------|----------|-------------| | domain_key | string | Yes | Path | The key identifier for a Zero Trust principle or domain (e.g., "verification_conventional", "assume_breach_ai") |
Response:
Error Responses:
Pricing
| Plan | Calls/Day | Calls/Month | Price | |------|-----------|-------------|-------| | Free | 5 | 50 | Free | | Developer | 20 | 500 | $39/mo | | Professional | 200 | 5,000 | $99/mo | | Enterprise | 100,000 | 1,000,000 | $299/mo |
About
ToolWeb.in - 200+ security APIs, CISSP & CISM, platforms: Pay-per-run, API Gateway, MCP Server, OpenClaw, RapidAPI, YouTube.
References
π‘ Examples
Example Request:
{
"sessionId": "sess-abc123def456",
"userId": 1001,
"timestamp": "2024-01-15T10:30:00Z",
"assessmentData": {
"sessionId": "sess-abc123def456",
"timestamp": "2024-01-15T10:30:00Z",
"verification_conventional": {
"mfa_enabled": true,
"mfa_coverage_percent": 95,
"device_verification": "enabled"
},
"verification_ai": {
"model_provenance_tracking": true,
"data_lineage_implemented": true
},
"least_privilege_conventional": {
"rbac_implemented": true,
"principle_of_least_privilege_score": 78
},
"least_privilege_ai": {
"model_access_controls": true,
"training_data_access_restricted": true
},
"assume_breach_conventional": {
"segmentation_level": "advanced",
"incident_response_plan": "documented"
},
"assume_breach_ai": {
"model_poisoning_detection": true,
"adversarial_testing_frequency": "quarterly"
},
"microsegmentation_conventional": {
"network_segments": 12,
"segment_isolation_score": 85
},
"microsegmentation_ai": {
"model_isolation": true,
"inference_sandbox": "enabled"
},
"continuous_monitoring_conventional": {
"siem_deployed": true,
"log_retention_days": 365
},
"continuous_monitoring_ai": {
"model_drift_monitoring": true,
"inference_anomaly_detection": "enabled"
},
"supply_chain_conventional": {
"vendor_assessment_process": "defined",
"sbom_requirement": true
},
"supply_chain_ai": {
"model_source_verification": true,
"training_data_provenance_verified": true
}
}
}
Example Response:
{
"status": "success",
"assessment_id": "ztai-2024-001",
"sessionId": "sess-abc123def456",
"timestamp": "2024-01-15T10:30:45Z",
"overall_maturity_score": 82,
"conventional_maturity_score": 84,
"ai_maturity_score": 80,
"principle_scores": {
"verification": {
"conventional": 95,
"ai": 88
},
"least_privilege": {
"conventional": 78,
"ai": 82
},
"assume_breach": {
"conventional": 80,
"ai": 75
},
"microsegmentation": {
"conventional": 85,
"ai": 78
},
"continuous_monitoring": {
"conventional": 88,
"ai": 85
},
"supply_chain": {
"conventional": 72,
"ai": 68
}
},
"recommendations": [
{
"principle": "supply_chain_ai",
"priority": "high",
"action": "Implement model source verification and training data provenance tracking"
},
{
"principle": "assume_breach_ai",
"priority": "medium",
"action": "Increase adversarial testing frequency and enhance model poisoning detection"
}
]
}