Aribot Security Platform SDK by Aristiun & Ayurak - Threat modeling, compliance, and cloud security APIs
Project description
Aribot Python SDK
Official Python SDK for the Aribot Security Platform by Aristiun & Ayurak.
Installation
pip install aribot
Quick Start
from aribot import Aribot
client = Aribot(api_key="your_api_key")
# Analyze architecture diagram for threats
result = client.threat_modeling.analyze_diagram("architecture.png")
print(f"Found {result['threat_count']} threats")
# Get detailed threats
threats = client.threat_modeling.get_threats(result['diagram_id'])
for threat in threats:
print(f"[{threat['severity']}] {threat['title']}")
# Digital Twin - List cloud providers
providers = client.digital_twin.get_providers()
for p in providers:
print(f"Provider: {p['name']} - {p['display_name']}")
# Economics - Get dashboard
dashboard = client.economics.get_dashboard()
print(f"Total Monthly Cost: ${dashboard['company_summary']['total_monthly']}")
# Red Team - Get methodologies
methodologies = client.redteam.get_methodologies()
for m in methodologies:
print(f"Methodology: {m['name']}")
Features
- Threat Modeling - Upload diagrams, detect components, identify threats
- Compliance Scanning - ISO 27001, SOC2, GDPR, HIPAA, PCI-DSS, NIST
- Cloud Security - Scan AWS, Azure, GCP for misconfigurations
- Pipeline Security - SAST, SCA, secrets detection in CI/CD
- Digital Twin - Cloud provider integration, resource discovery, health monitoring
- Economics - Cost analysis, ROI calculations, market intelligence
- Red Team - Attack simulations, methodologies, threat intelligence
API Reference
Threat Modeling
# Upload and analyze a diagram
result = client.threat_modeling.analyze_diagram(
"architecture.png",
analysis_depth="comprehensive", # basic, comprehensive, detailed
wait=True, # wait for analysis to complete
timeout=300 # max wait time in seconds
)
# List diagrams
diagrams = client.threat_modeling.list(page=1, limit=25)
# Get diagram details
diagram = client.threat_modeling.get(diagram_id)
# Get threats for a diagram
threats = client.threat_modeling.get_threats(diagram_id)
# Get detected components
components = client.threat_modeling.get_components(diagram_id)
# Run AI-powered analysis
ai_result = client.threat_modeling.analyze_with_ai(
diagram_id,
analysis_types=["attack_paths", "data_flow"]
)
# Delete a diagram
client.threat_modeling.delete(diagram_id)
# Get dashboard metrics
dashboard = client.threat_modeling.dashboard(period="month")
Compliance Scanning
# Run compliance scan
result = client.compliance.scan(
diagram_id,
standards=["ISO27001", "SOC2", "GDPR"],
include_recommendations=True
)
print(f"Compliance score: {result['overall_score']}%")
# Get compliance report
report = client.compliance.get_report(diagram_id, format="json")
# List available standards
standards = client.compliance.list_standards()
# Get standard details
iso = client.compliance.get_standard("ISO27001")
# List controls for a standard
controls = client.compliance.list_controls("SOC2", category="access_control")
# Get compliance gaps
gaps = client.compliance.get_gaps(diagram_id, standard_id="ISO27001")
# Create custom standard
custom = client.compliance.add_custom_standard(
name="Internal Security Policy",
description="Company security requirements",
controls=[
{
"id": "ISP-001",
"name": "Data Encryption",
"description": "All data must be encrypted at rest",
"severity": "high"
}
]
)
# Get compliance dashboard
dashboard = client.compliance.dashboard(period="quarter")
Cloud Security
# Run cloud security scan
scan = client.cloud.scan(
project_id="123456789012",
provider="aws",
services=["iam", "s3", "ec2"],
compliance_standards=["CIS-AWS"]
)
# Get scan results
scan = client.cloud.get_scan(scan_id)
# List scans
scans = client.cloud.list_scans(provider="aws", status="completed")
# Get findings
findings = client.cloud.get_findings(
scan_id,
severity="critical",
service="s3"
)
# Connect AWS account
account = client.cloud.connect_account(
provider="aws",
credentials={
"role_arn": "arn:aws:iam::123456789012:role/AribotSecurityRole",
"external_id": "your-external-id"
},
name="Production AWS"
)
# Connect GCP project
account = client.cloud.connect_account(
provider="gcp",
credentials={
"service_account_key": "{ ... }",
"project_id": "my-project-123"
}
)
# Connect Azure subscription
account = client.cloud.connect_account(
provider="azure",
credentials={
"tenant_id": "...",
"client_id": "...",
"client_secret": "..."
}
)
# List connected accounts
accounts = client.cloud.list_accounts(provider="aws")
# Get remediation steps
remediation = client.cloud.get_remediation(finding_id)
# Resolve a finding
client.cloud.resolve_finding(
finding_id,
resolution="fixed",
notes="Patched in deployment v1.2.3"
)
# Suppress a finding
client.cloud.suppress_finding(
finding_id,
reason="Accepted risk per security review",
duration_days=90
)
# Get cloud security dashboard
dashboard = client.cloud.dashboard(project_id="123456789012")
Pipeline Security
# Create a project
project = client.pipeline.create_project(
name="my-api",
repository_url="https://github.com/org/my-api",
scan_types=["sast", "sca", "secrets"]
)
# Run security scan
result = client.pipeline.scan(
project_id,
commit_sha="abc123def456",
branch="main",
scan_types=["sast", "sca", "secrets"],
fail_on_severity="high",
wait=True
)
if result['status'] == 'failed':
print("Security gate failed!")
for finding in result['blocking_findings']:
print(f" [{finding['severity']}] {finding['title']}")
# Get scan details
scan = client.pipeline.get_scan(scan_id)
# Get specific finding types
sast_findings = client.pipeline.get_sast_findings(scan_id)
sca_findings = client.pipeline.get_sca_findings(scan_id)
secrets = client.pipeline.get_secrets_findings(scan_id)
# Configure security gates
client.pipeline.configure_gates(
project_id,
gates={
"fail_on_critical": True,
"fail_on_high": True,
"max_high_findings": 5,
"block_secrets": True,
"required_scan_types": ["sast", "secrets"]
}
)
# Set baseline (suppress existing findings)
client.pipeline.add_baseline(project_id, scan_id)
# Suppress a finding
client.pipeline.suppress_finding(
finding_id,
reason="False positive - validated manually"
)
# Get pipeline dashboard
dashboard = client.pipeline.dashboard(project_id=project_id)
Digital Twin
# Get cloud providers
providers = client.digital_twin.get_providers()
# Returns: [{'id': '...', 'name': 'aws', 'display_name': 'Amazon Web Services', 'connected': False}, ...]
# Get provider health
health = client.digital_twin.get_health()
# Returns: {'status': 'ok', 'configured': True, 'infrastructure': {...}, 'capabilities': [...]}
# Get analytics
analytics = client.digital_twin.get_analytics()
# Returns: {'configured': True, 'graph_statistics': {...}, 'entities_by_type': {...}}
# Get resources
resources = client.digital_twin.get_resources(limit=50)
# Returns: [{'id': '...', 'name': 'my-bucket', 'provider': 'aws', 'resource_type': 's3'}, ...]
# Sync resources from provider
result = client.digital_twin.sync_resources(provider_id='aws-123')
# Returns: {'status': 'syncing', 'resources_found': 150}
# Discover new resources
discovery = client.digital_twin.discover_resources(provider_id='aws-123')
# Returns: {'status': 'discovered', 'new_resources': 25}
Economics
# Get economics dashboard
dashboard = client.economics.get_dashboard(period='month')
# Returns: {'success': True, 'company_summary': {'total_monthly': 5000, 'total_annual': 60000}, ...}
# Get diagram cost analysis
cost = client.economics.get_diagram_cost_analysis(diagram_id)
# Returns: {'monthly_cost': 1500, 'annual_cost': 18000, 'cost_breakdown': [...]}
# Get component cost
component_cost = client.economics.get_component_cost(component_id)
# Returns: {'component': 'EC2 Instance', 'monthly_cost': 200, 'recommendations': [...]}
# Get economic intelligence
intel = client.economics.get_economic_intelligence()
# Returns: {'status': 'success', 'provider': 'aws', 'pricing': {...}}
# Get market intelligence
market = client.economics.get_market_intelligence()
# Returns: {'trends': [...], 'benchmarks': {...}, 'recommendations': [...]}
# Calculate ROI
roi = client.economics.calculate_roi(
investment=100000,
risks_addressed=['risk-1', 'risk-2'],
timeframe_days=365
)
# Returns: {'roi_percentage': 250, 'npv': 150000, 'payback_months': 8}
Red Team
# Get threat modeling methodologies
methodologies = client.redteam.get_methodologies()
# Returns: [{'id': 'stride', 'name': 'STRIDE', 'description': '...'}, ...]
# Get simulations
simulations = client.redteam.get_simulations(limit=10)
# Returns: [{'id': '...', 'name': 'APT29 Simulation', 'status': 'completed'}, ...]
# Get threat intelligence
intel = client.redteam.get_intelligence()
# Returns: {'threats': [...], 'indicators': [...], 'campaigns': [...]}
# Generate attack paths for a diagram
paths = client.redteam.generate_attack_paths(
diagram_id,
scope='single',
include_compliance=True
)
# Returns: {'status': 'success', 'paths': [{'title': '...', 'risk_score': 85, 'steps': [...]}]}
# Get security requirements
requirements = client.redteam.get_security_requirements(diagram_id)
# Returns: [{'id': '...', 'requirement': '...', 'priority': 'high'}, ...]
Error Handling
from aribot import (
Aribot,
AribotError,
AuthenticationError,
RateLimitError,
ValidationError,
NotFoundError,
ServerError
)
client = Aribot(api_key="your_api_key")
try:
result = client.threat_modeling.analyze_diagram("diagram.png")
except AuthenticationError:
print("Invalid API key")
except RateLimitError as e:
print(f"Rate limited. Retry after {e.retry_after} seconds")
except ValidationError as e:
print(f"Invalid request: {e.errors}")
except NotFoundError:
print("Resource not found")
except ServerError:
print("Server error - try again later")
except AribotError as e:
print(f"API error: {e.message}")
Configuration
# Custom base URL (for on-premise deployments)
client = Aribot(
api_key="your_api_key",
base_url="https://aribot.internal.company.com/api",
timeout=60
)
# Check API health
health = client.health()
# Get current user info
user = client.me()
# Get usage stats
usage = client.usage(period="month")
print(f"API calls used: {usage['calls_used']}/{usage['calls_limit']}")
CI/CD Integration
GitHub Actions
- name: Security Scan
env:
AYURAK_API_KEY: ${{ secrets.AYURAK_API_KEY }}
run: |
pip install aribot
python -c "
from aribot import Aribot
client = Aribot(api_key='$AYURAK_API_KEY')
result = client.pipeline.scan(
project_id='${{ vars.PROJECT_ID }}',
commit_sha='${{ github.sha }}',
fail_on_severity='high',
wait=True
)
if result['status'] == 'failed':
exit(1)
"
GitLab CI
security_scan:
script:
- pip install aribot
- python scripts/security_scan.py
variables:
AYURAK_API_KEY: $AYURAK_API_KEY
Support
- Documentation: https://developers.aribot.com/docs/python-sdk
- API Reference: https://developers.aribot.com/api
- Issues: https://github.com/AribotAI/aribot-python/issues
Changelog
v1.4.0
- Updated base URL to
api.aribot.ayurak.com - Added AI module (
client.ai) - usage, quota, models, configure, analyze, queue status - Added SBOM module (
client.sbom) - document management and vulnerability scanning - Added Dashboard module (
client.dashboard) - overview, recent activity, risk summary - Added FinOps module (
client.finops) - cost optimization recommendations and tracking - Added Marketplace module (
client.marketplace) - templates, categories, featured content - Added API Keys module (
client.api_keys) - key listing and revocation
v1.1.0
- Added Digital Twin, Economics, and Red Team modules
- Added Remediation module
v1.0.0
- Initial release with Threat Modeling, Compliance, Cloud Security, and Pipeline modules
License
MIT
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