Products
Enterprise-grade tools for securing AI agent and LLM deployments.
A Rust framework that wraps AI/LLM calls with enterprise-grade security. Protect your agents from prompt injection, data leakage, and privilege escalation.
Comprehensive protection across the entire AI agent attack surface
Built with our SDK, this AI-powered code assistant combines static analysis and real-time threat detection to ensure all generated code meets rigorous security standards. Vulnerabilities like prompt injection, SQL injection, and code injection are reliably caught and prevented before code ever reaches production.
AI-powered customer support with built-in PII protection and conversation safety. Automatically detects and redacts sensitive data like credit cards, SSNs, and emails. Includes sentiment analysis with escalation detection to route frustrated customers to human agents when needed.
Comprehensive compliance and security analysis agent supporting SOC 2, ISO 27001/42001, NIST AI RMF, and OWASP Top 10 for LLMs. Automatically analyzes security logs, detects prompt injection attempts, generates compliance reports, and creates interactive dashboards — all powered by Claude and GPT models.
HIPAA-compliant AI medical assistant with built-in PHI protection and clinical guardrails. Automatically redacts patient identifiers, enforces scope limitations to prevent diagnosis beyond its training, and maintains complete audit trails. Integrates with EHR systems while ensuring patient data never leaves secure boundaries.
Click any example to see how our SDK protects your AI applications
# Vulnerable code sample query = "SELECT * FROM users WHERE id = " + user_input cursor.execute(query)
$ ./code-assistant analyze --file vulnerable.py --language python
{
"language": "python",
"security_score": 85.0,
"issues": [
{
"severity": "High",
"line": 1,
"description": "Potential SQL injection vulnerability",
"fix": "Use parameterized queries"
}
],
"safe_to_execute": false
}
$ ./code-assistant interactive
> analyze import os; os.system("rm -rf /")
✗ Security Score: 0.0/100 Issues: 1 CRITICAL (Line 1): Dangerous function detected: os.system Fix: Use safer alternatives with input validation # The code is blocked from execution
$ ./customer-support-bot ticket \ --message "Urgent: payment failed with card 1234-5678-9012-3456" \ --priority critical
Ticket Created ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ID: 550e8400-e29b-41d4-a716-446655440000 Priority: Critical Category: Billing Status: Open Message: Urgent: payment failed with card [REDACTED] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
$ ./customer-support-bot sentiment \ --conversation "I'm very angry! This is the third time my order was delayed!"
Sentiment Analysis ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Overall: Angry Confidence: 80.0% Urgency: 90.0% Escalate: YES Emotions: • angry (70.0%) • frustrated (70.0%) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
from data_analyst_agent import AISecurityDataAnalyst analyst = AISecurityDataAnalyst() # Generate SOC 2 compliance report report = analyst.generate_compliance_report( framework='SOC2', data_source='compliance_data.csv' ) # Export in multiple formats analyst.export_report(report, format='html')
{
"framework": "SOC2",
"generated_at": "2025-10-10T11:28:48",
"summary": {
"total_controls": 12,
"compliant": 6,
"non_compliant": 3,
"in_progress": 3,
"compliance_rate": 50.0
},
"gaps_by_severity": {
"critical": 2,
"high": 2,
"medium": 2
},
"recommendations": [
"URGENT: Address 2 critical gaps immediately",
"High priority: Remediate within 30 days"
]
}