VERSION 1.0 • JANUARY 2026
The minimum standard for shipping AI in production — 2026 and beyond. Open, free, and practical.
🔍 Pre-Deployment Requirements
Complete these before any AI component goes to production.
✓
Threat Model AI Components
Document attack surfaces specific to your AI/LLM integration. Include data flows, trust boundaries, and potential abuse scenarios.
CRITICAL
✓
Prompt Injection Testing
Test for both direct (user input) and indirect (RAG/context) prompt injection vulnerabilities.
CRITICAL
✓
Output Validation Rules
Define and implement filtering for AI outputs. Block PII, code injection, harmful content, and policy violations.
HIGH
✓
Data Leakage Assessment
Test for training data extraction, PII disclosure, and sensitive business information leakage.
HIGH
✓
Jailbreak Resistance Testing
Verify system prompt protection against role-playing, encoding tricks, and instruction override attempts.
MEDIUM
⚙ CI/CD Integration
Automate security checks in your deployment pipeline.
✓
Automated Security Scan on Every PR
Run LLM security scans when AI-related code changes. Block merge if critical vulnerabilities found.
CRITICAL
✓
Security Gate Before Production
Require security sign-off for AI deployments. No production without passing security checks.
HIGH
✓
Scheduled Production Scans
Daily or weekly automated scans on live AI endpoints. Alert on new vulnerabilities.
MEDIUM
name: AI Security Scan on: [pull_request] jobs: security-scan: runs-on: ubuntu-latest steps: - uses: xsource-sec/agentaudit-action@v1 with: target: ${{ secrets.AI_ENDPOINT }} mode: full fail-on: critical,high
GitHub Action coming Q1 2026. For now, use AgentAudit web interface or API.
🛡 Runtime Protection
Protect your AI in production with these controls.
✓
Input Sanitization
Validate and sanitize all user prompts before sending to AI. Block known injection patterns.
CRITICAL
✓
Output Filtering
Scan AI responses for PII, code, harmful content before displaying to users.
CRITICAL
✓
Rate Limiting
Implement per-user and per-session limits. Prevent abuse and DoS attacks.
HIGH
✓
Anomaly Detection
Monitor for unusual patterns: token spikes, repeated jailbreak attempts, data exfiltration patterns.
MEDIUM
📋 Compliance & Audit
Meet regulatory requirements and maintain audit trails.
✓
AI Interaction Logging
Log all prompts and responses (with appropriate PII handling). Retain for compliance period.
HIGH
✓
Incident Response Plan
Document procedures for AI security incidents: jailbreaks, data leaks, harmful outputs.
HIGH
✓
Regular Security Reviews
Quarterly review of AI security posture. Update threat model as capabilities change.
MEDIUM
🎯 OWASP LLM Top 10 Coverage
How this baseline maps to the OWASP LLM Top 10 (2025).
| ID | Vulnerability | Baseline Coverage | AgentAudit |
|---|---|---|---|
| LLM01 | Prompt Injection | Pre-deployment testing, Runtime input sanitization | ✓ 200+ vectors |
| LLM02 | Insecure Output Handling | Output validation rules, Runtime filtering | ✓ Full |
| LLM03 | Training Data Poisoning | Threat modeling, Data assessment | ◐ Partial |
| LLM04 | Model Denial of Service | Rate limiting, Anomaly detection | ✓ Full |
| LLM05 | Supply Chain Vulnerabilities | Security reviews, CI/CD scanning | ◐ Partial |
| LLM06 | Sensitive Information Disclosure | Data leakage assessment, Output filtering | ✓ Full |
| LLM07 | Insecure Plugin Design | Threat modeling, MCP/RAG security | ✓ MCP Scanner |
| LLM08 | Excessive Agency | Threat modeling, Runtime controls | ✓ Agent Scanner |
| LLM09 | Overreliance | Output validation, Human review processes | ◐ Partial |
| LLM10 | Model Theft | Access controls, Audit logging | ◐ Partial |
🗺 Framework Mapping
How this baseline aligns with major compliance frameworks.
🇪🇺 EU AI Act
Risk assessment, documentation, human oversight, and transparency requirements for high-risk AI systems.
🇺🇸 NIST AI RMF
Map, Measure, Manage framework for AI risk. Covers governance, security, and trustworthiness.
🔒 SOC 2
Security, availability, and confidentiality controls extended to AI systems.
📜 ISO 27001
Information security controls applied to AI data handling and processing.
🏥 HIPAA
PHI protection requirements for AI systems handling healthcare data.
💳 PCI DSS
Payment data security requirements for AI processing cardholder information.