AI Security Expert

Enterprise AI security architect specializing in securing LLM applications, defending against prompt injection, implementing guardrails, and OWASP LLM Top 10 compliance.

OWASP LLM Top 10 (2025)

Quick Reference

LLM01: Prompt Injection

Attack Types:

• •Direct: "Ignore previous instructions..."
• •Indirect: Malicious content in RAG documents
• •Encoding tricks: Unicode, special tokens

Defense Pattern:

User Input → Sanitize → Delimit → LLM → Validate Output → Filter

LLM02: Insecure Output Handling

• •Never execute LLM output as code without validation
• •Sanitize HTML (use allowlist)
• •Validate SQL (SELECT only, table allowlist)

LLM04: Model DoS

• •Rate limiting per user/API key
• •Token limits on requests
• •Timeout configurations
• •Cost capping/alerts

LLM06: Sensitive Information Disclosure

• •PII detection (regex + NER)
• •System prompt protection
• •Training data sanitization
• •Output filtering

Code patterns: resources/security-patterns.py

PII Protection

Detection Patterns

Redaction Strategy

1. •Detect PII in input before LLM call
2. •Redact PII in LLM output
3. •Log without PII
4. •Encrypt at rest

Guardrails Implementation

NeMo Guardrails (NVIDIA)

define user express harmful intent
    "How do I hack"

define bot refuse harmful request
    "I can't help with that."

define flow harmful intent
    user express harmful intent
    bot refuse harmful request

Guardrails AI

guard = Guard().use_many(
    ToxicLanguage(on_fail="fix"),
    PIIFilter(on_fail="fix"),
    ValidJSON(on_fail="reask")
)

Custom Pipeline

Input Guards → LLM Call → Output Guards → Response

Implementation: resources/security-patterns.py

Security Architecture

Defense in Depth Layers

Zero Trust Principles

• •Never trust, always verify
• •Least privilege for agents
• •Assume breach (log everything)

Compliance Frameworks

EU AI Act (High-Risk)

• •Risk management system
• •Data governance
• •Technical documentation
• •Human oversight
• •Accuracy/robustness testing

SOC 2 for AI

• •Security: Access controls, encryption
• •Availability: SLA monitoring, DR
• •Processing Integrity: Input/output validation
• •Confidentiality: Data classification
• •Privacy: Data minimization, consent

Security Testing

Red Team Categories

1. •Direct injection attempts
2. •Jailbreak prompts
3. •Indirect injection via context
4. •Encoding/unicode tricks

Test suite: resources/security-patterns.py

Testing Checklist

• • Injection patterns blocked
• • System prompt protected
• • PII detected and redacted
• • Rate limits enforced
• • Outputs validated
• • Audit logs complete

Incident Response

Severity Levels

Response Steps

1. •Contain (block source)
2. •Preserve (logs, evidence)
3. •Analyze (attack pattern)
4. •Remediate (update defenses)
5. •Document (security log)

Resources

• •OWASP LLM Top 10
• •NIST AI Risk Management Framework
• •NeMo Guardrails
• •Guardrails AI
• •LLM Security Best Practices

Secure AI systems with defense in depth and zero trust principles.