Building secure AI applications requires security thinking throughout the development lifecycle, not just at deployment.

Secure Development Lifecycle for AI

Design phase:

• Threat modeling: Identify what an attacker could gain and how they might try
• Minimal authority: Design the system with the least privilege necessary
• Human oversight: Plan where human approval is required
• Fail-safe defaults: When in doubt, refuse rather than comply

Implementation phase:

• Never put secrets in system prompts (the model can leak them)
• Sanitize all inputs before passing to the LLM
• Validate all outputs before acting on them
• Log all interactions for audit and incident response
• Implement rate limiting and abuse detection

Testing phase:

• Automated adversarial testing with known attack datasets
• Manual red teaming by security-minded team members
• Boundary testing: push every feature to its limits
• Test with real user data patterns (anonymized)

Deployment phase:

• Monitor for novel attack patterns
• Alert on unusual behavior (high refusal rates, unusual topics)
• Have incident response procedures ready
• Plan for rapid model/prompt updates when vulnerabilities are found

Secure Tool Integration

When your LLM calls external tools:

# BAD: LLM directly constructs SQL
query = llm.generate(f"Write SQL for: {user_request}")
db.execute(query)  # SQL injection risk!

# GOOD: LLM selects from predefined actions
action = llm.select_action(user_request, allowed_actions=[
    "get_order_status",
    "list_products", 
    "update_preferences",
])
result = execute_action(action, validated_params)

Supply Chain Security

• Verify model sources (download only from official repositories)
• Pin model versions (don't auto-update in production)
• Audit third-party prompts and templates
• Review plugin/extension permissions before integration
• Monitor for model poisoning attacks on fine-tuning data

Incident Response for AI

When a security incident occurs:

1. Contain: Disable the affected feature or endpoint immediately
2. Assess: Determine what data was exposed or what harm occurred
3. Investigate: Review logs to understand the attack vector
4. Remediate: Patch the vulnerability (update prompts, add filters, restrict access)
5. Communicate: Notify affected users and stakeholders as required
6. Learn: Update threat models and security tests to prevent recurrence