Advanced

Best Practices

A comprehensive guide to building secure LLM applications from the ground up, with actionable checklists, architecture patterns, and organizational strategies.

Security-First Architecture Checklist

Layer	Control	Priority
Input	Input length limits, injection detection, content classification	Critical
System Prompt	No secrets in prompts, extraction resistance, instruction hierarchy	Critical
Model	Model selection, temperature tuning, token limits	High
Output	PII redaction, safety classification, output sanitization	Critical
Tools	Least privilege, input validation, human approval for high-risk actions	Critical
RAG	Source validation, content scanning, access control	High
Monitoring	Audit logging, anomaly detection, automated alerting	High
Infrastructure	API key rotation, rate limiting, network segmentation	High

Defense-in-Depth Architecture

# Complete security pipeline for LLM applications
class SecureLLMApplication:
    def handle_request(self, user_input, user_context):
        # Layer 1: Rate limiting and authentication
        self.rate_limiter.check(user_context)

        # Layer 2: Input security
        input_scan = self.input_scanner.scan(user_input)
        if input_scan.is_blocked:
            return self.blocked_response(input_scan.reason)

        # Layer 3: Context assembly with access controls
        context = self.rag.retrieve(
            user_input,
            access_level=user_context.permissions
        )

        # Layer 4: Hardened system prompt
        messages = self.prompt_builder.build(
            user_input, context,
            include_safety_instructions=True
        )

        # Layer 5: Model inference with guardrails
        response = self.model.generate(
            messages,
            max_tokens=self.config.max_output_tokens
        )

        # Layer 6: Output security
        output_scan = self.output_scanner.scan(response)
        if output_scan.has_violations:
            response = self.output_scanner.remediate(response)

        # Layer 7: Audit logging
        self.audit_log.record(user_input, response, user_context)

        return response

Organizational Best Practices

Security Training for AI Teams

All developers building LLM features should receive training on LLM-specific security risks. This is different from traditional AppSec training and covers prompt injection, data leakage, and agent risks.
Security Review Process

Establish mandatory security review for all LLM feature launches. Reviews should cover system prompt design, tool permissions, data access patterns, and monitoring coverage.
Red Team Program

Maintain a continuous red teaming program that tests LLM applications against evolving attack techniques. Include both automated and manual red teaming.
Incident Response Readiness

Ensure AI-specific incident response playbooks exist for every LLM application. Conduct regular tabletop exercises. Maintain model rollback capabilities.

Compliance and Governance

Data Privacy

Ensure GDPR, CCPA, and other privacy law compliance. Implement data minimization, purpose limitation, and right-to-erasure for training data and conversation logs.

AI Transparency

Disclose AI usage to users. Maintain documentation of model capabilities, limitations, and known failure modes. Support explainability requirements.

Risk Assessment

Conduct formal risk assessments for each LLM application. Document threats, mitigations, residual risks, and acceptance criteria aligned with organizational risk appetite.

Third-Party Risk

Assess the security posture of LLM API providers, model hosting platforms, and plugin/tool vendors. Include AI-specific clauses in vendor contracts.

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Course Complete: Congratulations on completing the LLM Security Fundamentals course! You now have a comprehensive understanding of LLM security risks and defenses. Continue with our Prompt Injection Defense Advanced course for deep-dive techniques.