LLM security best practices
Eight actionable best practices for securing enterprise LLM deployments — from prompt-level defense through governance and SOC integration. Prioritized by impact and implementation order.
01
Deploy prompt-level inspection
Every AI interaction should pass through a prompt firewall before reaching the provider. Detect PII, injection attempts, and policy violations in real-time.
Learn more →02
Implement AI-native DLP
Traditional DLP cannot see AI prompts. Deploy purpose-built AI DLP that detects and masks sensitive data inside browser, editor, and CLI workflows.
Learn more →03
Discover shadow AI
Identify all AI tools in use across the organization. You cannot secure what you cannot see. Shadow AI detection provides complete visibility.
Learn more →04
Establish governance framework
Define AI usage policies, acceptable use criteria, and automated enforcement. Governance should enable productive AI use under security controls.
Learn more →05
Maintain audit trails
Log every AI interaction with inspection-grade detail. Audit trails enable incident response, compliance reporting, and continuous security improvement.
Learn more →06
Integrate with SOC
Forward AI security events to existing SIEM infrastructure. AI security should be part of the unified security monitoring program, not a separate silo.
Learn more →07
Conduct LLM threat modeling
Apply OWASP Top 10 for LLMs to your specific AI deployments. Identify high-risk scenarios and prioritize controls based on business impact.
Learn more →08
Plan for compliance
Map AI security controls to regulatory requirements (SOC 2, HIPAA, EU AI Act). Automated compliance evidence reduces audit burden.
Learn more →Implementation priority
Start with practices 1–3 (prompt firewall, AI DLP, shadow AI discovery) for immediate risk reduction. Add practices 4–5 (governance, audit trails) within the first month. Implement 6–8 (SOC integration, threat modeling, compliance) in the following quarter. This phased approach delivers security value quickly while building toward comprehensive coverage.
Common mistakes to avoid
- Blocking instead of governing — Prohibition drives shadow AI. Secure access is more effective than denied access.
- Relying on provider safety — AI provider safety features protect the provider, not your data. You need your own controls.
- Treating AI security separately — AI security should integrate with existing SOC, GRC, and incident response programs.
- Delaying until a breach — Proactive deployment costs a fraction of breach response. Start with the highest-risk AI surfaces first.
Implement LLM security best practices
Deploy PromptWall to address the top 8 enterprise AI security priorities.
Frequently asked questions
What is the single most important LLM security control?+
Prompt-level inspection. Every other control (audit trail, governance, SOC integration) depends on visibility into what users send to AI models. Without prompt inspection, you have no data to audit, no input to govern, and no events to integrate. Deploy a prompt firewall first.
Should I block AI tools or secure them?+
Secure them. Blocking AI tools pushes employees to use personal accounts and workarounds — creating shadow AI. A policy-based approach — inspect, mask sensitive data, enforce governance — enables productivity while maintaining security control.
How do I measure LLM security effectiveness?+
Key metrics: percentage of AI interactions under governance, PII detection rate, injection attempt blocking rate, audit trail coverage, mean time to detection for security events, and compliance audit pass rate.