OWASP LLM Security Guidelines
Large Language Models (LLMs) are increasingly embedded in modern applications and workflows, driving everything from customer support bots to real-time data interpretation. But with great power comes new attack surfaces. The OWASP LLM Top 10 has sparked global discussions about how to approach GenAI risks, and now security teams must expand their playbooks.
This article explores how to use OWASP LLM Security Guidelines to secure LLM-integrated systems while achieving real-time audit, dynamic masking, data discovery, and regulatory compliance.
Understanding the OWASP LLM Landscape
OWASP’s initiative around LLMs offers a structured approach to identifying and mitigating vulnerabilities specific to GenAI systems. Some key threats include prompt injection, model denial-of-service, sensitive data leakage, and insecure plugin use.
Security teams can refer to the OWASP Top 10 for LLM Applications to align their GenAI adoption with a threat modeling process tailored for LLM workflows.
Real-Time Audit: Monitoring AI Behavior and Data Access
To safeguard systems powered by LLMs, it’s critical to implement audit mechanisms that capture both user interactions and internal prompts issued by the system.
Solutions like DataSunrise’s audit rules and database activity monitoring can provide continuous tracking of LLM interactions with structured data sources.
Here’s a PostgreSQL snippet for capturing access patterns by LLM agents:
SELECT datname, usename, query, backend_start, state_change
FROM pg_stat_activity
WHERE application_name LIKE '%llm_agent%';
Audit logs enriched with user context, model input/output, and timestamp metadata help organizations build forensic readiness and detect abnormal behavior.
Dynamic Data Masking for Prompt Safety
When LLMs access production data, unmasked sensitive fields can be inadvertently exposed in output. Integrating dynamic masking directly into the data pipeline is key.
Using DataSunrise’s dynamic masking capability, you can redact or tokenize personally identifiable information (PII) before it's sent into prompts.
A masking rule might replace real email addresses with placeholder values like:
REPLACE(email, SUBSTRING(email, 1, POSITION('@' IN email)-1), 'user')
This ensures compliance with GDPR and HIPAA when building AI solutions with real-world data.
Data Discovery: Building Trust in Your Input Corpus
LLMs can amplify risks if trained or fine-tuned on unknown or unclassified datasets. Data discovery tools let organizations automatically scan, label, and catalog sensitive data before it reaches an embedding or retrieval layer.
A robust discovery process helps reduce shadow AI pipelines and aligns with security policies like role-based access controls (RBAC).
External data governance solutions like Open Policy Agent can also integrate with these findings to enforce dynamic access conditions for prompt-building agents.
Security Policies: Aligning AI Workflows with Controls
LLMs can’t be monitored like traditional apps — their behaviors are probabilistic, context-sensitive, and often non-deterministic. Thus, organizations must apply layered security policies that include:
- Input validation and output filtering
- Rate limiting and API usage monitoring
- Plugin sandboxing or review workflows
Reference the DataSunrise security guide for building a defense-in-depth model tailored to GenAI operations.
To reduce LLM-induced security drift, some teams deploy a lightweight firewall or proxy to parse prompt content and redact any non-compliant data before sending it to the model. Security rules against SQL injection can be adapted to prevent prompt injection exploits.
OWASP-Inspired Protections at a Glance
| Category | Recommended Controls | Purpose |
|---|---|---|
| Input Handling | Validation, Length Checks, Token Filtering | Prevent prompt injection and misuse |
| Data Access | Role-based Access, Real-Time Audit, Masking | Enforce least privilege and compliance |
| Output Filtering | Regex Filters, Toxicity Check, Redaction | Limit exposure of harmful or private data |
| Plugin and Tooling Use | Plugin Whitelisting, Sandboxing, Rate Limiting | Reduce model compromise via integrations |
| Compliance Logging | Audit Trails, Alerting Rules, Data Classification | Support GDPR, HIPAA, PCI DSS requirements |
Compliance: When AI Meets Regulation
Adopting LLMs doesn’t exempt an organization from complying with standards. On the contrary, regulations like SOX, PCI DSS, and HIPAA apply even more stringently when automated agents access or act on sensitive data.
Using a compliance-aware AI infrastructure that combines audit logs, masking, and report generation tools enables businesses to document evidence for audits and demonstrate policy enforcement.
Several open-source initiatives are also emerging to standardize AI audit trails, such as Microsoft’s PyRIT and the EU’s efforts around the AI Act.
Conclusion: Building Secure and Compliant LLM Systems
Following the OWASP LLM Security Guidelines isn’t about limiting innovation — it’s about ensuring trust, accountability, and alignment with enterprise-grade security. By combining real-time audit, dynamic masking, and data discovery, organizations can tame the complexity of GenAI and unlock its full potential without exposing themselves to undue risk.
To dig deeper, explore DataSunrise’s compliance manager and its LLM/ML security insights for protecting hybrid and AI-powered databases.
