OWASP Checklist for LLM AI Security & Governance

As the use of large language models (LLMs) expands in enterprise security and governance frameworks, the OWASP Foundation has begun highlighting critical areas of concern. This article explores the practical application of the OWASP Checklist for LLM AI Security & Governance, particularly focusing on real-time audit, dynamic data masking, data discovery, general data security, and regulatory compliance.

Why the OWASP Checklist Matters for GenAI Systems

LLMs like GPT-4 and Claude are being integrated into tools that analyze logs, classify threats, or automate incident response. However, these models introduce novel risks, including model inversion attacks, prompt injection, sensitive data leakage, and misuse of internal data repositories. The OWASP Top 10 for LLMs provides a framework to identify and mitigate these risks. Integrating this with enterprise governance ensures that GenAI remains an asset, not a liability.

Real-Time Audit for AI Activity and Decision Chains

LLMs often operate as black boxes. Logging user prompts, generated completions, model decisions, and backend data lookups is critical for auditing. Real-time audit systems like DataSunrise Audit can intercept and log:

SELECT *
FROM vector_logs
WHERE embedding_model = 'GPT-4' AND confidence_score < 0.5;

This example query can surface uncertain or low-confidence model outputs for review. Systems like Database Activity Monitoring allow tagging and alerting based on unusual usage patterns or access to sensitive vector stores.

Dynamic Masking in LLM-Driven Queries

When an LLM generates SQL queries dynamically, there's a real chance it may expose sensitive data. Using dynamic masking ensures that even if a prompt triggers a data retrieval operation, personally identifiable information (PII) like emails or SSNs remains obfuscated.

For example:

SELECT name, MASK(email), MASK(phone_number)
FROM customers
WHERE interaction_type = 'chatbot';

This lets GenAI-driven systems function safely in customer-facing apps without violating privacy obligations.

Data Discovery and LLM Input Filtering

Effective data discovery helps identify which parts of the data warehouse or vector store contain sensitive records. When combined with LLM pipelines, this ensures input prompts do not retrieve or inject unauthorized context.

OWASP recommends classification and filtering of training data and runtime inputs to mitigate data leakage. Tools like Amazon Macie, DataSunrise's discovery engine, and vector metadata scanning play a vital role.

Aligning with Security Policies and Threat Models

According to OWASP guidance, the threat surface of LLMs includes exposed APIs, third-party plug-ins, insecure model configuration, and over-permissive database access. Governance should include:

• Limiting model output context via system prompts.

• Enforcing RBAC (Role-Based Access Controls).

• Blocking dangerous queries using a Database Firewall).

OWASP also recommends behavior analytics to detect prompt flooding or abuse.

Ensuring Data Compliance in Generative AI Use

Compliance remains central when GenAI interacts with protected data (GDPR, HIPAA, PCI DSS). A compliance manager for GenAI pipelines helps map:

• What data types are processed by the model.

• Whether outputs are stored or logged.

• If inferred information can re-identify users.

In practice, this involves configuring automated compliance reporting and combining audit trail insights with masking and filtering rules.

External Reference Points

Several external resources also address the intersection of GenAI and data governance:

• NIST AI Risk Management Framework for structured governance.

• Microsoft’s Responsible AI Standard for large model deployments.

• AWS guidance on Athena monitoring and masking for AI-accessible datasets.

These can complement OWASP principles and help establish end-to-end AI lifecycle security.

Conclusion

The OWASP Checklist for LLM AI Security & Governance is more than a policy document. It’s a blueprint for reducing risks as generative AI becomes embedded in security operations, compliance monitoring, and decision-making. With tools like DataSunrise Audit, dynamic masking, and data discovery engines, organizations can enforce boundaries around AI behavior while still benefiting from its power.

Data governance must evolve alongside AI. Combining OWASP’s focus with concrete tools makes that not only possible, but practical.