OWASP LLM Risks and Mitigations

The surge of interest in large language models (LLMs) has revolutionized how organizations interact with data, automate decisions, and extract insights. But with this transformative capability comes a new set of security and compliance risks. The OWASP Top 10 for LLM Applications highlights these vulnerabilities and provides mitigation strategies crucial for any organization using generative AI.

This article explores those risks with a practical focus on real-time auditing, dynamic masking, data discovery, and how to maintain data compliance in GenAI-powered environments.

Understanding the Landscape: LLM Risk Categories

OWASP categorizes LLM-specific risks into areas such as prompt injection, data leakage, model misuse, insecure plugins, and training data poisoning. These threats exploit the very strengths of GenAI—its adaptability and contextual learning. Might trick the model into bypassing internal guardrails and leaking sensitive outputs.

Real-Time Audit: Watch Everything, Respond Instantly

Mitigating risks in dynamic LLM environments starts with visibility. Real-time audit capabilities enable organizations to monitor queries, responses, and data access patterns as they occur. Tools like DataSunrise Audit Logs can flag unauthorized data access attempts or suspicious prompt patterns immediately.

For example, a security rule could detect repeated attempts to enumerate sensitive schema fields and trigger alerts via Slack or MS Teams:

{
  "event_type": "query_attempt",
  "query": "SELECT * FROM information_schema.columns WHERE ...",
  "action": "alert_and_block"
}

With support for real-time notifications and learning-based audit rules, teams can adapt audit logic to evolving LLM threats.

Dynamic Masking: Don’t Just Block—Obscure

Dynamic data masking protects PII and sensitive fields during LLM interactions without breaking application functionality. Instead of removing access entirely, it substitutes real values with masked placeholders.

For instance, the response to a vector query:

SELECT name, credit_card_number FROM customers LIMIT 1;

might return:

John Doe | XXXX-XXXX-XXXX-1234

This capability is vital for use cases involving prompt augmentation or RAG (Retrieval-Augmented Generation), where the LLM must fetch and synthesize enterprise data without violating privacy rules. Dynamic masking in DataSunrise allows fine-tuned policies per role or model behavior.

Data Discovery: Know What You Have Before LLMs Do

Before implementing LLM security measures, it's essential to understand where sensitive data resides. That’s where automated data discovery tools come into play.

Mapping data assets across unstructured sources, relational databases, and vector indexes can help prioritize controls and reduce exposure. Integration with LLM pipelines ensures that only compliant and classified data is eligible for augmentation or inference.

Data Compliance: Aligning LLM Use with Global Standards

GDPR, HIPAA, PCI DSS, and other regulations apply regardless of whether data is processed by humans or machines. As such, integrating LLMs into data workflows demands traceability, access control, and clear audit trails.

Solutions like the DataSunrise Compliance Manager offer a centralized dashboard for managing compliance artifacts, masking policies, and audit outputs. This helps organizations pass audits and demonstrate responsible LLM use.

Additionally, support for GDPR and HIPAA is baked into the audit and masking layers, ensuring LLM outputs never leak protected health or personal information.

Secure Prompt Interfaces and Role-Based Controls

Applying role-based access control (RBAC) ensures that prompts from different user types (admin, analyst, external API) are governed by different policies. Combined with data-inspired security techniques, this helps align LLM usage with enterprise-grade standards.

Secure plugin systems, sandboxed execution environments, and security rules against SQL injection also play a vital role in protecting against common LLM exploits.

Conclusion: A Balanced, Audited, Masked Future for GenAI

The integration of GenAI into enterprise environments is inevitable, but it must be done responsibly. By embracing OWASP’s LLM risk framework, enforcing real-time auditing, applying dynamic masking, enabling comprehensive data discovery, and adhering to compliance norms, organizations can safely unlock the full potential of generative AI.

To learn more about building secure GenAI systems with audit and masking, explore our resources on Database Activity Monitoring and Data Security.