GenAI Security & Governance Checklist for LLMs
Generative AI (GenAI), particularly models powered by large language models (LLMs), introduces a new frontier of innovation—and risk. These models ingest, generate, and process data at scale, often pulling from diverse and sensitive sources. With great power comes the urgent need for strong security and governance.
This checklist outlines how to secure GenAI pipelines through layered controls: visibility, protection, accountability, and automation.
Real-Time Monitoring for LLM Workloads
LLMs may not look like traditional databases, but their behavior should be logged just as thoroughly. Capturing every interaction—from prompts to generated responses—lets you track sensitive data exposure, detect abuse, and satisfy compliance auditors. Tools like Database Activity Monitoring provide this visibility in real-time.
SELECT * FROM pg_stat_activity WHERE datname = 'llm_logs';
This PostgreSQL example shows how to correlate database sessions with LLM usage metadata.
Also see: Learning Rules and Audit for tuning audit policies.
Real-Time Data Masking in GenAI Pipelines
Sensitive data such as customer emails, tokens, or healthcare records must not leak into prompts, responses, or logs. Dynamic data masking acts in real-time, adjusting based on user role or access level.
It’s especially valuable in environments like prompt engineering tools, RAG-based systems, and shared inference APIs—where user interactions are frequent and data risks are high.
For deeper insight into protecting sensitive fields, visit the article on In-Place Masking.
Automated Discovery of Sensitive Data
You can’t govern what you can’t find. Automated data discovery scans are crucial to locating sensitive fields, especially in vector stores or multi-modal pipelines.
These scans classify input prompts and user data, label outputs for policy enforcement, and support regulatory needs such as GDPR or HIPAA. They help teams maintain control over what the model learns and outputs.
Check out Treating Data as a Valuable Asset to reinforce your discovery strategy.
Access Control and Prompt-Level Restrictions
LLMs are susceptible to misuse through prompt injection or plugin escalation. To reduce these risks, implement role-based access control (RBAC) and input filtering. These measures prevent leaks from low-trust users, limit prompt abuse targeting administrative actions, and help manage access across teams and departments.
You may also want to explore Access Controls and Least Privilege Principle to harden LLM deployments.
Manual reviews won’t scale. Tools like Compliance Manager help define regulatory mappings, enforce access and retention rules, and generate reports on demand.
For example:
CREATE MASKING RULE mask_email
ON llm_prompt_logs(email)
USING FULL;
This rule ensures that email data is masked before it’s analyzed or exported.
You can further explore the importance of this step in Report Generation.
Resources Worth Exploring
Explore these references for further insight into securing LLM pipelines:
- NIST AI Risk Management Framework
- Microsoft Responsible AI
- OWASP Top 10 for LLMs
- Google Secure AI Framework (SAIF)
- Anthropic’s Constitutional AI paper
Build for Visibility and Trust
Securing GenAI is about layering smart controls, not blocking innovation. Your governance stack should include real-time auditability, role-aware masking, continuous discovery, and automated compliance checks.
To go deeper, read about Audit Trails, Data Security, LLM and ML Tools for Database Security, and Synthetic Data Generation as an alternative for testing or training GenAI systems securely.
