AI Security and Compliance Checklist

As Large Language Models (LLMs) transition from research labs to enterprise production, the attack surface for organizations has expanded overnight. For regulated industries, the challenge isn't just about making the AI work—it's about making it compliant.

This checklist provides a roadmap for Information Security Officers (CISOs) and DPOs to audit their AI infrastructure against global regulatory frameworks and modern security best practices.

1. Regulatory Mapping: GDPR, SOC2, & ISO

AI systems do not exist in a legal vacuum. Existing frameworks apply directly to how data is processed by and for LLMs.

GDPR Article 32 (Technical & Organizational Measures)

• •Data Classification: Are you ensuring that Protected Health Information (PHI) or Personally Identifiable Information (PII) is not used for model training without explicit consent?
• •Right to Erasure: How do you handle "unlearning" data that has been ingested into a vector database (RAG)?
• •Privacy by Design: Is the AI architecture air-gapped or restricted to prevent data leakage to external API providers?

SOC2 Trust Service Criteria (Security & Privacy)

• •Access Control: Are AI query interfaces protected by Multi-Factor Authentication (MFA) and Role-Based Access Control (RBAC)?
• •Audit Trails: Are all prompt/response pairs logged in an immutable, encrypted database for forensic review?

2. Technical Security Controls

A secure deployment requires moving beyond "black-box" systems to transparent, governed environments.

Vulnerability Management

• •Automated Secret Scanning: Use tools like Gitleaks or TruffleHog to ensure API keys, passwords, or database strings are not accidentally included in prompts or system instructions.
• •Dependency Audits: Scan the AI stack (LangChain, LlamaIndex, PyTorch) for known CVEs regularly.

Prompt Injection & Input Sanitization

• •OWASP Top 10 for LLM: Prioritize protection against "Prompt Injection" (LLM01) where users bypass system instructions to extract sensitive data.
• •Output Validation: Implement a "Guardian" layer that scans AI-generated code or text for malicious patterns before it reaches the end-user.

Network & Egress Filtering

• •Zero-Trust VPC: The AI inference server should never have direct outbound access to the internet.
• •Egress Proxies: Use transparent proxies to whitelist only the specific endpoints required for model updates or authorized external connectors.

3. Data Governance in RAG Systems

Retrieval-Augmented Generation (RAG) is the gold standard for enterprise AI, but it introduces new data leakage risks.

• •Source Attribution: Every AI response must cite its source (e.g., "According to Document X on Page Y").
• •Dynamic Access Sync: If a user does not have permission to read "HR_Payroll.pdf", the RAG system must automatically filter that document from the vector search results at query time.
• •Vector DB Encryption: Ensure that the vector embeddings (which are mathematical representations of your data) are encrypted at rest.

4. The Human-in-the-Loop (HITL) Requirement

Automated systems should never make high-stakes decisions without oversight.

• •Feedback Loops: Provide a mechanism for users to flag inaccurate or biased AI responses.
• •Manual Review: High-risk AI outputs (e.g., legal advice, medical summaries) should be periodically audited by human subject matter experts.

Conclusion

Compliance is not a checkbox—it is a continuous engineering discipline. By following this roadmap, organizations can leverage the power of LLMs while maintaining the trust of their customers and the approval of their regulators.

Inlock AI provides automated compliance scanning and secure infrastructure templates designed for these exact requirements. Contact our security team for a full audit of your current AI posture.