Securing the Integration of Automated Systems

The rapid adoption of Large Language Models (LLMs) and internal AI systems introduces novel attack vectors, including prompt injection, training data poisoning, and unauthorized proprietary data exfiltration.

AI Security Controls:

• Data Privacy Guardrails: Engineering structural boundaries to prevent sensitive corporate data (PII/PHI) from entering external LLM training loops.
• LLM Architecture Review: Auditing API integrations, plugin access controls, and RAG (Retrieval-Augmented Generation) infrastructure logic.
• Acceptable Use Policy (AUP): Drafting ironclad internal governance to dictate how employees can securely leverage AI tooling.