AI Security in Production: Threats, Controls and Architecture

Enterprise AI security is a new discipline that sits at the intersection of traditional cybersecurity and AI systems engineering. The threat landscape is fundamentally different — and requires fundamentally different defences.

The AI Threat Landscape

• Prompt injection — manipulating LLM behaviour through crafted inputs
• Jailbreaking — bypassing safety controls and guardrails
• Data poisoning — corrupting training or retrieval data to influence outputs
• Model theft — extracting model weights or behaviour through API probing
• Membership inference — determining whether specific data was used in training
• Adversarial examples — crafting inputs that cause misclassification
• Indirect prompt injection — injecting instructions through retrieved documents (RAG systems)

Security Architecture for LLM Systems

Securing an LLM deployment requires defence in depth: input validation and sanitisation, system prompt hardening, output filtering, rate limiting, user authentication, access-controlled retrieval, audit logging, and real-time anomaly detection.

RAG System Security

RAG systems introduce a unique attack vector: indirect prompt injection through poisoned documents in the retrieval corpus. Every document ingested into a production RAG system should be treated as untrusted input and scanned for injection attempts before indexing.

AI Red-Teaming

Before deploying any production AI system, it should undergo structured red-teaming: adversarial testing by a team tasked with finding security vulnerabilities, safety failures and misuse vectors. This is now a regulatory expectation under the EU AI Act for high-risk systems.

Compliance and Regulatory Alignment

AI security controls must align with applicable regulations: GDPR for data privacy, the EU AI Act for high-risk system requirements, SOC 2 for enterprise security assurance, sector-specific regulations (FCA, HIPAA, CBN) and emerging AI-specific standards from NIST (AI RMF) and ISO/IEC 42001.