LLM Security

Large Language Model (LLM) security is the set of processes, controls, and architectures that manage security risks related to deploying and operating large language models in enterprise environments, including model behavior, data protection, and interaction with other systems.

Expanded Explanation

1. Technical Function and Core Characteristics

LLM security focuses on protecting LLM pipelines, including data ingestion, training, fine-tuning, inference, and output delivery. It addresses risks such as prompt injection, data leakage, model abuse, and unsafe outputs. It also covers security of the underlying model artifacts, APIs, and runtime environments.

Practices include input and output filtering, content moderation, policy enforcement, rate limiting, access control, and monitoring of model interactions. Research and guidance from security and standards organizations describe LLM-specific threat models and evaluation methods for adversarial prompts, jailbreaks, data extraction, and model misuse.

2. Enterprise Usage and Architectural Context

In enterprise architectures, LLM security integrates with application, Application Programming Interface (API), data, and identity security controls. It typically spans model gateways, Retrieval Augmented Generation (RAG) systems, orchestration frameworks, and connections to internal and external data sources. It also interacts with software supply chain security for LLM components and dependencies.

Enterprises apply LLM security within secure development lifecycles, threat modeling, and security testing for AI-enabled applications. Security teams align LLM security controls with existing frameworks for risk management, access governance, incident response, and logging to maintain traceability and compliance.

3. Related or Adjacent Technologies

LLM security relates to Artificial Intelligence (AI) security, Machine Learning (ML) security, and traditional application and API security. It connects with data security, Data Loss Prevention (DLP), privacy engineering, and identity and access management. It also intersects with model governance, evaluation, and assurance practices.

Standards and guidance for trustworthy and secure AI from governmental and standards bodies reference controls that apply to LLMs, including robustness, transparency, and secure deployment. LLM security often uses techniques from adversarial ML, secure coding, and content classification.

4. Business and Operational Significance

Enterprises use LLM security to reduce risks of data exfiltration, policy violations, and harmful content in AI-assisted workflows. It supports compliance with regulatory requirements on privacy, data protection, and safety when LLMs interact with sensitive information or regulated processes.

Operationally, LLM security enables controlled use of general-purpose and domain-specific models across business units. It provides a basis for monitoring, auditing, and enforcing organizational policies around AI usage, which supports governance, risk management, and assurance activities.