
LLMOps Security & Compliance
Description
As large language models (LLMs) scale across enterprises, the risks they introduce—from model leakage to adversarial prompt injection—demand a specialized discipline known as LLMOps Security. This report analyzes how organizations must integrate security frameworks across model training, deployment, and monitoring. It identifies the critical attack vectors unique to generative systems, including data exfiltration, hallucination manipulation, and dependency poisoning.
The study highlights best practices for Secure-by-Design AI, emphasizing cryptographic safeguards, output filtering, and traceable lineage logging. It further explores alignment with regulatory frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 23894:2023, positioning security as the core enabler of enterprise trust in GenAI.
Combines market and policy research from NIST, EU AI Act, and enterprise security surveys. Identifies key vendors providing secure LLM lifecycle management solutions and highlights early adoption by regulated sectors such as finance and healthcare.
Table of Contents
23 Pages
- 1. Executive Summary
- 2. LLM Threat Landscape Overview
- 3. Frameworks for LLM Security & Governance
- 4. Mitigating Model Leakage and Prompt Injection
- 5. Secure Deployment Architecture
- 6. Regulatory and Compliance Mapping
- 7. Strategic Recommendations
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