With the increasing prevalence of agentic AI, the demand for robust safety measures is more pressing than ever.
Recently, Nvidia unveiled significant enhancements to its NeMo Guardrails technology aimed at meeting the evolving requirements of agentic AI. The core concept of these guardrails is to instate a framework for policies and controls that govern large language models (LLMs), effectively safeguarding against unauthorized and unintended outputs. This guardrail approach has gained traction among numerous industry players, notably including AWS.
Nvidia’s latest updates to NeMo Guardrails simplify deployment for organizations while offering more nuanced control options. Now available as Nvidia Inference Microservices (NIM), these guardrails are optimized specifically for use with Nvidia’s GPUs. Moreover, enterprises can take advantage of three additional NIM services focusing on content safety, topic management, and detection of bypass attempts. These advancements target agentic AI applications rather than solely individual LLMs.
Kari Briski, Vice President of Enterprise AI Models at Nvidia, elaborated during a press briefing: “Our focus has shifted from mere model safeguards to encompassing the entire system.”
The adoption of agentic AI is poised to be a major trend heading into 2025.
Despite its numerous advantages, integrating agentic AI introduces new challenges related to security protocols, data privacy standards, and governance issues—all potential hurdles that could hinder implementation efforts.
The introduction of three new Network Interface Modules (NIMs) under NeMo Guardrails aims to tackle some prevalent issues:
- Content Safety NIM: Drawing from an extensive dataset featuring 35,000 meticulously annotated samples from Nvidia’s Aegis program designed explicitly to filter out hazardous or unethical content.
- Topic Control NIM: Ensures that all interactions stay within designated topical realms thereby curbing undesired information leakage or distractions during conversations.
- Jailbreak Detection NIM: This component thwarts clever evasion methods by monitoring training insights derived from 17,000 previously identified jailbreak instances.
The task of securing systems involving agentic AIs presents considerable complexity due to their multi-agent configurations and interconnected LLMs.
An illustrative scenario provided by Briski involves a retail customer service situation where users engage concurrently with several agents—a reasoning-based LLM alongside both retrieval-augmented generation (RAG) functionalities and a dedicated customer support assistant—all necessary for efficient assistance delivery.
“Given varying user interactions across multiple agents or models,” noted Briski. “Every distinct interaction necessitates specific guardrail implementations.” While she acknowledged this complexity as daunting, she also emphasized that one objective behind introducing NeMo Guardrail NIMs is simplifying this process for businesses. As part of this launch initiative today—blueprints demonstrating varied deployment scenarios across different domains like retail customer service have been made available.”
A paramount concern facing enterprises when implementing agentic AIs revolves around performance metrics.
Briski pointed out potential latency issues when embedding guard rails within systems.
“Initial attempts at implementing guards would often involve utilizing larger-scale LLM architectures,” she clarified.
Currently optimized versions of the newly updated NeMo Guardrail
NIMs aim at addressing these latency considerations; early tests indicate businesses can achieve up
to 50% increased protective measures without substantial delays—instead only adding about half a second
to response times.
“The importance lies not only on single-agent environments but how various agents function collectively
in an overarching system,” emphasized Briski.
For organizations interested in deploying Nvidia’s advanced NeMo Guardrail NIM solutions tailored specifically
for agential AIs—the offerings are currently accessible through an enterprise license priced at $4,
500 annually per GPU—developers can also explore limited trials under open-source licensing conditions via build.nvidia.com.
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