AIStorm

Product Introduction

AIStorm Large AI Model Application Firewall (AISMAF) by AIStorm is a one-stop, end-to-end solution designed to secure large language model (LLM) and Agents across every interaction. Built to address the unique security and compliance challenges of modern AI deployments, AISMAF establishes a robust protective layer between users and AI models, ensuring trust, reliability, and regulatory adherence.

At its core, AISMAF delivers Input/Output Semantic Security, Comprehensive Attack Protection, and Enhanced Security & Compliance—three foundational pillars that safeguard AI systems from emerging threats while enabling safe, productive AI adoption.

Core Security Pillars

Input/Output Semantic Security

AISMAF employs multi-modal detection across text, image, audio, and video to analyze and secure both user inputs and model outputs. Pretrained security models enable "Model-as-a-Model" protection, achieving over 95% accuracy in identifying factual inconsistencies, harmful content, and privacy leaks—ensuring AI-generated content remains trustworthy and secure.
Comprehensive Attack Protection

The solution defends against a wide spectrum of adversarial threats, including backdoor, poisoning, gradient, and manipulation attacks. It provides full coverage against the OWASP LLM Top 10 vulnerabilities, with a success rate exceeding 95% in mitigating sophisticated, real-world exploits.
Enhanced Security & Compliance

AISMAF enforces data privacy and regulatory compliance through fine-grained access control and proactive data leakage prevention. It aligns with key global AI security and compliance standards, including GDPR, EU AI Act, NIST AI Risk Management Framework (AI RMF), and ISO/IEC 24089, helping organizations meet stringent regulatory requirements seamlessly.

Product Advantages

AISMAF stands out in the market with a unique combination of efficiency, performance, and comprehensive protection, making it ideal for high-scale, mission-critical AI deployments.

Ultra-Lightweight Efficiency

Optimized to run on 8GB GPUs, consuming just 1/10 of the resources required by competing solutions. This enables cost-effective deployment without sacrificing security performance, making it accessible for a wide range of infrastructure setups.
High Vulnerability Detection Coverage

Assesses and protects against over 2000 system vulnerabilities, providing extensive coverage against known and emerging risks. It also covers more than 60 evaluation criteria, including misinformation, violence, bias, and other harmful content categories.
Advanced Attack Detection

Detects over 30 types of adversarial attacks, including jailbreaking, prompt injection, and automated attacks, effectively neutralizing attempts to manipulate or compromise AI models.
Full Multi-Modal Protection

Delivers native security across four core modalities - text, image, audio, and video - ensuring comprehensive protection for modern, multi-sensory AI applications.
Ultra-Low Latency Performance

Maintains detection latency of less than 200ms, ensuring minimal delay in AI interactions. This preserves a seamless user experience while enforcing real-time security checks, critical for customer-facing and high-throughput applications.

Key Features

AISMAF’s capabilities are built on three foundational pillars—Attack Samples, Model Characteristics, and Algorithm Optimization—ensuring it remains powerful and effective against evolving threats.

Attack Samples & Threat Intelligence

Diverse Attack Library: Maintains a vast and continuously updated library of attack samples, providing a rich dataset for training and testing defensive mechanisms.
Red Team Collaboration: Works closely with red teams to simulate real-world adversarial scenarios, ensuring defenses are rigorously validated against practical attack vectors.
Adversarial Technique Summarization: Has summarized and categorized over 30 adversarial techniques, forming the basis for proactive "Model-vs-Model" defensive strategies.
Foundational Defense Framework: Serves as the foundation for advanced "Model-vs-Model" defense, where AI security models directly counteract adversarial AI techniques.

Specialized Model Characteristics

Deep Semantic Understanding: Leverages a specialized security model with deep semantic comprehension to analyze context and intent, enabling accurate threat detection.
Bidirectional Context Analysis: Processes and understands bidirectional context in conversations, ensuring nuanced detection of subtle or context-dependent threats.
Advanced Model Architecture: Utilizes a pre-training and fine-tuning pipeline with multi-head attention mechanisms, enabling robust generalization across diverse attack scenarios.
Sophisticated Attack Identification: Identifies a wide range of complex, multi-stage attacks that evade simpler rule-based or pattern-matching systems.

Optimized Algorithm Performance

Exceptional Accuracy: Delivers an average detection accuracy of over 95%, with critical threats identified at a rate exceeding 98%, minimizing missed threats.
Low False Positive Rate: Maintains a false positive rate of less than 2%, reducing operational overhead and avoiding unnecessary disruptions to legitimate AI interactions.
High Throughput Performance: Achieves over 50 concurrent requests per second on an 8GB GPU, supporting high-scale, enterprise-grade AI deployments without performance bottlenecks.