Ensure your LLM applications stay safe, compliant, and performant in production. Read the GAF paper here.
NeuralTrust secures your Generative AI with a real-time defense layer, protecting against misuse, leakage, and abuse.
GAF protects every layer of your generative AI stack, from prompt inputs to network traffic, detecting bots, multi-turn attacks, and behavioral threats in real time.
It achieves industry-best detection accuracy with <10ms latency (GPU) and scales linearly to handle 20,000+ requests per second on commodity hardware.
Designed for flexibility, GAF supports deep customization, plugin-based extensions, and is available as open source to reduce vendor lock-in.
GAF works across all major LLM providers, supports cloud, on-prem, or hybrid deployments, and integrates easily with SIEMs, auth systems, and enterprise infrastructure.
NeuralTrust is a high-performance security layer that outperforms all alternatives on the market in both execution speed and detection accuracy.
Protect your LLM applications from jailbreaks, obfuscations, and malicious inputs with the most advanced guardrail engine on the market.
Monitor usage patterns in real time to identify abnormal, risky, or compromised interactions before they escalate into security incidents.
Detect and block automated traffic, scrapers, and synthetic users before they drain your tokens, hijack your data, or poison your results.
Automatically detect and redact PII, credentials, and financial information in LLM prompts and responses before it reaches the wrong hands.
Create and enforce moderation policies so you can automatically route flagged content, apply tailored remediations, and oversee reviews in line with your workflows.
Centrally access hundreds of AI models with robust control and deploy faster, with less friction.
It detects prompt injections, context hijacking, obfuscated payloads, tool misuse, toxic output, and real-time behavior anomalies — across prompt and response.
Yes. NeuralTrust includes volumetric controls that block abuse like prompt floods or DoS attacks, and rate-limits based on IP, fingerprint, or session behavior.
Yes. We support streaming protocols like WebSocket and HTTP Stream, applying protections even during token-by-token generation.
Highly. You can define policies by app, user group, or model; customize masking actions; and extend with plugins for unique use cases.
Do not leave vulnerabilites uncovered, make sure your LLMs are secure and reliable
