AuraScape

AuraScape is a specialized security and governance layer for LLM applications that filters prompts and responses to prevent data leaks and jailbreaks—best for enterprises moving AI from prototype to production.

Excellent for real-time PII masking and prompt injection defense, weaker for teams looking for general-purpose application security.

Ai Governance Security Llm Compliance Real-Time Enterprise Api Data Analysis

Analysis based on product data, pricing structure, traffic signals, and public user sentiment.

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Who Should Use AuraScape?

Typical users

Security engineers and product teams at mid-to-large enterprises deploying customer-facing or internal LLM applications.

Maturity fit

scaling to advanced

Choose this if…

Your priority is preventing PII from reaching third-party LLM providers
You need to block prompt injection and jailbreak attempts in real-time
Your industry requires strict audit logs and compliance reporting for AI interactions

Skip this if…

You are only using standard SaaS AI tools without building custom integrations
You need a general-purpose web application firewall (WAF) rather than an AI-specific one
Your AI usage is low-volume and doesn't handle sensitive data

About AuraScape

AuraScape provides an AI-native security platform designed to protect generative AI applications. It functions as a gateway between users and Large Language Models (LLMs) to ensure safety, compliance, and data privacy.

Official profiles

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What it actually does

The tool acts as an 'AI Firewall' that inspects every prompt sent to an LLM and every response received. It automatically redacts sensitive information, blocks malicious instructions, and monitors for hallucinations or toxic content.

What makes it different

Unlike traditional security tools that focus on network traffic, AuraScape is built specifically for the non-deterministic nature of LLMs. It prioritizes low-latency inspection to ensure security checks don't degrade the user experience of the AI application.

Real-time prompt injection detection Automated PII and PHI masking Jailbreak and bypass prevention Toxicity and bias filtering Hallucination detection and scoring Centralized security policy management Compliance-ready audit logging

Key Features

LLM Firewall

Blocks malicious prompts before they reach the model to prevent unauthorized data access.

PII Redaction

Automatically identifies and masks sensitive data like emails, SSNs, and credit card numbers in real-time.

Jailbreak Detection

Identifies sophisticated attempts to trick the LLM into ignoring its safety guidelines.

Custom Policy Engine

Allows security teams to define specific rules for what the AI is allowed to discuss or output.

Model Agnostic Integration

Works across various LLM providers including OpenAI, Anthropic, and self-hosted models.

Security Dashboard

Provides a centralized view of all blocked threats and sensitive data exposure attempts.

Pricing

Popular

Enterprise

Custom monthly

Full LLM Firewall access
Real-time PII masking
Advanced jailbreak detection
Custom policy management
Dedicated support and SLA

Pricing checked 4 months ago

Pricing guidance

Best plan for most users: The Enterprise plan is currently the primary offering, tailored for organizations with significant AI traffic.

Free plan enough? No — there is no publicly listed free tier; the tool is positioned for professional and enterprise use cases.

Upgrade when:

When moving an AI feature from beta to public production
When handling regulated data (PII, PHI, PCI)
When internal security audits flag LLM vulnerabilities

Watch out for:

Latency may vary based on the complexity of the policy rules applied
Integration is typically via API/SDK, requiring code changes

Premium enterprise positioning focused on high-stakes security and compliance.

Pros & Cons

Strengths

Granular PII control
The ability to mask sensitive data before it leaves your infrastructure is critical for companies using third-party models like GPT-4 while maintaining HIPAA or GDPR compliance.
Low-latency performance
Designed to minimize the 'security tax' on AI response times, which is vital for maintaining a responsive chat interface.
Proactive threat defense
Goes beyond simple keyword blocking to understand the intent behind prompts, catching complex jailbreak attempts that standard filters miss.

Weaknesses

Integration overhead
Requires developers to route LLM traffic through their gateway, which adds a layer of architectural complexity during initial setup.

Affects: DevOps and Backend Engineers
Niche focus
It does not replace traditional security tools; it is a specialized add-on specifically for AI, meaning another vendor to manage in the security stack.

Affects: Security Procurement Teams
Opaque pricing
Lack of public pricing tiers makes it difficult for smaller teams to evaluate the cost-to-benefit ratio without a sales call.

Affects: Startups and solo developers

Real User Sentiment

Generally positive among security professionals who recognize the gap in traditional WAFs for LLM-specific threats.

Users tend to like

Ease of setting up PII redaction
Comprehensive dashboard for monitoring AI threats
Support for multiple LLM providers

Users commonly complain about

Lack of transparent self-serve pricing
The need for more documentation on complex custom policies

Recurring tradeoffs

Users trade a small amount of latency for a significant increase in data security and compliance.

Happiest users

CISO and Security Leads at fintech or healthcare companies deploying generative AI.

Often frustrated

Developers looking for a quick, free, or open-source library for basic prompt filtering.

Use Cases

Customer Support Bots

Preventing users from tricking the bot into giving unauthorized discounts or revealing internal data.

Healthcare AI Assistants

Masking patient health information (PHI) before it is processed by a cloud-based LLM.

Internal Knowledge Bases

Ensuring employees don't accidentally upload proprietary code or trade secrets to public AI models.

Financial Services

Monitoring for toxic or non-compliant financial advice generated by an AI agent.

Compliance Auditing

Generating reports to prove to regulators that AI interactions are being monitored and filtered.

Frequently Asked Questions

How much does AuraScape cost?

AuraScape does not publish fixed pricing. It is an enterprise-grade tool where costs are typically based on the volume of AI traffic (tokens or requests) and the specific security modules required. You must contact their sales team for a quote.

How does AuraScape compare to Lakera or Protect AI?

AuraScape competes directly with Lakera and Protect AI. While Lakera is well-known for its 'Gandalf' jailbreak game and robust database of threats, AuraScape focuses heavily on the governance and PII masking aspects for enterprise compliance workflows.

Does it work with on-premise LLMs?

Yes, AuraScape is designed to be model-agnostic and can be integrated with both cloud-based APIs (like OpenAI) and locally hosted models (like Llama 3) via their gateway architecture.

Will it slow down my AI's response time?

Any security layer adds some latency. However, AuraScape claims to use optimized inspection engines that keep this delay to a minimum, usually within a few milliseconds, which is often imperceptible in a streaming chat response.

Can it stop all hallucinations?

No tool can stop 100% of hallucinations. AuraScape provides 'hallucination detection' which scores the likelihood of a response being factually incorrect, allowing you to flag or block highly uncertain answers.

What integrations are supported?

It supports major LLM providers including OpenAI, Azure AI, Anthropic, and Google Vertex AI. It can be integrated into your application stack via a REST API or specific SDKs.

Why trust this page?

This evaluation combines product positioning, pricing analysis, traffic and market signals, and public user sentiment into a single decision-support page. Content is generated editorially — not copied from the vendor's website.

Funding & Company

Founded

2024

Stage

Series a

Total Raised

$62.8M

Latest Round

Series A (Apr 2025)

Notable Investors

Mayfield Fund Menlo Ventures Celesta Capital Mark McLaughlin

AuraScape has raised a total of $62.8 million over two rounds, including a significant $50 million Series A in April 2025. This substantial early-stage funding from notable investors like Mayfield Fund and Menlo Ventures indicates strong confidence in their approach to AI security and provides a solid financial runway for product development and market expansion.

Full funding report high confidence

Market Signals & Traffic

Estimated visits, global rank, geography, traffic sources, monthly visit trends, and organic search keywords (Similarweb)—on a dedicated page built for depth and search.

Estimated visits: 0
Global rank: —
Snapshot: May 2026
Traffic trend: Surging