Credal AI – Enterprise LLM Security and Governance

Overview

Credal AI is an LLM security gateway and governance platform established in 2023. It is designed for enterprises seeking to manage the security and compliance aspects of their large language model deployments. The platform focuses on intercepting and processing requests and responses to and from LLMs, allowing organizations to enforce policies related to data privacy, security, and usage. Its primary applications include securing LLM applications, redacting Personally Identifiable Information (PII), ensuring data governance, and providing auditability for enterprise AI interactions.

The platform operates as a proxy, sitting between an organization's applications and various LLM providers. This architectural approach enables Credal AI to inspect and modify data in transit, applying rules for PII detection and redaction, content moderation, and access control. This is particularly relevant for sectors with strict regulatory requirements, such as finance and healthcare, where sensitive data must be protected even when interacting with external AI services. For example, the need for robust data governance in AI systems is highlighted by organizations like McKinsey, which emphasize the importance of responsible AI practices to mitigate risks and ensure ethical deployment in their assessments of AI trends.

Credal AI's offerings include an LLM security gateway, PII redaction capabilities, data governance tools, and audit logs. The security gateway acts as a central control point, allowing administrators to define and enforce security policies across all LLM interactions. PII redaction automatically identifies and masks sensitive information in prompts and responses, preventing its exposure to LLMs or storage in logs. The data governance features provide mechanisms for controlling data flow, ensuring data residency requirements are met, and preventing unauthorized data use. Comprehensive audit logs record all LLM interactions and policy enforcement actions, providing a transparent record for compliance and incident response. This focus on verifiable security measures is a common thread among LLM security solutions, including alternatives like Lakera, which also emphasizes threat detection and response in AI systems on its platform.

The platform is suitable for developers and technical buyers who need to integrate AI security into existing enterprise infrastructure. Its API-first design aims to facilitate integration with various applications and LLM providers. Credal AI's capabilities are relevant for organizations deploying LLMs for internal use cases, such as customer service automation, content generation, or data analysis, where controlling data exposure and maintaining compliance are critical.

Key features

• LLM Security Gateway: Acts as a proxy to intercept and enforce security policies on all requests and responses to and from large language models.
• PII Redaction: Automatically detects and redacts Personally Identifiable Information (PII) from prompts and responses to prevent data leakage and ensure privacy.
• Data Governance: Provides controls for data residency, access management, and usage policies to ensure compliance with organizational and regulatory requirements.
• Audit Logs: Maintains detailed, immutable logs of all LLM interactions, policy enforcement actions, and data transformations for compliance, debugging, and incident response.
• Content Moderation: Enables filtering of harmful, inappropriate, or non-compliant content in both inputs and outputs of LLM interactions.
• Threat Detection: Identifies and mitigates common LLM-specific threats such as prompt injection, data exfiltration attempts, and denial-of-service attacks.
• Policy Enforcement: Allows administrators to define granular security and data handling policies that are automatically applied to all LLM traffic.

Pricing

Credal AI offers a tiered pricing structure, including a free developer plan and paid options for professional and enterprise use cases. Pricing details are current as of May 2026.

Common integrations

• OpenAI: Proxy requests to OpenAI's API endpoints for models like GPT-4, applying security and governance policies (Credal AI OpenAI Integration).
• Anthropic: Integrate with Anthropic's Claude models, securing prompts and responses with PII redaction and content moderation (Credal AI Anthropic Integration).
• Google Cloud AI: Connect with Google's Vertex AI and other Google LLM services for centralized security management (Credal AI Google Cloud AI Integration).
• Azure OpenAI Service: Secure deployments using Azure OpenAI, ensuring compliance and data protection within the Azure ecosystem (Credal AI Azure OpenAI Integration).
• Custom LLMs: Support for integrating and securing custom or self-hosted large language models via API proxying (Credal AI Custom LLM Integration).

Alternatives

• Lakera: Offers an AI safety platform focused on detecting and preventing risks like prompt injection and data leakage.
• Protect AI: Provides a suite of tools for securing the entire ML lifecycle, including vulnerability management and threat detection.
• Gretel.ai: Specializes in synthetic data generation and data anonymization, offering tools for privacy-preserving AI development.

Getting started

To get started with Credal AI, you typically configure your application to send LLM requests through the Credal AI gateway. This example demonstrates how to proxy a request to an OpenAI model using a Python client, assuming you have set up your Credal AI API key and endpoint.

import openai

# Configure the OpenAI client to use Credal AI as a proxy
# Replace with your Credal AI endpoint and API key
openai.api_base = "https://api.credal.ai/v1"
openai.api_key = "YOUR_CRED_AI_API_KEY"

# Set the actual LLM provider's API key as a custom header
# Credal AI will use this to forward the request to OpenAI
headers = {
    "X-Credal-LLM-Key": "YOUR_OPENAI_API_KEY"
}

def chat_with_llm(prompt):
    try:
        response = openai.chat.completions.create(
            model="gpt-3.5-turbo", # Or any other model supported by your Credal AI configuration
            messages=[
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": prompt}
            ],
            extra_headers=headers
        )
        return response.choices[0].message.content
    except openai.APIError as e:
        print(f"OpenAI API Error: {e}")
        return None

# Example usage
user_prompt = "Tell me about the capital of France."
llm_response = chat_with_llm(user_prompt)

if llm_response:
    print(f"LLM Response: {llm_response}")

This Python code snippet illustrates how to configure the OpenAI client to route requests through the Credal AI gateway. The openai.api_base is set to the Credal AI endpoint, and the actual OpenAI API key is passed via the X-Credal-LLM-Key custom header, allowing Credal AI to securely forward the request after applying any defined policies. More detailed setup instructions and examples for various LLM providers are available in the Credal AI documentation.