We take cybersecurity extremely seriously, and we implement several steps to ensure our platform is secure and user data is kept private.
Security in AI: Best Practices for Enterprise Implementation
AI security is critical for organizations adopting artificial intelligence solutions. This guide outlines the essential security considerations when implementing AI systems in your organization.
Table of contents
Why AI Security Matters
Three Key Security Components
Data Governance
AI Guardrails
Protection Against Training on Your Data
How We Secure Data at Hatz AI
Compliance and Certifications
Frequently Asked Questions
Why AI Security Matters
When implementing AI in your organization, security should be a foundational consideration rather than an afterthought. Without proper security measures, your organization risks:
Loss of intellectual property
Exposure of confidential information
Inconsistent AI outputs across your organization
Unintended training on your proprietary data
Three Key Security Components
1. Data Governance
What it is: Data governance ensures that information entered into AI systems remains within your organization's control and is properly managed.
Implementation steps:
Establish clear data ownership policies
Implement proper user permissions
Create secure storage protocols
Define appropriate data retention policies
Why it matters: Just as organizations maintain private business email and file storage systems to retain intellectual property when employees depart, AI systems require similar governance to ensure data remains within the enterprise ecosystem.
2. AI Guardrails
What it is: Guardrails are boundaries that standardize how AI is used within your organization, ensuring consistent outputs and appropriate usage.
Implementation steps:
Define approved AI use cases
Establish style and tone guidelines
Implement content filters for inappropriate outputs
Create user-specific permissions based on role
Why it matters: Without guardrails, employees may use unsanctioned AI systems that produce inconsistent results or incorporate inappropriate content from personal usage patterns.
3. Protection Against Training on Your Data
What it is: Measures to prevent your proprietary data from being used to train AI models that could later expose your information to competitors.
Implementation steps:
Select AI providers with clear data usage policies
Ensure data processing agreements prohibit training
Implement technical controls to separate inference from storage
Regularly audit data access and usage
Why it matters: Many "free" AI systems monetize by training on user inputs and outputs, potentially exposing sensitive organizational information that could later be reproduced when similar prompts are entered.
How We Secure Data at Hatz AI
At Hatz AI, we implement a comprehensive security architecture:
Segregated storage: All conversation histories, user settings, and organizational data reside in secured AWS data centers in Virginia, logically separated by tenant, organization, and user.
Inference isolation: Our architecture deliberately separates the inference layer from historical data storage, ensuring that large language models have no persistent access to previous interactions unless explicitly prompted by the user.
Multi-cloud approach: We host various AI models (including Anthropic, Llama, Mixtral, and Google models) within controlled environments, while interactions with external APIs like OpenAI are governed by agreements specifying near-zero retention and no training.
Access controls: Information remains accessible only to authorized individuals and entities based on your organizational settings.
Compliance and Certifications
Security is validated through industry-standard certifications:
SOC 2 Type I and Type II, and SOC3 compliance
Regular penetration testing: Independent security firms conduct regular testing of our systems to identify and remediate potential vulnerabilities.
External security reviews: Our development processes incorporate security reviews at every stage, with external cybersecurity consultants regularly evaluating both our code and operational practices.
Frequently Asked Questions
Q: How can I get a copy of your SOC 2 & 3 reports?
A: Security-focused organizations can obtain our SOC 2 & 3 reports by contacting [email protected]. We'll provide a mutual non-disclosure agreement and share the report.
You can also visit trust.hatz.ai to request a copy.
Q: Can I speak with someone on your security team?
A: Yes, we can facilitate meetings with our cybersecurity team. Contact us to arrange a discussion about our security practices.
Q: Where is my data stored?
A: All data is stored in AWS data centers in Virginia, with logical separation between organizations and users.
Q: Do you train AI models on my data?
A: No. We have a strict no-training policy on customer data. Your inputs an