At Hacker Sidekick, we understand that every prompt, completion, and embedding can reveal critical details about your assets, attack paths, and investigative logic. In cybersecurity operations, data privacy isn't just a preference—it's a fundamental requirement that can determine the success or failure of your security initiatives.
That's why we've architected our entire LLM pipeline around private, dedicated LLM instances—fully isolated from public endpoints and other tenants—to guarantee end-to-end data separation and compliance for every interaction.
The Critical Need for Data Isolation in Cybersecurity AI
Traditional AI platforms often operate on shared infrastructure where your sensitive security data mingles with other users' information. For cybersecurity professionals, this creates unacceptable risks:
- Attack Path Exposure: Your penetration testing methodologies and discovered vulnerabilities could inadvertently inform other users
- Asset Intelligence Leakage: Network topologies, system configurations, and security controls become visible to model providers
- Investigative Logic Compromise: Your incident response procedures and threat hunting techniques could be reverse-engineered
- Compliance Violations: Shared infrastructure may violate industry regulations and security frameworks
Hacker Sidekick's Private LLM Architecture
Our private deployment model ensures that your cybersecurity operations remain completely isolated. Here's how we achieve this:
Dedicated Infrastructure
Each customer receives their own dedicated LLM instances running on isolated infrastructure. Your models never share compute resources, memory, or storage with other tenants.
Zero Cross-Contamination
Unlike shared AI services, your security data never enters a common pool. Each interaction is processed entirely within your dedicated environment.
End-to-End Encryption
All data in transit and at rest is encrypted using enterprise-grade cryptography, with keys managed independently for each deployment.
Our Ironclad Data Privacy Commitments
For every Hacker Sidekick customer, your prompts (inputs), completions (outputs), and embeddings:
- Are NOT accessible by any other customer
- Are NOT accessible to LLM model providers
- Are NOT used to improve public models
- Are NOT used to improve Hacker Sidekick's models
- Are NOT used to train, retrain, or fine-tune foundational models
- Are NOT leveraged to improve any third-party products or services
Transparency in Future Model Training
We believe in complete transparency about our data practices. While we maintain strict data separation for all current customers, we want you to understand our potential future approach:
We may, in the future, utilize inputs, outputs, and embeddings from our free users only to train our models on a go-forward basis. However:
- Your historical data remains yours: Any past interactions will never be used for training
- Explicit consent required: We will always ask for your permission or express consent before using any data for model improvements
- Paid customers protected: Professional and Enterprise customers maintain complete data isolation regardless
Bottom Line: Your sensitive cybersecurity data will never be used to improve models or services without your explicit knowledge and consent.
Real-World Benefits for Security Teams
Confidential Vulnerability Research
Research zero-day vulnerabilities and develop proof-of-concept exploits with confidence that your discoveries remain completely private.
Sensitive Incident Response
Analyze breach indicators, malware samples, and attack vectors without risking exposure of your investigation methodologies.
Proprietary Security Tools
Develop custom security automation and detection logic without revealing your defensive capabilities to potential adversaries.
Regulatory Compliance
Meet strict data residency and privacy requirements for financial services, healthcare, and government sectors.
Getting Started with Private Deployments
Private LLM deployments are available for our Professional and Enterprise customers. Our team works with you to:
- Assess your specific privacy and compliance requirements
- Design a deployment architecture that meets your security standards
- Implement dedicated infrastructure in your preferred cloud region
- Provide ongoing monitoring and support for your private environment
Ready to secure your cybersecurity AI operations? Contact our team to discuss your private LLM deployment requirements and see how Hacker Sidekick can transform your security operations while maintaining complete data privacy.
With Hacker Sidekick's private LLM deployments, you can integrate AI-powered tools into your security workflows with complete confidence—knowing your data stays yours alone, your methodologies remain confidential, and your compliance requirements are fully met.
Your security is our security. Your privacy is our priority.