Practical Guides
Step-by-step guides for implementing privacy-first infrastructure, securing AI usage, and building zero-knowledge systems. Actionable intelligence you can deploy today.
Theory without practice is academic. The guides below translate the principles covered across our intelligence verticals into concrete, step-by-step implementation playbooks. Each guide is designed to be executed — not just read.
Whether you are an individual protecting your AI conversations from training data ingestion, an engineer implementing zero-trust architecture for your team, or a CISO auditing your organization’s AI provider stack, these guides provide the specific actions, tool recommendations, and architectural patterns required to move from awareness to implementation.
For individuals and teams using AI tools: Start with Use AI Without Tracking to lock down your day-to-day AI usage, then work through Protect Your Data from AI Training to verify opt-out status across every provider you use.
For engineers and architects: Implement Zero Trust covers network and identity architecture from first principles. Encrypt Cloud Storage walks through client-side encryption for data at rest. For messaging and collaboration, Build a Zero-Knowledge Communication Stack provides a full implementation blueprint.
For security leaders and procurement: Audit Your AI Provider’s Privacy gives you a structured evaluation framework for every LLM vendor in your stack. Choose a Privacy Cloud Provider translates jurisdiction, encryption, and key management criteria into a provider selection process. And Build a Threat Model ensures every decision is grounded in your actual risk profile.
Every recommendation is grounded in our primary research across AI privacy, cloud infrastructure, cryptography, and decentralized identity. No affiliate links. No sponsored recommendations. Just the technical steps required to build systems that respect privacy by design.
How to Use AI Without Being Tracked
A practical, step-by-step guide to using ChatGPT, Claude, Gemini, and other AI systems while minimizing data exposure. Covers API vs consumer tiers, opt-out settings, network-level protections, local models, and privacy-first alternatives.
How to Set Up a Zero-Knowledge Communication Stack
A complete, layer-by-layer guide to building a private communication stack using Signal, ProtonMail, SimpleLogin, Mullvad VPN, and privacy-focused DNS. Designed for professionals who treat privacy as infrastructure.
How to Protect Your Data from AI Training
Practical steps to prevent your content, code, and conversations from being ingested into AI training datasets. Covers opt-out mechanisms, robots.txt limitations, data poisoning, contractual protections, and technical countermeasures.
How to Implement Zero-Trust Architecture for Your Team
A practical implementation guide for deploying zero-trust architecture in small-to-medium teams. Covers identity verification, microsegmentation, least-privilege access, continuous verification, and tool recommendations for teams without a dedicated security operations center.
How to Encrypt Your Cloud Storage End-to-End
A step-by-step guide to encrypting files before they reach any cloud provider. Covers Cryptomator, VeraCrypt, rclone crypt, and client-side encryption architectures that ensure zero-knowledge file storage on any platform.
How to Choose a Privacy-First Cloud Provider
A decision framework for selecting cloud infrastructure based on privacy requirements. Covers jurisdiction, encryption defaults, compliance certifications, data residency, egress policies, and includes a comparison matrix for evaluating providers.
How to Build a Personal Threat Model
A structured framework for individuals and small teams to assess their privacy and security risks. Covers adversary identification, asset mapping, attack surface analysis, risk prioritization, and includes a practical worksheet for building your own threat model.
How to Audit Your AI Provider's Privacy Practices
A structured methodology for evaluating AI providers on privacy. Covers data retention policies, training data usage, API vs consumer tier differences, DPA review, subprocessor analysis, and includes a weighted scoring rubric for systematic comparison.