Securing Private LLM and RAG Systems: Boundaries Before Prompts
A security-first design checklist for private LLM applications, retrieval pipelines, tool access, sensitive data, and AI evaluation.
Cybersecurity · secure AI · practical engineering
Build secure systems. Put AI to work with confidence.
Security-first engineering for software, infrastructure, and AI.
Frazik helps Slovak companies reduce software and infrastructure risk, strengthen delivery practices, and adopt private, useful AI with security built in.
The person behind Frazik
Security is part of good engineering—not a final checkbox.
Frazik is an independent security and AI engineering practice. I work with teams to find practical improvements, make secure delivery repeatable, and deploy AI in ways that respect their data and risk.
What I can help with
Security-first services for modern teams
01
Make security a dependable part of how you build and operate.
Practical security engineering for software teams: identify meaningful risks, strengthen development and deployment workflows, and build safeguards around AI systems and sensitive data.
Discuss a security review →02
Local LLM hardware that stays under your control.
Custom, hardened workstations and servers for teams that need capable AI while keeping sensitive information in a controlled environment.
Discuss your setup →03
Turn repetitive work into a system your team can trust.
A practical path from scattered AI experiments to secure assistants, connected workflows, and measurable time saved—with governance and human oversight considered from the start.
Plan an automation pilot →A focused process
From understanding risk to lasting improvement.
01
Understand
We clarify your systems, threat exposure, delivery practices, data, and business priorities.
02
Prioritize
We agree on proportionate controls and a focused plan—security improvement without needless friction.
03
Improve
We implement, document, and verify changes, with a clear path for your team to maintain them.
Writing
Practical notes on cybersecurity, DevSecOps, and responsible AI.
A Practical DevSecOps Pipeline: Automation, AI, and Human Judgment
A risk-based guide to integrating security checks into CI/CD, using AI carefully, and making findings actionable for engineering teams.
The Complexity We Choose
AI can help us write more software, but our real opportunity is to make software simpler to understand, verify, and own.
Want to make your systems more resilient?
Start with a conversation about your software, infrastructure, security priorities, or AI plans.
Start a conversation