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Security operations that do not depend on one overworked admin

Small and mid-sized organizations run servers, cloud accounts, databases, and remote desktops, yet security usually falls to whoever is on call. Alerts go unread, patches wait, and incidents are found weeks late. We build the system that watches, and then we run it.

Services

Security system build-out

  • Log collection pipeline for servers, network devices, and cloud accounts
  • Detection rules tuned to your environment
  • Hardened baselines: SSH keys only, multi-factor sign-in, firewall policy
  • Backup design that survives deletion and ransomware

Managed operation

  • AI agents triage alerts around the clock
  • Known attack patterns blocked automatically, such as repeated sign-in attempts
  • Uncertain cases escalated to an engineer with the evidence attached
  • A monthly report of what happened and what we changed

Technical support and incident response

  • Investigation of suspicious activity and outages
  • Recovery from snapshots and backups
  • Root-cause analysis written up so it does not happen twice

How an engagement runs

  1. Assess

    We inventory your systems, read existing logs, and list the gaps that matter most.

  2. Build

    We deploy collection, detection, and hardening, starting with the highest-risk systems.

  3. Operate

    Agents watch every day. Engineers review every week and approve any risky action.

  4. Report

    Every month you receive a plain-language report and the next month's tuning plan.

Where AI fits, and where people stay in charge

Agents do the reading: they go through log volume no person could, group related events, and explain in plain words why something looks wrong. They also draft the fix.

People keep the authority. Blocking a known attacker IP can run automatically. Deleting data, changing accounts, or restarting production waits for an engineer. Every agent action is recorded so you can audit it later.

Experience we bring

  • Real-time data pipeline with Kafka and Spark Streaming, unifying scattered sources into live dashboards
  • Legacy physical servers moved to a Proxmox cluster and container-based services
  • RAG-based LLM agent that answers routine customer questions from internal documents

Technology

  • Python
  • FastAPI
  • PostgreSQL
  • MariaDB
  • Kafka
  • Spark
  • Airflow
  • dbt
  • Proxmox
  • Podman
  • Docker
  • Kubernetes
  • Claude
  • LangGraph
  • LangChain
  • RAG
  • Ollama

Talk to us about your environment

Write to contact@themonthly.tech, or send a technical support request if something needs attention now.

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