I have been running a one-person cybersecurity practice for years. Then I discovered what happens when you pair an LLM with the right infrastructure.

This is my setup.

What I am Running

Three machines in my homelab:

• Mac mini (basement): Runs OpenClaw, my Telegram bot, automated agents
• Kraken: 4-GPU rig at for heavy compute
• Kali VM: Penetration testing playground at

The Mac mini handles the lightweight stuff — scheduling, messaging, orchestration. Kraken kicks in when I need GPU acceleration for model inference or training. The Kali VM is where I break things.

The Brains: OpenClaw + Claude

OpenClaw is an agent framework that gives me:

• A persistent agent I can message on Telegram
• Sub-agents I can spawn for parallel tasks
• Browser automation
• File system access
• MCP server integration

I talk to it like a person. Run a pen test on X. It figures out the tools, executes, reports back.

Here is what makes it different from just using Claude in the browser:

1. Persistence: The agent remembers context across conversations
2. Tool access: It can execute commands, not just suggest them
3. Automation: I can schedule recurring tasks (my daily AI pentesting research runs every morning)
4. MCP servers: I bolted on security tools directly

MCP Servers: The Force Multipliers

Model Context Protocol lets me connect AI directly to tools. My current setup:

• Metasploit: Automated vulnerability scanning
• Kali Linux: Full pen test toolkit
• Burp Suite: Web app testing
• OWASP ZAP: Automated DAST

When I tell the agent to check this URL for vulnerabilities, it spins up Burp, runs scans, parses results, and hands me a report. I do not touch the tools manually anymore.

The workflow is:

User → Telegram message → OpenClaw → Claude → MCP → Tool → Result → Telegram response

Total elapsed time: usually under a minute for basic tasks.

What Actually Happens

Let me give you a real example.

Yesterday I needed to test a client is web app. I typed:

Run a quick pen test on client-site.com, focus on OWASP Top 10

The agent:

1. Spawned a sub-agent
2. Fired up OWASP ZAP in passive mode
3. Kicked off a Nmap scan
4. Cross-referenced open ports with known exploits
5. Returned a prioritized finding list in about 45 seconds

Was it as thorough as a manual engagement? No. But it found three medium-severity issues I would have missed doing it manually. And it cost me zero extra effort.

The Numbers

• Monthly AI spend: Around $200-300 in API calls (Claude + Grok)
• Time saved: Hard to quantify, but I would guess 10-15 hours/week on repetitive tasks
• Tasks automated: Daily threat intel, vulnerability scanning, report drafting, Slack/Telegram notifications

What I would Do Different

If you are building this:

1. Start small: Do not try to automate everything. Pick one repetitive task and solve that first.
2. Do not cheap out on the LLM: The $20/month Claude subscription pays for itself in an hour. The reasoning quality difference between cheap and premium models is enormous.
3. Home lab > cloud: I run everything local. Kraken has 4 GPUs I use for model fine-tuning. Total electric bill: maybe $150/month. Compare that to AWS and it is not close.
4. MCP is the key: The integration layer matters more than the model. The better your tool connections, the more the AI can actually do.

The Point

I am a one-person shop. I do not have a team. I do not have a SOC. I do not have a devops department.

What I have is an agent that never sleeps, never complains, and can spin up a Metasploit session faster than I can remember the syntax.

The future of solo operators is not about working harder. It is about building better systems.

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Want details on any specific piece? Hit me up.