If you're in GRC, you know the pressure of being the interface between security and the business. That's not easy - especially when the technology moves faster than your policy review cycles. But here's an edge: build your own AI-powered news automation, and you'll walk away with more than a cool tool. You'll gain the hands-on AI security experience and technical understanding that prevents the business from blowing smoke past you.

This guide walks you through the GRC News Assistant 3.0: an open-source project featuring N8N workflows, container security hardening, prompt injection guardrails, and a NIST-aligned risk assessment template. 

Then this guide gets you hands on keyboard to install your own security hardened n8n deployment.

Whether this is your first GitHub project or your fiftieth, you'll come away with practical skills that transfer directly to enterprise environments.

The GRC News Assistant started in summer 2024 with Dr. Gerald Auger's Python-based version 1.0. Version 2.0 added AI labeling and rating using Daniel Meisler's Fabric Project to ruthlessly stack-rank which news articles deserve your attention. Version 3.0 takes it further with:

Here's what happens when you hit "Execute Workflow": every day before you wake up, your favorite news sources are checked. An army of AI interns (all named Claude) reads and rates the articles according to your preferences. The results land in a Notion database: S-tier content you need to read immediately, A-tier next, and so on.

Three things you're walking away with:

Before deploying any AI automation, you need to think through the risks systematically. That's where NIST Special Publication 800-30 comes in, the gold standard for conducting risk assessments.

📚 Free resource: NIST SP 800-30 Guide for Conducting Risk Assessments

Master this publication and you'll have 90% of what GRC candidates who just wing it during interviews are missing.

Your first spidey sense should activate when you see untrusted content from the internet. This workflow pulls external chaos onto your machine - news articles that could contain hidden instructions.

The lethal trifecta combines:

What if a threat actor hid these instructions in an article using white font?

If this happened without proper controls, you'd lose your API key - or worse.

Layer 1 - Guardrails Node: Before any content hits the AI, it passes through detection for jailbreak attempts, instruction injection, and topical alignment. Failed content gets flagged and blocked.

Layer 2 - Hardened System Prompts: The prompts explicitly tell the AI to treat input as data, not instructions. Example from the Fabric project:

Layer 3 - Output Validation: Even if the AI gets manipulated, we validate output. Only expected JSON fields are allowed. Invalid ratings default to D-tier and fall to the bottom.

👉 Pro Tip: This workflow deliberately excludes email webhooks and chatbot nodes. These are vectors for unauthorized access that would require the "full-slawn treatment" of additional security controls.

N8N has 400+ integrations, thousands of dependencies, and community contributions from around the world. That's a massive attack surface.

One compromised package - through typosquatting, account takeover, or a malicious maintainer - could backdoor your entire automation platform.

And here's the bigger concern: community nodes. N8N's own documentation warns these could contain Trojan horses with full access to your machine.

Before running docker compose, scan your containers with Docker Scout:

Here's the gotcha: the original Redis 7 Alpine image had 4 critical and 39 high severity vulnerabilities. Without scanning, those vulnerabilities would have shipped straight into your environment.

👉 Pro Tip: Accept risks with compensating controls. N8N has high-severity CVEs for Excel and expression evaluation libraries - but if you don't process Excel files from untrusted sources, you can document your acceptance and move forward.

Every security control lives in a single docker-compose.yml file. Here's what makes it hardened:

If Postgres or Redis gets compromised, the attacker is trapped. No exfiltration, no command and control.

Here's how the deployment maps to NIST Cybersecurity Framework functions:

Identity & Access Management:

Data Security:

Platform Security:

1. Clone the repository

The full project with installation instructions is available on GitHub.

📚 Free resource: GRC News Assistant 3.0 on GitHub

2. Review the Docker hardening controls

Understand why each one matters. This knowledge transfers directly to enterprise environments.

3. Grab the risk assessment template

Apply it to something else - another AI security assessment, a SaaS integration. The framework is the same. Available in Word and Markdown formats.

4. Put it in your portfolio

This isn't about the tool. It's about demonstrating you can take initiative, build securely, and document your thinking.

When you stand up this workflow, you're learning:

Here's the thing: practical skills are weighted way more heavily than certifications alone right now. If you can demonstrate you've built something, secured it, assessed the risks, and documented your thinking - that's what keeps you in the room.

AI security for automation workflows are here to stay. They're diffusing as shadow IT faster than most security teams can assess them. By building this project, you're not just getting a news aggregator - you're developing the technical chops to be a trusted business partner when these conversations get heated.

Check out the video walkthrough: "GRC News Assistant 3.0: AI Automation with Container Security"

For structured GRC training that builds these skills systematically, explore the Definitive GRC Analyst Master Class at Simply Cyber Academy.

And if you want to stay current on the threats informing your risk assessments, join the Simply Cyber Daily Threat Brief - live every weekday morning.

Now go build something secure. 🙌