98% Of ChatGPT Users Are Doing It Wrong – Here’s How To Unlock The Other 95% For Cybersecurity & IT Workflows + Video

Introduction:

Most people interact with ChatGPT like a digital search engine – asking a question, getting an answer, and moving on. But that surface-level usage barely scratches 5% of what the platform can actually deliver. The real transformation happens when you stop treating AI as a single-turn chatbot and start engineering it as a persistent workflow layer with memory, projects, research, and execution patterns combined together. For cybersecurity professionals, IT administrators, and AI engineers, this shift isn’t just about productivity – it’s about building repeatable, secure, and intelligent systems that compound in value over time.

Learning Objectives:

Master advanced ChatGPT features including Projects, Memory, Agent Mode, and Deep Research to build persistent security workflows.
Develop structured prompting techniques that yield verifiable, actionable outputs for vulnerability assessments, threat intelligence, and system hardening.
Integrate AI-driven automation with Linux and Windows command-line tools to accelerate incident response, log analysis, and compliance auditing.

Beyond the Chatbot: Building a Persistent AI Workflow Layer

The fundamental mistake most users make is treating every interaction with ChatGPT as a standalone event. In cybersecurity, where context is everything, this approach is crippling. The shift begins when you combine four core features into a cohesive system:

Memory: ChatGPT retains context across sessions, allowing it to remember your organization’s infrastructure, preferred tools, and historical incident patterns.
Projects: Organize prompts, files, and research into dedicated workspaces – ideal for tracking a specific penetration test, compliance audit, or threat hunting campaign.
Deep Research: Deploy autonomous research agents that crawl and synthesize information from multiple sources, saving 4–5 hours weekly on market intel and competitive analysis alone.
Agent Mode: Delegate execution tasks to AI agents rather than performing them manually – a paradigm shift that changes how much you can actually get done.

Step‑by‑step guide to building your first security workflow:

Create a Project: In ChatGPT, create a new Project named “Weekly Threat Intel.”
Set Memory Context: Feed it your organization’s asset inventory, critical CVEs, and preferred SIEM tools.
Add Research Instructions: “Each Monday, research the latest zero-day vulnerabilities affecting our stack. Summarize each with CVSS score, affected versions, and recommended mitigation steps.”
Enable Agent Mode: Allow the agent to pull data from web sources, cross-reference with NVD, and format outputs as a structured report.
Review and Iterate: Never settle for the first output – iterate by refining prompts and adding constraints until the results are production-ready.
Structured Prompting: The Art of Getting Verifiable Answers

Vague prompts produce vague, often hallucinated, outputs – a dangerous outcome when you’re relying on AI for security decisions. The solution lies in structured prompting: providing clear context, defining output formats explicitly, and demanding verifiable sources.

Key techniques for cybersecurity prompts:

Role Assignment: “Act as a senior SOC analyst with 10 years of experience in threat hunting.”
Context Injection: “Our environment runs Ubuntu 22.04 with NGINX 1.18. We’ve observed anomalous outbound connections on port 4444.”
Output Formatting: “Return findings as a JSON object with fields: ‘indicator’, ‘confidence_score’, ‘recommended_action’, and ‘references’.”
Verification Requirement: “For every claim, provide a direct source URL or command that can be independently verified.”

Step‑by‑step guide to prompt hardening:

Define the Objective: Clearly state what you need (e.g., “Generate a Suricata rule for CVE-2025-1234”).
Provide Environmental Context: Include OS versions, network architecture, and existing security controls.
Set Quality Gates: Instruct the model to include validation steps, test commands, and fallback procedures.
Iterate on Failures: If the first output is incomplete, feed it back with specific correction requests – “The rule misses UDP traffic; revise to include both TCP and UDP.”
Save as Template: Once perfected, save the prompt chain as a reusable template within your Project.
Linux & Windows Command Integration for AI-Assisted Security

AI becomes truly powerful when it generates actionable commands that you can execute directly in your terminal or PowerShell. Below are verified commands that complement AI-driven security workflows.

Linux Commands for Log Analysis & Threat Hunting:

 Extract all failed SSH login attempts from auth.log
sudo grep "Failed password" /var/log/auth.log | awk '{print $11}' | sort | uniq -c | sort -1r

Monitor real-time network connections and feed into AI for analysis
ss -tunap | grep ESTAB | awk '{print $5}' | cut -d: -f1 | sort | uniq -c

Query systemd journal for specific service errors (e.g., NGINX)
sudo journalctl -u nginx --since "1 hour ago" | grep -E "error|critical|alert"

Generate a hash of critical binaries for integrity checking
sha256sum /usr/bin/ssh /usr/bin/sudo /bin/ls > baseline_hashes.txt

Compare current hashes against baseline to detect tampering
sha256sum -c baseline_hashes.txt 2>&1 | grep -v "OK"

Windows PowerShell Commands for Security Auditing:

 Get all scheduled tasks that run with SYSTEM privileges
Get-ScheduledTask | Where-Object {$_.Principal.UserId -eq "SYSTEM"} | Select-Object TaskName, State

List all inbound firewall rules allowing any protocol from any source
Get-1etFirewallRule -Direction Inbound -Action Allow | Where-Object {$_.RemoteAddress -eq "Any"}

Retrieve recent security event logs (Event ID 4624 = successful logon)
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4624; StartTime=(Get-Date).AddHours(-24)} | Select-Object TimeCreated, @{N='User';E={$_.Properties[bash].Value}}

Check for unsigned drivers (potential rootkit indicators)
Get-WindowsDriver -Online | Where-Object {$_.Signer -eq $null}

Export all running processes with network connections for AI analysis
Get-1etTCPConnection | Select-Object LocalAddress, LocalPort, RemoteAddress, RemotePort, State, OwningProcess | ForEach-Object { $_ | Add-Member -1otePropertyName "ProcessName" -1otePropertyValue (Get-Process -Id $_.OwningProcess -ErrorAction SilentlyContinue).ProcessName -PassThru }

Step‑by‑step guide to using AI for command generation:

Describe the Task: “Generate a Linux one-liner to find all files modified in the last 15 minutes under /etc and /var that are owned by root.”
Request Explanation: “Explain each component of the command and what it does.”
Ask for Alternatives: “Provide a Windows PowerShell equivalent for the same task.”
Validate Output: Run the command in a test environment first – never execute AI-generated commands directly in production.
Feed Back Errors: If a command fails, paste the error message back into ChatGPT and ask for a corrected version.
Advanced Features: Memory, Projects, and the Compounding Effect

Most users ignore ChatGPT’s advanced features, treating it as a disposable chatbot. But when Memory and Projects click together, the tool becomes fundamentally different – a thinking partner that compounds in value over time.

Memory allows the AI to retain context across sessions. For a security team, this means:
– Remembering your organization’s approved tools (e.g., “We use Wazuh for SIEM and TheHive for case management”).
– Storing incident response playbooks so they’re always available.
– Retaining lessons learned from past breaches to inform future decisions.

Projects provide a structured workspace where you can:

Organize threat intelligence feeds, vulnerability databases, and internal documentation.
Maintain separate workspaces for different clients, environments, or compliance frameworks (GDPR, HIPAA, PCI-DSS).
Share projects with team members for collaborative threat hunting.

Step‑by‑step guide to setting up Memory and Projects:

Enable Memory: In ChatGPT settings, ensure Memory is turned on.
Seed the Memory: Explicitly tell ChatGPT: “Remember that our primary cloud provider is AWS, we use Terraform for IaC, and our critical assets are in eu-west-1.”
Create a Project: Click “Projects” in the sidebar and name it “AWS Security Hardening.”
Add Files: Upload your Terraform scripts, security group configurations, and IAM policies.
Set Instructions: Add a persistent instruction: “When analyzing any security group, always check for 0.0.0.0/0 on ports 22, 3389, and 27017.”
Test the Workflow: Ask a question like “Review our security groups for overly permissive rules” – the AI will now reference your uploaded files and persistent instructions.
Don’t Use ChatGPT Only for Content – The Ultimate Trap

Adam Biddlecombe warns that using ChatGPT solely for content creation is “the ultimate trap most people fall into”. In cybersecurity, the real value lies in using AI as a strategist, researcher, creator, and assistant – a full team, not just a writer.

Practical security applications beyond content:

Vulnerability Research: Ask ChatGPT to analyze a new CVE, correlate it with your asset inventory, and recommend prioritized patches.
Incident Response: During an active breach, use ChatGPT to suggest containment steps, draft communication templates, and correlate IOCs.
Compliance Automation: Generate audit-ready documentation, map controls to frameworks, and identify gaps.
Threat Modeling: Describe your application architecture and let ChatGPT identify potential attack vectors and mitigation strategies.
Code Review: Submit snippets of your infrastructure-as-code and have the AI flag security misconfigurations.

Step‑by‑step guide to using AI as a security strategist:

Define the Strategic Goal: “We need to improve our cloud security posture before the next external audit.”
Break Down into Sub‑tasks: Use ChatGPT to decompose the goal into actionable items (e.g., “Review IAM policies,” “Audit S3 buckets,” “Enable CloudTrail”).
Execute with Agent Mode: Delegate each sub‑task to an AI agent with specific instructions and success criteria.
Synthesize Results: Have the AI compile all findings into a single executive summary with prioritized recommendations.
Review and Refine: Treat the AI’s output as a first draft – iterate by challenging its assumptions and requesting deeper dives.

6. Security Considerations: The Risk of Shadow AI

While AI accelerates security workflows, it also introduces new risks. Employees pasting sensitive data into ChatGPT is a growing concern for CISOs. Organizations must extend audits beyond sanctioned apps to include shadow AI platforms like ChatGPT, Claude, and others accessed through unmanaged personal accounts.

Mitigation strategies:

Data Classification: Never input proprietary source code, PII, or classified information into public AI models.
Use Enterprise Tiers: Opt for enterprise-grade AI solutions that offer data isolation, compliance certifications, and audit trails.
Prompt Sanitization: Before submitting any prompt, remove or anonymize sensitive data (e.g., replace real IPs with 10.0.0.0/8, redact usernames).
Regular Training: Educate your team on the risks of shadow AI and establish clear usage policies.
Monitor Outbound Traffic: Use network monitoring tools to detect unusual API calls to AI platforms.

Step‑by‑step guide to safe AI usage in security:

Create a Data Sanitization Script: Write a Python or bash script that automatically redacts sensitive information from logs before they’re fed into ChatGPT.
Establish a Review Process: Never act on AI-generated commands or configurations without human verification.
Enable Audit Logging: If using an enterprise AI platform, ensure all interactions are logged and reviewable.
Conduct Regular Risk Assessments: Periodically evaluate the security posture of your AI usage, including data flows and access controls.
The 20-Level Roadmap: From Novice to AI Power User

Adam Biddlecombe has developed a meticulous 20-level roadmap designed to guide users from basic chatbot usage to advanced AI orchestration. While the full roadmap is extensive, here are the key milestones relevant to cybersecurity professionals:

Levels 1–5: Basic prompting, single-turn queries, and simple research.
Levels 6–10: Structured prompting, role assignment, and output formatting – essential for getting verifiable security answers.
Levels 11–15: Project-based workflows, memory integration, and cross-session context – building persistent security knowledge bases.
Levels 16–20: Agent mode, multi‑tool orchestration, and autonomous research – enabling AI to act as a full-fledged security analyst.

Step‑by‑step guide to advancing your AI skills:

Self‑Assess: Identify where you currently stand on the roadmap.
Set a Target: Choose the next level and focus exclusively on mastering its skills.
Practice Daily: Dedicate 15 minutes each day to crafting and refining prompts for real security tasks.
Join a Community: Engage with AI security forums, LinkedIn groups, or newsletters like Mindstream to stay updated on best practices.
Track Progress: Maintain a journal of your prompts, outputs, and lessons learned – this compounds your expertise over time.

What Undercode Say:

Key Takeaway 1: The gap between basic and advanced AI usage isn’t technical skill – it’s intentionality. Structuring context, defining output formats, and iterating on results are the real differentiators.
Key Takeaway 2: Treating AI as a persistent workflow layer with memory, projects, and agent mode transforms it from a question‑answer tool into a force multiplier for security operations.

Analysis: Adam Biddlecombe’s insights reveal a profound truth about AI adoption in cybersecurity: most professionals are underutilizing the technology not because of a lack of capability, but because of a lack of imagination and structured approach. The shift from reactive prompting to proactive workflow design is analogous to moving from manual scripting to full infrastructure-as-code – it requires a mindset change, not just a tool upgrade. For security teams, this means investing time in building reusable prompt libraries, documenting AI-assisted playbooks, and continuously refining their approach based on real-world outcomes. The organizations that embrace this paradigm will not only achieve greater efficiency but also develop a compounding knowledge asset that improves with every interaction. Conversely, those who remain stuck in single-turn chatbot mode will find themselves increasingly outpaced by adversaries who are already weaponizing AI for reconnaissance, phishing, and exploit development. The stakes are high, and the window for catching up is narrowing.

Prediction:

+1 The integration of AI-driven workflows into mainstream security operations will reduce mean time to detect (MTTD) by 40–60% within the next 18 months, as teams leverage automated threat intelligence and real‑time log analysis.
+1 Enterprise-grade AI platforms with built‑in data isolation and compliance features will become the de facto standard, displacing consumer‑grade tools in regulated industries.
-1 The proliferation of shadow AI – employees using unmanaged personal accounts for work tasks – will lead to a significant data breach within a Fortune 500 company, triggering a wave of regulatory scrutiny and new compliance mandates.
-1 Adversaries will increasingly use AI to automate vulnerability discovery and exploit generation, widening the gap between advanced and novice security teams unless defensive AI adoption accelerates.
+1 The emergence of AI‑specific security certifications and training programs will create a new career track for professionals who can bridge the gap between cybersecurity and prompt engineering.

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