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Introduction:
Mike Holcomb’s experiment, using Google Gemini to generate a “mean-spirited but funny” critique of his own LinkedIn profile, is more than a holiday laugh—it’s a microcosm of generative AI’s dual-edged role in modern cybersecurity. This act of automated self-reflection in the OT/ICS security space highlights a critical shift: AI is no longer just a tool for defenders or attackers but is becoming integral to professional development, threat simulation, and the very mindset required to secure complex systems. As the technology used to create a professional headshot can also craft a devastating phishing campaign, understanding its full spectrum is no longer optional.
Learning Objectives:
- Understand the dual-use nature of generative AI as both a weapon for adversaries and a tool for defenders in cybersecurity.
- Learn practical applications of AI for professional self-assessment and security skill enhancement.
- Identify key resources and strategies for securing AI systems and pursuing specialized OT/ICS security training.
You Should Know:
- The Adversary’s New Arsenal: Offensive AI in Action
Generative AI has democratized and supercharged cybercrime, providing threat actors with tools for speed, scale, and sophistication previously available only to well-resourced groups. Adversaries leverage AI to analyze targets, craft convincing malicious content, and automate attacks, fundamentally changing the threat landscape.
Step-by-step guide explaining what this does and how to use it:
While ethical security professionals use these capabilities for defense and research, understanding the adversary’s workflow is crucial for building effective mitigations.
– Step 1: Reconnaissance & Profile Analysis: An attacker can use an LLM to scrape and synthesize publicly available information from LinkedIn, company websites, and news articles to create detailed profiles of key employees (like engineers or executives). This automates the target research phase.
Example Prompt for Research Synthesis (Illustrative): “Analyze the following text from a senior engineer’s professional blog and LinkedIn posts. Extract key technical projects mentioned, identify any technologies they express frustration with, and suggest a potential spear-phishing angle based on their writing style and interests.”
– Step 2: Campaign Generation: Using the profile, AI can generate highly personalized phishing emails or social media messages. It can mimic writing styles, reference real projects, and create compelling pretexts.
– Step 3: Malware & Exploit Adaptation: Generative AI can help obfuscate malicious code, generating polymorphic variants that evade signature-based detection. It can also be prompted to analyze public vulnerability descriptions (CVEs) and suggest or generate simple exploit code.
– Step 4: Automated Interaction: AI-powered agents (chatbots) can engage with initial victims in real-time, answering follow-up questions to build trust and guide them toward compromising actions, making Business Email Compromise (BEC) attacks more resilient.
The table below contrasts traditional attacks with their AI-enhanced counterparts:
| Attack Vector | Traditional Method | AI-Enhanced Evolution |
| : | : | : |
| Phishing | Generic, bulk emails with poor grammar. | Hyper-personalized emails mimicking colleague’s style, based on scraped data. |
| Deepfakes | Crude video/audio forgeries. | Real-time audio cloning for vishing (voice phishing) or convincing fake video calls. |
| Vulnerability Discovery | Manual code review or fuzzing. | AI-assisted code analysis to suggest potential weak points and generate proof-of-concept exploits. |
| Password Attacks | Brute-force or dictionary attacks. | AI-generated password guesses based on target’s personal info (pet names, hobbies) from social media. |
- The Defender’s Advantage: Leveraging AI for Security Operations
On the defensive front, generative AI acts as a force multiplier for Security Operations Center (SOC) teams, automating tedious tasks, enhancing threat detection, and accelerating response.
Step-by-step guide explaining what this does and how to use it:
A core defensive application is automating incident response and reporting, turning raw alert data into actionable intelligence.
– Step 1: Log Ingestion & Triage: AI models continuously analyze logs from SIEM, endpoints, and network traffic. They establish a behavioral baseline for users and systems and flag anomalies.
– Step 2: Incident Analysis & Enrichment: When an alert is flagged, AI can automatically correlate it with related events, threat intelligence feeds, and internal asset databases. It can generate a summary of the suspected incident.
Example SIEM Query Enhancement via AI: An analyst might start with a basic query for failed logins. An AI assistant could be prompted to: “Expand this query to also include subsequent successful logins from the same IP address within 10 minutes, and cross-reference the username with our list of privileged accounts.”
– Step 3: Actionable Response Playbooks: AI can recommend or even execute initial containment steps. For example, if a malware signature is detected on an endpoint, the system can automatically isolate the device from the network and trigger a scan.
– Step 4: Automated Reporting: Generative AI excels at synthesizing technical details into coherent reports for different audiences. It can transform a complex incident timeline into a clear executive summary, a technical deep-dive for engineers, and a draft notification for legal/compliance teams—all from the same data set.
- The Professional Mirror: Using AI for Self-Assessment & Skill Development
Mike Holcomb’s profile “roast” demonstrates a proactive, low-stakes application of AI for professional growth. Cybersecurity is a field where continuous learning and critical self-review are essential, and AI can serve as a brutally honest peer reviewer.
Step-by-step guide explaining what this does and how to use it:
You can use AI to audit and improve your professional materials and technical knowledge.
– Step 1: Choose Your Focus: Decide what you want to critique: your LinkedIn profile, resume, a project report, or even your understanding of a technical concept.
– Step 2: Craft a Specific, Critical Vague prompts yield vague results. Instruct the AI to adopt a specific expert persona and focus on weaknesses.
Mike Holcomb’s Effective Prompt (Adaptable): “Review my [LinkedIn profile text/Resume for a SOC analyst role]. Overlay it with hand-drawn red-ink scribbles, doodles, remarks, and comments, as a mean-spirited but funny critique from a veteran expert in [Cybersecurity/Cloud Security/Incident Response]. Focus on identifying vague buzzwords, missing technical specifics, unconvincing achievements, and gaps in the narrative. Ensure all feedback is in English.”
– Step 3: Iterate and Refine: Use the AI’s output as a brainstorming tool. Ask follow-up prompts like: “Based on these criticisms, rewrite my ‘Experience’ section for the [Job ] role to be more impactful and quantifiable.”
– Step 4: Technical Knowledge Checks: Test your understanding of vulnerabilities or protocols. Example “I am a junior security analyst. Explain the Log4Shell (CVE-2021-44228) vulnerability to me as if I were a system administrator who doesn’t understand Java. Then, role-play as a grumpy senior engineer and quiz me with 5 progressively harder questions about its exploitation and mitigation.”
- Securing the AI Pipeline: Defense Against AI-Powered Threats
As organizations adopt generative AI, they must secure the AI pipeline itself—the data, models, and applications—from poisoning, theft, and misuse. The OWASP Top 10 for LLMs outlines critical risks like prompt injection, sensitive data leakage, and insecure plugin design.
Step-by-step guide explaining what this does and how to use it:
Implementing basic security controls for AI applications is a necessary first step.
– Step 1: Data and Model Governance: Protect the training data and AI models as critical assets. Use encryption for data at rest and in transit. Implement strict access controls (Role-Based Access Control – RBAC) to ensure only authorized personnel can retrain or modify models.
– Step 2: Secure Application Design: Treat AI applications like any other web application. Conduct regular penetration testing. Guard against OWASP-identified risks like:
– Prompt Injection: Sanitize and validate all user inputs before sending them to the LLM. Implement context-aware filtering to reject instructions that try to override the system prompt.
– Sensitive Information Disclosure: Configure the AI tool not to use user prompts for further training. Implement data loss prevention (DLP) filters to scan AI outputs for accidental leakage of sensitive data.
– Step 3: Monitoring and Auditing: Maintain full logs of AI usage, including prompts and responses, for security audits and forensic analysis. Monitor for anomalous usage patterns that might indicate an account is being used to generate malicious content.
– Step 4: User Training and Policy: Establish clear Acceptable Use Policies for AI tools. Train employees on the risks of inputting proprietary code, customer data, or personal identifiable information (PII) into public AI chatbots.
5. Building Expertise: Foundational OT/ICS Cybersecurity Training
The context of Mike Holcomb’s post—OT/ICS security—is a specialized field protecting critical infrastructure. Bridging the IT/OT knowledge gap requires targeted training.
Step-by-step guide explaining what this does and how to use it:
Free and paid resources are available to build foundational knowledge.
– Step 1: Start with Free, Foundational Courses: The U.S. Cybersecurity and Infrastructure Security Agency (CISA) offers free, on-demand ICS training through its Virtual Learning Portal (VLP).
– How to Access: Visit the CISA VLP and register with a corporate or educational email. Recommended starting courses include “ICS Cybersecurity Landscape for Managers (FRE2115)” and the “210W” series, which covers differences between IT and ICS, common components, and risk.
– Step 2: Progress to Specialized, Hands-On Training: For technical professionals, seek out training with practical labs. Organizations like SANS Institute offer in-depth courses such as ICS410: ICS/SCADA Security Essentials, which provides hands-on experience with industrial control system protocols and attack simulations.
– Step 3: Leverage Vendor-Specific Platforms: If your organization uses specific OT security technology, explore their training. For example, Dragos Academy offers on-demand courses focused on operationalizing their platform and general OT cybersecurity practices.
– Step 4: Join Communities and Practice: Follow experts like Mike Holcomb for insights. Participate in communities around the OWASP GenAI Security Project to stay current on AI-specific risks. Set up a safe lab environment (using virtual machines and simulated PLCs) to practice concepts.
What Undercode Say:
- AI is a Dual-Edged Sword, Not Just a Tool: The most critical takeaway is that generative AI is an active participant in the cybersecurity arms race, capable of autonomous and creative actions on both sides. Defensive strategies must now account for AI-generated attacks that are more adaptive, persuasive, and scalable than human-created ones.
- Human Oversight and Humility are Non-Negotiable Security Controls: Mike’s experiment underscores that AI’s output requires human judgment. In security, this means AI-generated alerts, code, and policies must be reviewed. The “humility” to question AI’s conclusions and the wisdom to apply ethical and operational context are irreplaceable human skills that form the final layer of defense.
The analysis suggests we are moving from an era of human-versus-human hacking to human+AI versus human+AI conflict. The professional who will thrive is not the one who fears or blindly trusts AI, but the one who learns to pilot it effectively—using it to stress-test their own assumptions, automate their drudgery, and amplify their expertise, all while rigorously validating its output and safeguarding its pipeline. The “roast” is a metaphor for this entire process: using a powerful, sometimes uncomfortably blunt, automated system to reveal blind spots and strengthen your position.
Prediction:
In the near future, we will see the emergence of fully autonomous AI-powered cyber campaigns, where AI agents handle reconnaissance, vulnerability discovery, exploit tailoring, and deployment with minimal human intervention. This will compress attack timelines from months to days or hours. In response, regulatory frameworks for AI security will rapidly mature, mandating “AI Bills of Materials” (AIBOMs) and secure development lifecycles, as pioneered by projects like OWASP’s. The cybersecurity job market will bifurcate: high-demand roles will be for “AI Security Engineers” who harden AI systems and “Cyber AI Trainers” who curate data and fine-tune models for defense, while routine SOC analyst tasks will be almost fully automated. Ultimately, the most resilient organizations will be those that foster a culture of continuous learning and ethical humility, using AI not as a crutch but as a catalyst for sharper human expertise.
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