AI Training for Cybersecurity Engineers

AI Is Both A Tool And A Target In Cybersecurity. What Skills Do You Need to Stay Relevant in the AI Era ? 1 - AI Governance and Ethics ↳ Understand the ethical use of AI, privacy concerns, and governance frameworks like the EU AI Act or NIST AI Risk Management Framework. Organizations need professionals who can ensure AI systems comply with regulations and align with ethical principles. 2 - Adversarial AI Defense ↳ Cyber-criminals are weaponizing AI for attacks. Learn how to defend against adversarial AI techniques, like poisoning machine learning models or bypassing AI-powered defenses. 3 - Secure AI Systems ↳ AI systems have unique vulnerabilities, such as data manipulation, model extraction, and bias exploitation. Gain expertise in securing AI pipelines, from training data to deployment, and mitigating these risks. To stay ahead, you need to understand how to govern it, secure it, and leverage its capabilities. These skills will not only keep you relevant but position you as a leader in the AI era of cybersecurity. Check out the AWS Generative AI Security Scoping Matrix for more detail on this topic Good luck on your journey!

🚨 Want to get better at AI? I gathered these trainings so you don't have to🚨 AI is already changing how we detect threats, respond to incidents, and build smarter security solutions. If you're in cybersecurity and not learning how AI works (or how to work with it), you're risking obsolescence. 💡 The good news? You don’t need a computer science degree or a huge budget to get started. Below are free or low-cost AI training resources that are beginner-friendly and relevant to our field: 🔗 Top AI Training Resources: Google AI – Learn with Google (https://lnkd.in/gBq3RCZ7) ✅ Free 📚 Topics: Machine learning basics, AI ethics, practical tools. ⭐ Review: A solid introduction with real-world examples. Great if you're just getting started. Elements of AI (https://lnkd.in/g5skhnVs) ✅ Free 📚 Topics: Introduction to AI and machine learning, no coding needed. ⭐ Review: Perfect for non-technical learners. Created by the University of Helsinki. Very clear and well-paced. Microsoft Learn – AI Fundamentals (https://lnkd.in/gjpb75X2) ✅ Free 📚 Topics: AI concepts, Azure AI services, real-world use cases. ⭐ Review: Useful for understanding how AI integrates with cloud and enterprise systems. Great for security professionals. Coursera – AI for Everyone by Andrew Ng (https://lnkd.in/gSt-FFtY) ✅ Free (with option to pay for certificate) 📚 Topics: What AI can and can’t do, business applications, ethics. ⭐ Review: One of the best overviews from a world-class instructor. No prior knowledge needed. IBM AI Engineering on Coursera (https://lnkd.in/gwNJbndJ) 💰 Low cost (monthly fee, free trial available) 📚 Topics: Deep learning, machine learning, Python, OpenCV, and more. ⭐ Review: More advanced and hands-on. Ideal for those ready to dive deep. 👨💻 In cybersecurity, AI is being used for: Threat detection and triage Anomaly analysis Predictive risk modeling Automating SOC tasks The more you understand AI, the better you'll be at adapting and staying competitive. Whether you're a SOC analyst, threat hunter, or aspiring CISO, AI literacy is now a critical career skill. Start small. Stay consistent. Grow your skill set. Future you will thank you. 🔁 Know someone in cyber who needs this? Share it with your network. #Cybersecurity #CareerGrowth #InfoSec #AICybersecurity

The Hidden Goldmine: Where AI and Cybersecurity Intersect (and How to Stand Out) Everyone is talking about AI. ↳Everyone is hiring in cybersecurity. ↳But here’s the secret most career switchers and seasoned tech professionals are missing: The most strategic, high-growth roles in tech sit at the intersection of AI and Cybersecurity. Let’s break it down: 1. AI systems are vulnerable by default ↳ Every AI model you see—from ChatGPT to fraud detectors—is trained on data. ↳ That data is often sensitive, personal, or proprietary. ↳ That means these systems require privacy engineering, data governance, model risk oversight, and trust architecture. If you're skilled in GRC, privacy, or data security, you’re not an outsider to AI—you’re essential. 2. AI is triggering compliance gaps and regulatory firestorms ↳ From the EU AI Act to U.S. Algorithmic Accountability frameworks, companies are being forced to govern how AI is used, audited, and tested. ↳ Cyber pros who understand frameworks like NIST AI RMF or ISO 42001 are getting hired to write policies, run gap analyses, and build oversight committees. This isn’t theoretical—it’s urgent. 3. Roles are already shifting AI + Cyber roles are showing up on job boards under titles like: ↳ AI Risk Analyst ↳ AI Governance Manager ↳ Algorithmic Compliance Lead ↳ Model Security Architect ↳ Responsible AI Lead ↳ AI Privacy Officer These roles require cybersecurity insight, ethical judgment, and documentation skills. Sound familiar? 4. You don’t need to be a machine learning expert Let me be clear: You don’t need to train LLMs to qualify. You need to: ↳ Understand model behavior risks (bias, hallucination, drift) ↳ Know how to assess vendor model risks ↳ Translate ethical risk into policy + controls ↳ Work with Legal, Product, and Engineering to enforce governance ↳The future of cybersecurity isn’t just about breaches. It’s about AI misuse, manipulation, and malfunction. 5. Here’s how to break in: ↳ Learn AI risk and governance basics (start with NIST AI RMF) ↳ Build a portfolio of 1–2 AI + GRC use cases (e.g., assessing AI-powered HR tools) ↳ Tailor your résumé to speak to AI risk, model audits, or vendor reviews ↳ Write 2–3 LinkedIn posts showing your take on AI ethics or oversight ↳You don’t need to wait for the industry to catch up. You can lead. ↳Book a 1-on-1 session, via my Bio, so we can work together with strategy, style, and visibility. 🔔 Follow for more tech career insights! ♻️ Repost if this was helpful!