Reskilling and Upskilling: Cybersecurity and Ethical AI
No, this isn’t just another blog about AI. It’s about skills we all need to be arming ourselves with to future-proof our careers as AI becomes more prevalent. Cybersecurity and Ethical AI are more connected than ever before. These fields are closely intertwined when it comes to protecting digital systems, data, and ensuring the responsible use of technology. As AI continues to weave itself into the fabric of our digital and working lives, the line between keeping systems secure and ensuring AI is used ethically is becoming increasingly important. Developing expertise in these areas will help you stay relevant and well-prepared to tackle the challenges ahead.
Sadly I don’t have a crystal ball on exactly which skills we will all need, this series will be based on member feedback and suggestions from - you guessed it - Gen AI platforms.
Getting Started: Cybersecurity and Ethics
Before diving into the AI aspects, let’s talk about the foundation: cybersecurity and ethics. If you're looking to break into these areas, start by building a strong understanding of the core principles of cybersecurity and the ethical frameworks guiding responsible technology use.
1. Cybersecurity: Protecting Digital Systems
Concepts first: It’s essential to grasp the key concepts of cybersecurity—such as encryption, secure communication, and risk management. CSUnplugged (aimed at school aged learners) offers a great introduction to Public Key Encryption for instance. Free online resources like Cybrary or Khan Academy offer beginner-friendly courses.
Certifications to guide learning: Consider earning certifications to solidify your knowledge and show employers that you're serious. Certifications can guide you through understanding security frameworks and how to protect systems from threats. Two non-vendor specific ones include CompTIA Security+ or there are many different forms of a Certified Ethical Hacker (CEH), this one on Udemy.
Practical experience: Engage in hands-on activities like Capture the Flag (CTF) competitions or participate in cybersecurity labs to test and hone your skills in real-world scenarios. Platforms like Hack The Box offers hands on labs (freemium model) or TryHackMe (also freemium).
2. Ethical Foundations: Responsible Use of Technology
Learn about data ethics: Understanding how technology, particularly AI, impacts people is critical. But where to start. The Data Ethics and Society Reading Group has a great reading list and other resources on their site. Ethical frameworks like IEEE’s Ethically Aligned Design provide clear guidance on how technology should be built responsibly. Even reading up on regulations like GDPR and the new EU AI Act will help with learning the basics and what the regulations are protecting us from.
Get familiar with bias and fairness: Understanding Bias isn’t new. I’ve written about this before in What is a Code of Ethics and some years ago Getting to know your Unconscious Bias. I suggest you also explore topics like algorithmic bias and the ethical implications of AI systems by finding organisations focused in this space like The Ethics Centre in Australia.
Attend events: I know in person events are few and far between these days but there are plenty of online events out there on Ethics to join - acknowledging the timezones mean many are after hours. Look for webinars, Lunch and Learns, meetups.
Adding AI to the Mix: Cybersecurity and AI Synergy
Once you have a grasp on cybersecurity and ethics concepts, it will be time to look at how AI and Machine Learning (ML) fit into the puzzle. ML, a key subset of AI, is revolutionising the way we both protect and attack systems. Machine learning models can be trained to detect patterns in vast amounts of data, enabling cybersecurity tools to identify threats in real-time, predict breaches before they happen, and even automate incident response.
At the same time, cybercriminals are leveraging ML to make their attacks more sophisticated and adaptive. As a result, understanding how both AI and ML work—not just in isolation but in tandem with cybersecurity—is critical to safeguarding digital systems in this new age of automated threats.
Focus on the basics of AI and ML: There are loads of free AI and ML courses out there on Platforms like Coursera. Vendors also offer courses on their platforms but I would suggest focus on introduction courses first.
Understand AI vulnerabilities: While AI can be deployed to protect us, it’s also susceptible to manipulation. Learn how adversarial attacks work and how to protect AI models from being compromised. This knowledge will allow you to secure both traditional digital systems and AI-driven technologies. I’d start with this resource from the UK Government.
Security Vendors have free getting started resources: eBooks like this one from Splunk or this course from LinkedIN learning can help to really understand the convergence of AI / ML and Cybersecurity.
Why These Skills Matter for the Future
While you may not see job listings today specifically asking for this combination of cybersecurity and ethical AI skills, developing expertise in both is about future-proofing the workforce. As AI systems become more integrated into everything from critical infrastructure to personal technology, the need for professionals who understand both the security implications and the ethical considerations will only grow. Many orgs are already feeling the pressure to adapt to AI, but the systems we put in place today will need to be protected and used responsibly for years to come.
If you can start acquiring these skills now, you will position yourself “ahead of the curve” as the saying goes. Whether it’s cybersecurity analysts preventing AI-driven threats or ethical technologists guiding the responsible deployment of AI systems, the dual importance of protecting AI-driven systems and ensuring those systems are used responsibly will be in high demand. As businesses and industries scramble to keep up with technological advancements, those who can navigate both worlds—security and ethics—should have the competitive edge.
