Here's how you can incorporate artificial intelligence and machine learning into your Cybersecurity career.

Career in cybersecurity has taken a different turn, especially with GenAI applications. While these tools can be used to shorten the learning process, they can also be considered a serious help in solving problems. As a result, it will not be a prophecy to say that those who use these tools effectively will always be one step ahead in climbing the career ladder.

Incorporating AI and ML into your cybersecurity career can greatly enhance your capabilities in detecting, preventing, and responding to cyber threats. Here are some ways you can do this: 2-Threat Detection: Use machine learning to spot patterns of malicious activity in large datasets. Predictive Analysis: Employ AI to forecast potential cyber threats based on historical data. 3-Automated Response: Develop AI-driven systems to respond swiftly to security incidents. Fraud Prevention: Utilize AI to detect and prevent fraud by learning from patterns. Enhanced Authentication: Implement AI-based authentication like behavioral biometrics. 4-Security Analytics: Apply ML for advanced security analytics and log analysis.

Using machine learning (ML) and artificial intelligence (AI) in your cybersecurity work can greatly improve your capacity to recognize, stop, and handle threats. AI and ML can help you improve response times, stay ahead of cyberattacks, and ensure robust security for your business.

As we move to digitize our society further, every new digital solution opens up new needs in cybersecurity. AWS provides a platform to outsource a large portion of cybersecurity in new developments. Still, most companies hold a significant portion of their infrastructure on-premise. Cybersecurity experts focusing on on-premise to cloud migration will be in high demand over the next decade.

Generative AI has reduced the bar of entry into this field in the recent years. No longer are you required to learn how the underlying thing works - you just need to master how to apply and fine tune a base model and use that in your application. "Prompt Engineering" is now becoming a field of study on its own.

Stackexchange boards have been my favorite place to discuss technical details across different fields, like math, computer science, statistics and cyber security. They have cultivated a healthy environment for discussion, and it's easy to separate truth from falsehood. I learned that to understand a concept deeply, it's necessary to rewrite the found solution from scratch. This slows down progress but provides a much more comprehensive understanding.

While knowing theoretical aspects of AI is good - if it does not appeal to you - that's perfectly fine. In my personal experiences, data scientists who have a grasp of theoretical aspects or a Masters degree still struggle and fail to see light in cybersecurity. What matters is your will to experiment with using AI for different use cases. Simple things like learning to use pre-trained models via prompt engineering can go a long way.

Moving from our studies into the private industry, the difference between theoretical and practical solutions becomes apparent quickly. A simple solution that considers business goals across the company is often better than an elegant theoretical solution. A forward-thinking specialist will constantly stay up-to-date with the latest theoretical breakthroughs and look for ways to bring the concepts into practice. Most of our innovation stems from academia, such as ChatGPT.