The rapid integration of artificial intelligence (AI) across industries, including cybersecurity, has sparked a sense of urgency among professionals. As organizations increasingly adopt AI tools to bolster security defenses, cyber professionals now face a pivotal question: What new skills do I need to stay relevant?

October is Cybersecurity Awareness Month, which makes it the perfect time to address this pressing issue. With AI transforming threat detection, prevention and response, what better moment to explore the essential skills professionals might require?

Whether you’re a seasoned AI expert or just entering the field, understanding which AI cybersecurity competencies give you the most value is critical to staying competitive.

The growing need for AI expertise in cybersecurity

Initially, AI systems were used in predictive analysis and behavior monitoring to detect anomalies in network traffic. Now, AI’s influence has expanded, powering everything from advanced threat detection systems to incident response frameworks. Increasingly, AI and machine learning (ML) are the top skills that cybersecurity managers want from their teams.

With 92% of IT jobs expected to be transformed by AI and 74% of professionals fearing AI could make their skills obsolete, cybersecurity professionals must embrace AI-specific upskilling to remain competitive.

Key AI skills for cybersecurity professionals to develop

The following areas represent some of the most valuable and multifunctional skills that can be applied in AI-driven cybersecurity roles:

1. AI/ML models and model auditing

A deep understanding of how AI and ML models work is indispensable for modern cybersecurity. AI/ML models are now integral to security operations, with systems like next-generation firewalls and intrusion detection tools using AI to detect threats based on behavior rather than content.

However, AI models are not immune to attacks where malicious actors attempt to manipulate data inputs to fool AI systems. Adversarial perturbations can trick AI models into misclassifying data, leading to potential security breaches. Therefore, as a cybersecurity professional, it’s crucial to learn how to audit these models.

The auditor should know how to assess design documentation and threat models to verify the algorithm’s logic and ensure that user and societal impacts are considered. Before deployment, the auditor must confirm that proper validation/testing has been conducted with satisfactory results and that only authorized models are deployed. Organizations using third-party libraries or pre-trained models should integrate static scans into CI/CD pipelines to detect software vulnerabilities and prevent model supply chain attacks.

2. Data science

While AI and ML are headline technologies, data science is the backbone that powers them. Effective threat detection, risk analysis and anomaly identification depend on the ability to analyze and interpret large sets of data.

Courses like the SANS Institute’s SEC595 teach professionals how to apply data science and AI to real-world cybersecurity problems. From extracting valuable insights from threat intelligence to building custom AI models that can predict future attacks, data science is an essential skill for any professional looking to leverage AI effectively in cybersecurity.

Knowing how AI tools like ChatGPT really work enables you to intelligently discuss their potential uses in your organization. Applied data science also helps you understand how to build effective solutions to solve real cybersecurity problems using machine learning and AI.

3. AI-specific threat intelligence

The ability to work with AI-powered threat intelligence tools is also becoming increasingly important. AI enables faster detection and response to threats, providing real-time insights that would be impossible with traditional methods. For example, AI tools can identify shadow data, monitor for abnormalities in data access and alert cybersecurity professionals about potential threats by intruders accessing the data or sensitive information.

However, this also opens the door for AI-powered attacks, such as data poisoning or adversarial attacks that corrupt machine learning models by feeding them manipulated data. Cybersecurity professionals must not only understand how to use AI for defensive purposes but also how to safeguard AI systems from being exploited. Incorporating AI-specific threat intelligence skills, such as recognizing AI-driven phishing attacks or understanding how AI tools can be used for reconnaissance, is vital to maintaining a robust defense.

4. Natural language processing (NLP)

As AI applications expand, NLP has emerged as a critical tool in the cybersecurity arsenal. NLP allows machines to process and analyze vast amounts of unstructured textual data, such as email communications or malware code, to detect hidden threats. It can also be used to automate phishing detection, analyze the intent of potential attackers and even identify security vulnerabilities in code.

Professionals skilled in NLP can help organizations automate tasks like filtering malicious emails, detecting insider threats through communication monitoring and analyzing attack vectors based on text data from threat intelligence feeds.

5. Generative AI for cybersecurity

Generative AI, a subset of machine learning, is becoming a powerful tool in both defensive and offensive cybersecurity operations. By analyzing patterns and generating predictive models, generative AI helps cybersecurity teams preemptively identify vulnerabilities and potential attack vectors.

For example, in threat hunting, experts can use generative AI to simulate attack scenarios and develop malware in a controlled environment, allowing security professionals to practice defenses in real time. Learning how to integrate generative AI into your security operations, particularly within Security Information and Event Management (SIEM) systems, will significantly enhance your ability to manage advanced cyber threats.

AI certifications to consider

Certifications are a great way to formalize new skills and stay ahead of the curve in the AI-driven cybersecurity landscape. Here are some certifications worth considering:

• GIAC Machine Learning Engineer (GMLE): This certification, provided by the SANS Institute, focuses on applying AI and machine learning techniques in cybersecurity, with an emphasis on data science and real-world problem-solving.

• Certified Ethical Hacker (CEH) v13: This certification integrates AI tools like ChatGPT and ShellGPT into ethical hacking practices. CEH v13 emphasizes using AI in all stages of hacking, from reconnaissance to vulnerability exploitation.

• Coursera’s AI and Cybersecurity Course: This program, provided by IBM, provides a hands-on introduction to using generative AI in cybersecurity operations, focusing on incident response and threat detection.

• IBM SkillsBuild: Offers a host of cyber educational resources, including AI-based courses for a wide range of skill levels.

The future of cybersecurity is AI-powered

As AI continues to transform the cybersecurity landscape, professionals who invest in AI-specific skills will position themselves as leaders in the field.

Cybersecurity Awareness Month is the ideal time to assess where you stand in the rapidly changing world of AI and cybersecurity. By investing in the right skills and certifications, you can ensure that you not only stay relevant but also thrive in the AI-driven future.