The rapid integration of artificial intelligence (AI) across industries, including cybersecurity, has created a sense of urgency among professionals. As organizations increasingly adopt AI tools to strengthen security, cyber professionals now face a crucial question: What new skills do I need to stay relevant?
October is Cybersecurity Awareness Month, making it the perfect time to tackle this pressing issue. With AI transforming threat detection, prevention, and response, there’s no better time to explore the essential skills professionals may need.
Whether you’re a seasoned AI expert or just entering the field, understanding which AI cybersecurity competencies will provide you with the most value is critical to staying competitive.
The growing need for AI expertise in the field of cybersecurity
Initially, AI systems were used in predictive analytics and behavioral monitoring to detect anomalies in network traffic. Now, AI’s influence has expanded, enabling everything from advanced threat detection systems to incident response frameworks. AI and machine learning (ML) are increasingly the most important skills that cybersecurity managers require from their teams.
With 92% of IT jobs expected to be transformed by AI and 74% of professionals fearing AI will make their skills obsolete, cybersecurity professionals must embrace AI-specific upskilling to stay competitive.
Learn how to develop cybersecurity skills
Key AI skills cybersecurity professionals need to develop
The following areas represent some of the most valuable and cross-functional skills that can be applied in AI-driven cybersecurity roles:
1. AI/ML models and model audit
A deep understanding of how AI and ML models work is essential for modern cybersecurity. AI/ML models are now an integral part of security operations, with systems such as 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 input to fool AI systems. Adversary disruptions can trick AI models to misclassify data, leading to potential security breaches. That’s why, as a cybersecurity professional, it’s crucial to learn how to audit these models.
The auditor must know how to review design documentation and threat models to verify the logic of the algorithm and ensure that the consequences for the user and society are taken into account. Before implementation, the auditor must confirm that appropriate validation/testing has been performed 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 attacks on the model supply chain.
2. Data science
While AI and ML are important technologies, data science is the backbone that powers these technologies. 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 can help you intelligently discuss their potential applications in your organization. Applied data science also helps you understand how to build effective solutions to solve real-world 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 threat detection and response, providing real-time insights that would be impossible with traditional methods. For example, AI tools can identify shadow data, monitor for anomalies in data access, and alert cybersecurity professionals to potential threats from intruders who gain access to the data or sensitive information.
However, this also opens the door to AI-powered attacks, such as data poisoning or adversarial attacks that corrupt machine learning models by feeding them manipulated data. Cybersecurity professionals need to understand not only how to use AI for defensive purposes, but also how to prevent AI systems from being exploited. Integrating AI-specific threat intelligence skills, such as recognizing AI-driven phishing attacks or understanding how AI tools can be used for reconnaissance, is critical to maintaining robust defenses.
4. Natural Language Processing (NLP)
As AI applications expand, NLP has become a crucial tool in the cybersecurity arsenal. NLP enables machines to process and analyze large 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 intentions of potential attackers, and even identify security vulnerabilities in code.
Professionals skilled in NLP can help organizations automate tasks such as filtering malicious emails, detecting insider threats through communications 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, when hunting for threats, 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, especially within SIEM (Security Information and Event Management) systems, will significantly increase your ability to manage advanced cyber threats.
AI Certifications to Consider
Certifications are a great way to formalize new skills and stay at the forefront of the AI-driven cybersecurity landscape. Here are some certifications worth considering:
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GIAC Machine Learning Engineer (GMLE): This certification, provided by the SANS Institute, focuses on the application of AI and machine learning techniques in cybersecurity, with an emphasis on data science and real-world problem solving.
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Certified Ethical Hacker (CEH) v13: This certification integrates AI tools such as ChatGPT and ShellGPT into ethical hacking practices. CEH v13 emphasizes the use of AI in all phases of hacking, from exploration to vulnerability exploitation.
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Coursera’s AI and Cybersecurity Course: This program, offered by IBM, provides a practical introduction to using generative AI in cybersecurity operations, focusing on incident response and threat detection.
- IBM SkillsBuild: Offers a wide range of cyber-educational resources, including AI-based courses for a wide range of skill levels.
The future of cybersecurity is powered by AI
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. Investing in the right skills and certifications can ensure you not only stay relevant, but thrive in the AI-driven future.
