AI has a profound impact on cybersecurity. On one hand, the AI-powered security controls enable efficient analysis of large datasets in real-time and quicker identification of anomalies that allow human analysts to focus on more complex cybersecurity tasks. On the other hand, AI also enables cyber attackers to create adaptive and blended threats at a much higher velocity. Thus, cybersecurity professionals need to increase their focus on adapting skills to stay relevant in this evolving era of AI-powered cybersecurity services.
In this changing landscape, the future of cybersecurity lies in nurturing a new generation of experts who are not just technically adept but also strategic, collaborative, and forward-thinking. Consequently, the cybersecurity professionals must embrace agile methodologies, automation, and scalable security practices rooted in software engineering principles. These professionals will be the architects of next-generation defences – ensuring enterprises remain resilient, agile, and secure in an increasingly digital world.
Adoption of hyper automation in cybersecurity shall involve advanced automation tools and AI to perform many repetitive tasks such as monitoring, detecting and responding to incidents without much human intervention. Some of the examples are
- AI-Driven SIEM, SOAR
- Automation with cognitive RPA
- Knowledge of various AI tool sets to create data models
- AI assists in vulnerability management to detect critical vulnerabilities
- Handling zero-day threat management and mitigations using AI-based threat intelligence
- AI Assist and Gen AI in IDAM to handle complex access provisioning workflows
- AI for endpoint security to detect and manage malicious behaviour
- AI for preventing malware execution, detection of malicious services and changing log patterns swiftly, and so on
How to become an expert in AI-enabled cybersecurity solutions?
To become an expert in AI-enabled cybersecurity offerings, start with learning Cybersecurity fundamentals, including network security, threat intelligence, security incident and event management, cloud security, etc. This should be supplemented with enhancing core technologies, including Python, ML with Data Science, etc., that enable practising AI with various tools used for detecting duplicate incidents, outliers, and auditing potential vulnerabilities in data models.
Also, learning Natural Language Processing (NLP) for automating phishing detection, analysing various conversational texts, and threat feed summarisation would be advantageous as several specialised tracks are emerging within AI security, requiring a deeper understanding to evaluate AI-specific threats. Some essential topics to be considered for learning gen AI are:
- Generative AI Landscape
- Prompt Engineering
- Learning ChatGPT, Azure OpenAI models
- LLMs and SLMs in Cybersecurity
- Cybersecurity Chatbots and Their Uses
- Subscribing to capstone projects under senior mentors to simulate and demonstrate capabilities
It is also important to mention the need for stronger communication skills to explain AI security risks to non-technical stakeholders. Many executives and business users don’t understand AI security implications, making clear risk communication essential for getting proper resources and support. This is essential as AI technology is evolving rapidly, creating new attack surfaces and defensive opportunities that need to be governed with sponsorship from the business stakeholders.
Why are cybersecurity AI certifications important?
Certifications are a crucial validation of expertise in AI-driven security practices. They not only demonstrate specialised skills and deepen understanding of AI attack vectors and risks but also bolster credibility in responding to complex cyber threats. Industry-recognised credentials accelerate career growth and ensure security professionals are equipped with the latest knowledge to defend against the evolving landscape of AI-enabled threats.
Conclusion
As cybersecurity is becoming increasingly complex to handle, security professionals should consider building new-age AI into their skillsets in machine learning, deep learning, gen AI and AI ethics for threat detection. The most successful cybersecurity professionals will exhibit deep technical knowledge with strategic thinking, maintaining their core security expertise while developing AI literacy and staying adaptable to rapid technological change.
Authored by Brijesh Balakrishnan, VP & Global Head – Cybersecurity, Infosys
