The rise of AI has impacted many industries but none more so than Cybersecurity. This will continue to be the case in the years to come, as areas like auditing, governance, and risk assessment adapt to technological progress.
In turn, this will require cyber security professionals to learn new skills to remain both relevant and employable. Read on to learn what those skills are, why they matter, and how to start building a strategy around them now.
How Cyber Security and AI Governance Have Converged
Traditionally, cybersecurity has focused on protecting systems and data. Its core concerns have centred around confidentiality system integrity and availability. In short, cyber security teams were primarily responsible for technical controls.
We’ve already seen widespread integration of AI into systems, and this will only continue to grow. In turn, this has changed the risk profile significantly.
Primarily, that’s because AI is used not only to process data, but to also make decisions. This opens up a sizable can of worms regarding bias, model drift, and other unintended outcomes. The outputs it generates can often be unpredictable, and even inexplicable. Such failures can create security problems, as well as compliance and reputational issues. As such, they present are both technical and governance risks.
That’s why AI governance has suddenly become such an integral part of cyber security. It is indisputably necessary for the safe usage of AI, requiring accountability around AI, monitoring of its usage and and audit it’s actions.
In short, cyber security is no longer simply about the technical side of things. It must now incorporate decision-making technology too.
Current Global Regulatory Trends
There are already a number of important regulations and standards surrounding AI, which directly affect cyber security, . Developed byboth governments and professional bodies. Overall, they generally require organisations to show that they are correctly maintaining control and standards regarding their AI usage.
You can gain a full understanding of what these involve by taking a relevant course with one of our qualified ALC Training instructors. For now, here are brief overviews of three of the most important examples:
- EU AI Act
Introduced in 2024, the European Union’s AI Act provides a common regulatory and legal framework for AI in the EU.
It classifies AI systems by risk, and ensures higher-risk systems have stronger requirements for documentation, governance, and oversight. Overall, the act made it clear that AI systems could no longer simply be functional, but needed to be governed and audited too.
- NIST AI RMF
The NIST AI Risk Management Framework (RMF) was created by the US government in 2023. It was developed through an open, consensus-driven process, and is completely voluntary.
In general, it emphasises the importance of initial risk-assessment, followed by governance, measurement, and monitoring. Though voluntary, it has been widely-seen as an excellent guide to AI best practices, even outside the US.
- ISO/IEC 42001
The ISO/IEC 42001 is the first truly worldwide collection of international standards for AI-based management systems.
Again, it focuses on governance, controls, monitoring, and other crucial areas for AI management. Similarly to the EU AI Act, it intends to make AI both auditable and certifiable, rather than purely functional.
Why AI Audit Skills Are Now Essential
As you can see, from the global regulatory trends are driving the need for the evaluation, monitoring, and audit of AI. It is critical that cyber security teams start looking at incorporating these practices, if they haven’t done so already.
AI systems rely on access to, and the usage of, sensitive data and infrastructure. Obviously, these are already the responsibility of cyber security teams. These teams will also need to respond and react if and when AI does fail, or make mistakes.
Internal auditors could bear some of the responsibility here. The problem, however, is that audits are only carried out periodically. AI systems, however, are in constant usage, with the potential for problems at any point. As such, they require persistent monitoring, which is much better suited to the cyber security team.
Finally, cyber security professionals are already likely carrying out activities like reviewing AI behaviour, assessing both its performance and the effectiveness of any regulations, and so on. These are essentially ‘audit’ tasks, suggesting that – even if it’s not official yet – auditing abilities are already key cyber security skills in 2026.
Securing Your Own Future
As AI continues to evolve at an ever increasing pace, traditional cyber security training might already not be enough. You could try to adapt to these changes yourself. The more comprehensive solution, however, is to get certification specifically designed to meet the need instead.
This not only helps you learn AI-related cyber security best practices, but also formally establishes you as being qualified in the field.
Here at ALC Training, we offer several detailed training courses to help you level up in this area.
Our ISO/IEC 42001 Foundation course provides a fantastic grounding in how Artificial Intelligence Management Systems (AIMS) work, specifically focusing on the aforementioned ISO/IEC 42001 international standards.
If you’d prefer to focus more on recognising, assessing, and responding to AI risks, opportunities, and impacts, our Advanced in AI Audit (AAIA) certification perfectly fits the bill.
Finally, there’s our Advanced in AI Security Management (AAISM) certification. This helps experienced IT security professionals navigate the evolving risks of AI, implement essential controls, and ensure effective and responsible organisation-wide AI usage.
Your Potential Career Paths
Whether you’re just getting going in your cyber security career, or are looking to adapt to recent industry changes, the rise of AI usage creates a range of different, AI-specific opportunities.
An AI Security Engineer, for example, focuses on securing AI systems, data pipelines, and infrastructures. He or she manages AI access, monitors its activity, and applies strict security controls.
AI Governance Leads take on a more strategic role. They are responsible for establishing AI frameworks and accountability, and ensuring AI usage is aligned with regulatory standards.
AI Risk Managers – as the title suggests – must identify and assess AI-related risks. They must then create strategies addressing these risks, and monitor both the risks and the success of their own solutions over time.
Finally, AI Auditors need to constantly assess AI controls, governance, and compliance. They must ensure the organisation is complying with standards such as the ISO/IEC 42001, and generally oversee all AI-related systems.
Looking Ahead
Widespread AI usage is already here, and has already changed cyber security enormously. Tasks and responsibilities which didn’t previously exist are now essential parts of cyber security work.
While this might sound intimidating, it also provides a whole range of new opportunities for cyber security professionals. Getting formal cyber security qualifications, such as the AI security training offered by ALC Training, won’t only help you learn about the AI-related changes of recent years, but will improve your employability, and ensure you stay relevant in this fast-moving landscape.