TL;DR
- AI/ML model auditing is critical: security teams must evaluate data quality, bias, and vulnerabilities in AI systems.
- AI threat intelligence is a must-have skill, including defending against attacks like prompt injection and data poisoning.
- Traditional security methods aren’t enough—AI requires new, data-driven detection and response approaches.
- AI governance ensures compliance, ethical use, and ongoing monitoring of AI systems.
- The cybersecurity skills gap is shifting—your resume needs AI-centric capabilities to stay competitive.
Has AI ever felt like a bigger threat to cybersecurity jobs than it does now?
Everywhere you look, there are new articles about how artificial intelligence is “raising the stakes” or even causing a “reckoning” within the cybersecurity industry. It’s enough for many cybersecurity professionals to start pulling out their resumes and wondering what they’re going to do.
But there’s another truth hidden in all this hype: While AI may indeed be changing cybersecurity, those cybersecurity professionals willing to put in the work are changing with it. Like the move to cloud and other previous changes, the individuals who succeed in this new era will be the ones who know which new skills to learn and adopt.
So in this new age of AI-powered cybersecurity, what will it actually take to succeed?
AI/ML model auditing
As AI has made its rapid ascent from a niche tool to an essential one, the models that power the technology have proliferated. In the push to get ahead, many organizations are now rushing to adopt third-party models or even trying to build their own. However, if these models are flawed, any potential benefits can also come with real and present security dangers.
For example, models trained on insufficient or inaccurate datasets can produce skewed outputs. If proper guardrails haven’t been implemented, these models might also be violating privacy regulations or, even worse, have major vulnerabilities that can leave the organization exposed.
All this is why cybersecurity professionals need to know how to properly audit the AI tools and models their company deploys. This means assessing datasets for accuracy and quality, including hidden bias, as well as applying stress tests to analyze error rates in outputs and using adversarial testing to look for weaknesses in the models.
And professionals should not only know how to do this at the time of adoption, but also on a continuous basis. Just as AI models are constantly shifting with new data and inputs, so should the security professionals in charge of monitoring them.
AI threat intelligence
If model auditing represents the defensive side of AI security, the ability to actively monitor, detect, and mitigate AI threats is what you do on the offense.
What does this new class of threats look like? There are a range of ways AI can be used to attack. For instance, prompt injection is when bad actors place malicious instructions inside an AI prompt so that it will allow them to bypass any guardrails or security controls. Or there’s data poisoning, an even more subtle type of attack that takes place during training. Attackers simply feed the model manipulated data that influences how it functions over the long-term.
These types of attacks can be particularly effective since they are largely out of the reach of traditional cybersecurity practices. Because of this, it’s become essential for security professionals to learn new AI-powered methods for managing these threats.
Unlike conventional threat mitigation, which often relies on signature-based detection methods to locate threats, AI threat detection can automatically ingest and analyze massive amounts of data to detect and prioritize possible threats, detect patterns, and even take automatic steps to protect data, such as quarantining threats or triggering multifactor authentication.
Knowing how to properly use these tools, as well as step in to validate alerts and make informed judgment calls, has become a must-have skill for today’s security professionals.
AI governance
As organizations increasingly hand over tasks and even entire workflows to AI, the role of proper governance, always a foundational aspect of good security, has only become more important.
Just as the purpose of traditional governance is to ensure safety and compliance across an organization, AI governance refers to the process of building on oversight mechanisms that help align AI models to regulations, prevent them from committing privacy violations, and reinforce security best practices. In addition, effective AI governance should also help make models and tools ethical so that they foster trust.
But how should today’s cybersecurity experts actually do this? While there is no one established framework for building an AI governance structure, a responsible place to begin is often the data the AI trains on. Knowing how to examine this data so that the AI’s decision-making process is fair and unbiased can lay the foundation for an ethical system.
From there, security professionals should become familiar with how to set up processes to continuously monitor AI systems for safety and compliance, establish rules to ensure it follows any organizational guidelines and legal regulations, and incorporate transparency and accountability throughout.
Get ahead of the AI security skills gap
AI has already altered how cybersecurity functions in the real world. But this doesn’t mean that the role of cybersecurity professionals will become obsolete. In contrast, the emerging vulnerabilities and threats within AI have made security professionals more essential than ever.
But in order to take advantage of this shift, you need to develop the right set of skills. The cybersecurity resume that may have worked a year ago has changed. It’s time you change with it.
Explore Cybrary’s AI curriculum to get started.
