This post was created in tandem between Scot Terban and ChatGPT using the ICEBREAKER Intel Analyst created and trained by Scot Terban

The integration of Artificial Intelligence (AI) into information security tools marks a revolutionary shift in the cybersecurity landscape. This fusion promises unparalleled efficiency in threat detection, response, and management. However, it also brings forth significant challenges for cybersecurity analysts and engineers. about the benefits and potential pitfalls. There are technical impacts and there are societal impacts from these technologies that we are all likely to feel and soon, so be ready.

While the advent of this technology may make our lives a bit easier, they also will likely make us much less marketable as employees offering skill sets that the companies will want to keep over leveraging the new tech in lieu of paying people like us to do the work. Thus, the problem becomes what does the skilled worker do once their skills are, well, “less necessary” according to the corporate HQ?

Of course most of you out there will likely reach first for the accuracy and tendency for the tool (A.I.) to hallucinate or be wrong, but, people are wrong a lot too, but they are wrong for more money yearly than the AI is, so consider that in your mental calculations over all this.

So… Onto the pro’s and cons.

Positive Impacts

Negative Impacts

Job Displacement Concerns

• One of the most significant concerns is the potential for job loss. As AI becomes capable of performing more tasks, there is a fear that it may replace human roles, particularly in monitoring and basic threat detection.

Quality of Data and Training Sets

• The effectiveness of AI in cybersecurity heavily relies on the quality of the data and training sets used. Inaccurate or biased data can lead to false positives or negatives, undermining the reliability of AI systems.

AI Hallucinations and Incoherence

• AI, especially large language models (LLMs), can sometimes produce incoherent or factually incorrect information, known as ‘hallucinations.’ In the context of cybersecurity, such inaccuracies can lead to misguided decisions or overlooked threats.

Conclusions

The integration of AI into cybersecurity is a double-edged sword. While it brings significant advancements in efficiency and threat management, it also introduces new challenges. Cybersecurity professionals must adapt to these changes, focusing on areas where human expertise is irreplaceable, such as strategic decision-making and interpreting nuanced threats. Continuous training and updating of AI systems are essential to mitigate risks associated with data quality and AI incoherence.

With all that said, yeah, us humans have to roll with it and learn to leverage the tools in order to still be marketable. Find your niche and keep learning really is the only way to keep viable and employed as far as I can see. In fact, my other post today about Active and Passive collection and analysis in CTI is an important area to consider as you try to navigate all of this coming at us like a freight train out of control. This all seems inevitable to me and others who I talk to, so learn how to game it all out or, leave the playing field altogether.

K.