AI presents cybersecurity challenges, opportunities

Advancements in AI technology are rapidly accelerating. For example, the differences between the first iteration of Open AI’s original ChatGPT in 2018 and today’s GPT-4 are monumental.

The same development speed applies to cybersecurity and the use of AI in operational and defensive environments.

The effectiveness and safety of large language models (LLMs), particularly in critical fields like cybersecurity, depend heavily on the integrity and quality of their training data. Persistent attempts by malicious actors to introduce false information pose significant challenges, potentially compromising the model’s outputs and, by extension, the security postures of those relying on these tools for information and guidance.

This underscores the importance of continuous monitoring, updating and curating sources used in training LLMs. Developing robust mechanisms to detect and mitigate the influence of incorrect information is key.

In security, AI is being integrated into security orchestration, automation and response (SOAR) products for straightforward tasks like modifying firewall rules or managing IP addresses and enhancing response capabilities.

Offensive Applications

From previous breaches from hashes via source code and customer information to armies of hackers that share information, there is an abundance of breached data and open-sourced information online.

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This means there is a good chance that AI can and will be used to change small things in previously used breaches to bypass security, based on signature detection, for example.

The most prominent ways in which threat actors are currently using generative AI tools include:

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