AI’s Role in Cybersecurity: A Glimpse into Tomorrow
Hackers, scammers, and other cybercrooks have always been at the cutting edge of tech development. Unsurprisingly, they embraced artificial intelligence (AI) early on, forcing the cybersecurity world to learn and adapt.
In this article, we explore AI’s impact on both sides of the cybersecurity medal. We also tentatively speculate on its future development and make a case for why AI alone won’t be enough for a comprehensive cyber defense – at least for the time being.
AI as a Cybersecurity Force Multiplier
Various aspects of AI are fundamentally reshaping how cybersecurity defenses identify and respond to threats.
For example, machine learning and behavioral analytics enable AI to sift through data and detect troubling trends far faster than human analysts, identifying patterns and anomalies much earlier. As a result, this helps prevent suspicious behavior from escalating into security incidents and provides appropriate, timely responses.
Additionally, access to historical data establishes a baseline for AI to recognize and act on threats. In turn, as new real-world data becomes available, AI can devise new countermeasures and predict incidents with greater accuracy. However, note that this largely depends on the data on which an AI tool is trained.
Building on this, natural language processing, a field of AI, goes beyond the coding level by understanding and analyzing human language. It’s invaluable for identifying increasingly sophisticated phishing emails. What’s more, it can help uncover unknown malware’s modus operandi by interpreting human-made comments or function names.
Furthermore, large language models (LLMs) coupled with generative AI augment human specialist capabilities. By processing vast amounts of data, LLMs can assist in quickly identifying patterns, summarizing complex reports, and providing insights that help human specialists make more informed decisions.
LLMs can also reduce the time needed for routine tasks, enabling human analysts to focus on higher-level, strategic concerns. This combination of AI and human expertise can significantly enhance efficiency and accuracy in cybersecurity, particularly in situations where human resources are limited.
Looking ahead, current trends point toward a future in which AI will become an even more integral part of effective cybersecurity responses. As AI systems become more reliable, they’ll receive more autonomy. Consequently, low-level tasks will be automated, allowing specialists to focus on tasks requiring intuition and ingenuity, such as threat modeling, incident investigation, and policy development.
AI as a Powerful Cybersecurity Risk Factor
Besides all this, there’s a flip side. If anything, AI in cybersecurity is currently in a reactive state. This means it’s developing more to address challenges as they emerge than anticipate and mitigate ones that don’t yet exist. As a result, AI often fails to predict and defend against emerging threats before they materialize, leaving systems vulnerable until a threat is identified and addressed by humans.
Generative AI’s impact on the evolution of cyber threats has been the most public and fruitful. An often-cited example is the explosion of phishing attacks that happened shortly after ChatGPT’s mainstream adoption. Such emails not only read more naturally but can bypass traditional security measures like DMARC with higher success.
The advancement of deepfakes is even more disturbing. It takes little skill to generate images, video, and sound that are increasingly harder to distinguish from the real thing. This has led to an increase in attacks targeting both high-profile individuals like C-suite decision-makers and everyday individuals.
Future developments will be multifaceted. We can expect the scale and sophistication of automated attacks to continue growing. Moreover, AI systems themselves will be increasingly targeted. Adaptive malware can already rewrite itself to avoid detection and will only become more dangerous. Lastly, AI will continue to lower the barrier of entry for novice hackers and speed up the rise of ready-made solutions like malware-as-a-service.
Where do Conventional Methods Fit In?
While AI’s rapid strides are undeniable, it’s still a single tool in the cybersecurity expert’s tool chest. Tried and tested cybersecurity measures still provide a necessary baseline and will remain relevant for a long time.
Password managers are an excellent example. AI has improved the success rate of credential-stuffing attacks that depend on stolen and reused login information. Nevertheless, it remains ineffective if you use the best password managers to create complex and unique passwords for individual accounts and secure them with 2FA.
Intrusion detection systems and next-gen firewalls still provide robust network security. They monitor traffic and prevent suspicious sources from reaching crucial network infrastructure.
It’s similar to VPNs. While AI might help attackers analyze network patterns, it still can’t access the underlying information if you first encrypt your connection with a VPN. In particular, USA VPNs offer advanced encryption protocols and high-speed server support to ensure your connection is secure and doesn’t negatively impact your network’s performance.
Conclusion
AI in cybersecurity remains in a tug-of-war as threats and solutions alike continue development at a rapid pace. The advancements are so fast that predicting anything past the next couple of years is thankless. Still, AI-assisted cybersecurity and its adversaries are bound to become even more intricate and surprising. We continue to observe these developments with bated breath.
