Leveraging AI LLMs for Real-Time Threat Detection and Response

Data is the new gold. Every organisation and industry is strongly steeped in data, from market research all the way to grassroots implementation, data plays a significant part in everything. As a result of this, security has become all the more important.

Data breaches can cost millions in lost patents, penalties, law suits, databases, and also the loss of potential profits. In a recent report by IBM, it is estimated that an average of $4.88 million is lost for every data breach and the head of IBM Security, Kevin Skapinetz was quoted as saying that, “Businesses are caught in a continuous cycle of breaches, containment and fallout response. This cycle now often includes investments in strengthening security defences and passing breach expenses on to consumers – making security the new cost of doing business.”

Aside from the costs associated with data breaches, clients would also be less likely to engage in the services of a company that doesn’t have a stellar track record in terms of privacy protection, which leads to the loss of potential sales. Harvard reports a 7.5% loss in stock values following data breaches. The loss of confidence is also a hefty price to pay for these security breaches.

Hackers are also known to use these breaches as a means to extort money from corporations that require their databases by encrypting it. These are some of the risks associated with data breaches, and one of the many cybersecurity threats that linger in the world wide web, especially in today’s landscape whereby data is largely stored in the cloud. Fortunately, consumer protection in AI LLMs is steadily increasing.

The Application of AI-driven LLMs in the Interest of Consumers

AI LLMs, or Large Language Models, might sound extremely technical to many but the fact is that many more are utilising these technologies without realising. For instance, one of the most prominent AI LLMs at the moment is ChatGPT, a generative tool. It is used by college students to complete their homework assignments, or by your everyday Joe to research on a particular matter by using it as a search engine. The key feature of the tool is that it is able to consolidate information in a comprehensible manner while taking into consideration the vast amount of data available at its disposal. But how can a generative tool be used in cybersecurity?

Being able to analyse vast amounts of data is key. LLMs have the capacity to process network traffic, emails, system logs, and other data to detect any anomalies that may indicate a security breach. Aside from that, it can also formulate a response based on the threat by drawing on its predefined response strategies. This means that the software will be able to either isolate the event, alert security teams, or block the attack altogether.

Understanding the context of a data breach is heavily reliant on the company’s ability to feed the tool a specific dataset, as generative tools are only able to draw on what data they have access to. Much like the human brain that stores information, LLMs are not able to generate information that they do not “know”. Therefore, it is important for these tools to be so sophisticated that it is able to distinguish between genuine threats or false alarms. Unlike a human brain, software can process huge amounts of information in very little time, and it is not prone to human error. By analysing the available history and emerging threat patterns, AI LLMs are able to provide predictive insights into potential future attacks as well as neutralising current threats.

The positive implications of using AI LLMs

Imagine how a security team might work to protect consumers in real time - they would need to work around the clock, without taking a break in between as they rotate shifts. The price to keep eyes on all digital fronts would be exorbitant. Fortunately, AI is able to offer a feasible and cost-effective solution. The Ponemon Institute's Cost of Data Breach Report 2023 indicated that organisations and corporations that are utilising AI LLMs save up to $1.2 million per breach, a comparable improvement especially when considering the amount of money needed to fund a manual process running.

AI-driven security solutions are not only much easier on the wallet for corporations, it is also much more efficient. According to IBM, these contemporary methods are 27% quicker in pinpointing breaches as compared to traditional methods.

But simply identifying these breaches is not enough, the accuracy of detection is also very much higher, with false positives reduced by 40% when using AI LLMs. This is crucial to prevent burnout of security teams which will have to review each individual breach to ensure that they have all been properly dealt with.

Identifying and responding to cybersecurity threats in real time to protect consumers with AI LLMs is the future

Rapid analysis capabilities afforded by AI LLMs will push forward the boundaries of cybersecurity, mitigating threats before they cause any significant damage as well as providing cost saving solutions that require less manpower, maintenance, yet having more powerful output and systems. In the future, all organisations will be outfitted with their own AI LLMs directly into their existing cybersecurity mainframe such as Security Information and Event Management (SIEM) systems and other threat managing platforms.

There will also be new positions available for tech crews who are dedicated to ‘teaching’ these LLMs with the latest trends and most relevant histories - as mentioned before, LLMs operate on the database that it is outfitted with, which means that when dealing with the ever-evolving landscape of something like security, the datasets require frequent updates and fine-tuning in order to stay current, much like the antiviruses that we run on our computers or the constant updates that we download onto our phones, all of which include the latest security updates.

But this also means that AI isn’t here to replace a manual workforce, but rather enhance it overall as these platforms will be able to deliver faster results, while giving the human workforce the ability to focus solely on the most crucial component: ensuring that the appropriate threat response is administered.