Strengthening Cybersecurity with ChatGPT-Based Systems

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Today’s Highlights:

• Strengthening Cybersecurity with ChatGPT-Based Systems🔐

• Favorite Product of the Day: SaaS Growth Kit💡

Fortify and Strengthen Cybersecurity with ChatGPT Applications

Cybersecurity defense has become an increasingly vital aspect for any company to strengthen and improve due to attackers getting as relentless as ever.

More and more intrusions have become part of numerous companies’ problems, and security operations (SecOps) teams have had to bend over backward and take further action to ensure the companies they work for are thoroughly protected.

What if there was a way to relieve the burdens SecOps teams have to go through, diminishing the chance of having them overworked and burnt out, causing human errors, misconfigurations, or even more fatal cyber consequences?

This is what chief information security officers (CISOs) and chief information officers (CIOs) from a few companies are trying to find out, and they have had help from ChatGPT.

Secure LLMs

Plenty of CISOs and CIOs have been piloting and using ChatGPT-based systems to test whether or not they could make their companies’ data governance more efficient and secure.

Though SecOps teams used ChatGPT-based systems to conduct their cybersecurity experiments, they have asserted that companies that want to try this method should avoid using ChatGPT itself, as it is an open-source large language model* (LLM) that never forgets any data, information, or threat analysis. Using ChatGPT could pose confidentiality risks, and so companies could either evaluate cybersecurity vendors that have LLMs, instead.

An example would be Airgap Networks, a firewall company that introduced ThreatGPT, an AI-powered threat detection and response asset. ThreatGPT can provide SecOps teams with threat insights using graph databases and GPT-3 models. Other generative AI options include Google Cloud’s Security AI Workbench, Microsoft’s Security Copilot, Charlotte AI, and Crowdstrike.

*Large language model: a type of artificial intelligence (AI) that has been trained on vast amounts of text to understand existing content and generate original content.

Streamlining SOC

As previously mentioned, the security of one’s company is in the hands of the SecOps team, and so it is of the utmost importance to keep the team in check. One of the ways to achieve this, as tested by a leading insurance and financial services firm, is by checking ChatGPT-based systems’ effectiveness in performing basic security policies and procedures.

The firm ran a Proof of Concept* (PoC) on ChatGPT to see if it could analyze cybersecurity incidents and, at the same time, make immediate and long-term threat response recommendations. The firm has also tested whether ChatGPT could give other basic IT advice. If achievable by these systems, SecOps teams could focus more on security threats and vulnerabilities with severity levels that require higher-level skills. As a result, too much workload that could lead to burnout can be avoided.

Aside from, at the very least, slightly lightening the load of SecOps teams, what else can ChatGPT potentially do?

*Proof of Concept (PoC): the realization of a certain method or idea in order to demonstrate its feasibility or practical potential.

IoC Fine-Tuning

The overworking of SecOps teams can lead to manual misconfigurations and errors, which have been known to be one of the leading causes of cybersecurity breaches but are often unavoidable in enterprises operating at scale. Due to this, CISOs, essentially the leader of SecOps teams responsible for developing security strategies, have been testing whether ChatGPT can recommend configuration improvements.

To do so, CISOs made ChatGPT interpret data indicators of compromise* (IoCs). IoCs allow the SecOps team to detect intrusion attempts, analyze malware behaviors, and find actionable threat intelligence. By doing so, the team will be able to improve the company’s incident response and strategies. By having ChatGPT systems interpret such data, it is hoped that they could fine-tune configurations without the need for manual work and minimize the false positives often caused by less-than-optimal IoC alerts.

*Indicators of Compromise (IoCs): forensic evidence of potential intrusions on a host system or network.

SIEM Automation and Secure Analyses

Another way in which SecOps teams believe ChatGPT-based systems could help improve cybersecurity is through the automation of security information and event management (SIEM) queries. These queries provide real-time security monitoring, which allows SecOps teams to track and analyze events that could potentially lead to actionable threats.

Though they are tremendously useful for cybersecurity measures, the process of creating and updating SIEM queries requires SecOps teams to log data from real-time events. A SOC analyst from a major financial services firm has stated that SIEM queries can quickly take up much of their time, essentially turning into about 30% of their daily workload. Automating these processes would potentially free up at least a day and a half a week, giving SecOps teams more time to carry out more intricate work.

Lastly, cybersecurity researchers have pushed ChatGPT to see if it could handle complex secure codes that would usually require higher-level skills. In a published IoC detection experiment by Victor Sergeev, ChatGPT successfully identified two malicious processes out of 137 benign ones without providing any false positives. This experiment has proven that ChatGPT-based systems can potentially create more thorough, accurate, and secure code analyses.

From the experiments and trials SecOps teams have conducted using ChatGPT-based systems, it is apparent that these systems exist to assist said teams instead of replacing them. With the help of LLMs, companies can create more efficient and automated security systems as well as streamline their employees’ workloads and responsibilities.

Favorite Product of the Day

SaaS Growth Kit

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