One of the most significant stories of this year should be the rapid expansion of Open AI's ChatGPT, with the possible influence of generative AI and LLMs - large language models affecting cybersecurity a major topic of concern.
The security risks that these emerging innovations may pose have been extensively discussed, ranging from concerns about disclosing sensitive business data to powerful self-learning algorithm to concerns that malicious actors may exploit them to significantly increase assaults.
Due to concerns over data safety, privacy and protection, some nations and companies have banned the use of AI technologies like ChatGPT. Clearly, generative chatbots powered by AI and LLMs provide a number of security issues.
But generative AI may benifit cybersecurity in many ways if security personnel has an urgent-needed assist in the battle against cyber crime.
Here are several ways that generative AI and LLMs may enhance security.
Scanning vulnerabilities and filtering
According to an AWS paper examining the cybersecurity implication of LLMs, AI models may be applied to greatly improve the detection and also filtering of safety vulnerabilities. In the article, AWS showed that Codex API performs well for scanning many kinds of programming languages which includes Java, JavaScript, C and C# for vulnerabilities. According to the paper, LLMs, including those from the Codex family, are anticipated to regularly be present in vulnerability scanners in the future.
Regarding filtration, generative AI have the capability to understand and give important contexts to the threat identifier which may otherwise be ignored by human security persons. For example, cybersecurity professionals may not be aware of TT1059.001, a method identifier inside the MITRE ATTCK architecture.
According to the AWS paper, ChatGPT is able to accurately identify the code as an MITRE ATTCK identifier and at the same time give explanation to the particular issue related to it, involving the usage of vicious PowerShell scripts.
Threat hunting
By using ChatGPT along with other LLMs to generate threat-hunting queries, safety defenders may improve effectiveness and reduce reaction times.
ChatGPT aids in quickly detecting and mitigating possible risks by generating queries for threat examination and detecting technologies such as YARA, making defenders concentrate on vital elements of their specific cybersecurity initiatives. This ability is crucial for keeping a strong security attitude in an everchanging threat environment.
AI will enhance safety for supply chain
By spotting probable vendor weaknesses, generative AI can help reduce the risks associated with supply chain safety. With the integration of OpenAI's GPT-4 platform with natural language global search, SecurityScorecard announced the debut of a lattest security rating platform to do just this.
According to the company, clients are likely to ask open questions regarding their vendor ecosystem and other aspects of their business environment and immediately receive responses that will assist them in making risk management choices.
Such questions may include "tell me which of my key suppliers were attacked in the last year" or "find out my 10 lowest in rating suppliers." According to SecurityScorecard, these kind of queries will produce information that will enable teams to make risk management choices rapidly.
Identifying generative AI text in attack
According to AWS, LLMs not only produce text but are also striving to recognise and watermark the AI-generated texts, which may become a general feature of email security softwares.
AWS stated that detecting AI-generated texts in attack can assist in detection of phishing emails as well as polymorphic code. It is also natural to expect that LLMs could quickly identify questionable email senders or their related domains, as well as be capable of checking whether links in the text lead to acknowledged malicious websites.
Leaders should guarantee generative AI are used safely
According to CSO of Gigamon, Chaim Mazal, generative AI and LLMs may be a dual-edged sword from the perspective of risk, like many technological advancements in the morden world, therefore it's crucial for leaders to make sure their team members are utilizing services responsibly and safely.
When considering the use of generative AI for security and defense, only utilize the data's dated, organized foundation as a starting point. You could ask it to explain its output if you're utilizing it to obtain any of the above-mentioned advantages. Offline editing by humans will improve, increase accuracy, and improve the usefulness of the result.
It will eventually become more natural for generative AI chatbots and LLMs to improve security and defenses, but how well cybersecurity postures are used AI and LLMs will ultimately depend on internal dialogue and reaction.
Generative AI/LLMs may offer a quicker, more effective solution to include stakeholders in addressing security challenges in general. Leaders must inform followers of possible risks while training them on how to use tools to promote organizational goals.
LLMs require human monitoring to guarantee appropriate operation and frequent upgrades to stay effective against attacks. And also, LLMs should be regularly tested and reviewed to find potential flaws or vulnerabilities and they require contextual awareness to give proper replies and detect security risks.