Welcome to the world where Generative AI is revolutionizing the field of cybersecurity.
Generative AI refers to the use of artificial intelligence (AI) techniques to generate or create new data, such as images, text, and audio. It has attracted a lot of attention in recent years because it can generate realistic and diverse outputs.
Regarding security services, Generative AI can play an important role. It can be used to detect and prevent a variety of threats such as malware, phishing attempts, and data breaches. By analyzing patterns and behaviors in large amounts of data, you can identify suspicious activity and alert security teams in real-time.
Here are seven practical use cases that demonstrate the power of Generative AI. There are many other possibilities for achieving your goals and strengthening your security operations, and this list should get you creative.
1) Information management
Information security deals with a wide range of data that continues to increase. Introducing new information is one of the challenges of information management, and Generative AI can help you extract it. Although there are many existing solutions for aggregating data, for example RSS feeds of news, the problem still arises of actually determining what information is useful and what is not.
Generative AI models have shown promising capabilities in producing accurate and concise summaries of text. These models can be trained on large datasets of security-relevant information and learn how to identify important information, extract important details, and generate summarized summaries.
Another task that these features can help with is providing existing documents, such as policy documents, to create new policies in your organization’s language.
2) Malware analysis
Generative AI solutions are not a cure-all, but they can be very helpful for security teams when performing malware analysis. AI models “learn” how to detect and recognize patterns within different types of malware thanks to the large amount of labeled data used to train them. The knowledge thus gained enables the identification of previously invisible code anomalies, paving the way for more effective and efficient threat detection. Plain text malware (such as decompiled executables or malicious Python scripts) is usually best suited for this.
In some cases, Generative AI can also deobfuscate common techniques such as encoding schemes. Enabling a Generative AI solution to use external tools for deobfuscation greatly enhances its capabilities. When properly applied to malware analysis use cases, Generative AI can help security teams fill coding knowledge gaps and quickly triage potential malware.
Deobfuscation on your own using external tools greatly improves your chances.
3) Tool development
Generative AI can also rapidly improve the ability of security teams to create useful and actionable tools. Generative AI has the potential to solve complex coding tasks. In general, with the right prompts, it is much easier for developers to debug AI-generated code than to design and rewrite the code from scratch. With a functional state-of-the-art model, debugging the generated code may not be necessary.
4) Risk assessment
Generative AI models are great at emulating and sticking to different personas. By applying appropriate prompting techniques, you can direct the model’s focus and behavior to a specific bias. From there, the model can evaluate different risk scenarios by emulating multiple personas and provide insights from different perspectives. By using many perspectives, generative AI can be leveraged to perform thorough risk assessments, allowing for the ability to be a much more neutral evaluator (via persona emulation) than humans. Masu. You can discuss the model with opposing personas and ensure that the scenarios being evaluated are fully red-teamed.
5) Table top
Generative AI can be leveraged on tabletops for a variety of mechanisms. For example, feed your model with information about recently released news articles that cover new threat scenarios and let it generate scenarios that are tailored to your organization and its risks.
Generative AI can also be used for secretarial tasks in tabletop scenarios, such as ingesting the calendars of various stakeholders and scheduling appropriate meeting times for tabletop meetings.
The chat model is particularly suited to tabletops, allowing you to process tabletop data live and provide real-time input and feedback.
6) Incident response
Generative AI is a great tool to aid in incident response. By creating workflows with AI insights to analyze payloads related to incidents, you can significantly reduce mean time to resolution (MTTR) of incidents. It’s probably impossible to train a model to consider every possible scenario, so it’s important to use search expansion in such scenarios. Applying search extensions to additional external data sources, such as threat intelligence, results in automated workflows that are accurate and work to eliminate illusions.
7) Threat Intelligence
Using Generative AI to assist and enhance various threat intelligence tasks is an obvious application. Generative AI analyzes vast amounts of structured and unstructured data, including indicators of compromise (IOCs), malware samples, and malicious URLs, to understand the current threat landscape, emerging trends, and potential vulnerabilities. Create insightful reports summarizing your gender.
You can also consolidate reporting on threat actor data and information on different threat actor TTPs to transform data into actionable intelligence. For example, you can flag potential attack vectors, vulnerable systems, or specific detection mechanisms that can be implemented to mitigate those threats.
what’s next
Generative AI holds immense potential for the future of cybersecurity. By harnessing the power to process and analyze vast amounts of data, you can transform the way you detect, investigate, and respond to cyber threats.read Understanding and leveraging generative AI in cybersecurity You can learn more.
Note: This article was written and contributed by an expert. Jonathan EchavarriaPrincipal researcher at ReliaQuest.