Welcome to a world where Generative AI is changing the field of cybersecurity.
Generative AI refers to the use of artificial intelligence (AI) techniques to create or generate new data, such as images, text, or sounds. It has gained a lot of attention in recent years due to its ability to generate realistic and varied outputs.
When it comes to security operations, Generative AI can play an important role. It can be used to detect and prevent a variety of threats, including malware, phishing attempts, and data breaches. Analyzing patterns and behaviors in large amounts of data allows it to 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 possibilities for how you can achieve those goals and strengthen security operations, but this list should get your creative juices flowing.
1) Information Management
Information security deals with a wide range of data that is constantly growing. Acquiring new information is an information management challenge, but Generative AI can help eliminate that information. For example, there are several existing solutions for gathering data, such as RSS feeds for news, but the problem of actually determining what information is useful and what is not still poses a problem.
Generative AI models show good capabilities in generating accurate and concise summaries of text. These models can be trained on large datasets of security-related information and learn to identify key information, extract important details, and generate a concise summary.
Another task where these capabilities can be useful is to create new language policies in your organization by providing existing documentation, such as policy documents.
2) Malware Detection
Generative AI solutions, although they cannot solve everything, are extremely useful for security teams in performing malware detection. AI models ‘learn’ to recognize and identify patterns within different types of malware, thanks to the vast amount of labeled data they are trained on. This acquired knowledge enables them to identify anomalies in previously unseen code, paving the way for more effective and efficient threat analysis. Plaintext malware (such as a decompiled executable, or a malicious python script) is usually best suited for this.
In some cases, Generative AI can even de-obfuscate common techniques such as encoding schemes. Enabling the Generative AI solution to use external tools for de-obfuscation will enhance its capabilities. When properly applied to malware analysis use cases, Generative AI can help security teams account for a lack of coding knowledge and quickly test for potential malware.
Leverage external tools de-obfuscate himself significantly improve his potential.
3) Tool Development
Generative AI can also rapidly increase a security team’s ability to create useful and actionable tools. Generative AI shows great potential to be able to solve complex coding tasks. In general, with good motivation, it is easier for a developer to debug AI generated code than to architect and recreate the code from scratch. With capable, state-of-the-art models, debugging generated code becomes unnecessary.
4) Risk Assessment
Generative AI models are very good at imitating different personas and retaining them. With the use of appropriate prompting techniques, the model’s focus or behavior can be directed to elicit a particular bias. From there, a model can evaluate different risk scenarios by simulating multiple personas, providing insight with different perspectives. By using multiple perspectives, Generative AI can be used to provide thorough risk assessments and is more capable of being a neutral evaluator (through persona emulation) than a human. A model can debate with an opposing persona and ensure that the scenarios being evaluated are fully integrated.
Generative AI can be used for tabletops in different mechanisms. For example, provide a model of information from a recently released news article that covers a new threat scenario, then turn it into a scenario tailored to your organization and its risks.
Generative AI can also be used for secretarial duties in a tabletop scenario, such as ingesting the calendars of various stakeholders and scheduling an appropriate meeting time to conduct the tabletop. .
Chat models are especially suited for tabletops, they can process tabletop data live and provide real-time input and feedback.
6) Incident Response
Generative AIs are excellent tools for assisting in incident response. By creating workflows that incorporate AI insights to analyze the payloads associated with incidents, the mean time to resolve (MTTR) of incidents can be significantly reduced. It is important to use retrieval augmentation in these scenarios, as it is probably impossible to train a model to account for every possible scenario. If you apply retrieval augmentation to additional external data sources, such as threat intelligence, you get an automated workflow that is accurate and works to eliminate hallucinations.
7) Threat Intelligence
Using Generative AI to assist and improve various threat intelligence tasks is an obvious application. Analyzing large amounts of structured and unstructured data, such as indicators of compromise (IOCs), malware samples, and malicious URLs, generative AI can produce insightful reports summarizing the current threat landscape, emerging as trends, and potential vulnerabilities.
It can also synthesize reports about threat actor data with information about the TTPs of various threat actors turning the data into actionable intelligence. For example, it can flag potential attack vectors, vulnerable systems, or specific detection mechanisms that can be implemented to mitigate threats.
Generative AI has a lot of potential for the future of cybersecurity. By using its ability to process and analyze large amounts of data, it can revolutionize how we identify, investigate, and respond to cyber threats. read Understanding and Using Generative AI in Cybersecurity to learn more.
Note: This article was expertly written and contributed by Jonathan EchavarriaPrincipal Research Scientist at ReliaQuest.