Welcome to a world where Generative AI revolutionizes 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, or sounds. It has gained significant attention in recent years due to its ability to generate realistic and diverse outputs.
When it comes to security operations, Generative AI can play a significant role. It can be used to detect and prevent various threats, including malware, phishing attempts, and data breaches. Analyzing patterns and behaviors in large amounts of data allows it to identify suspicious activities and alert security teams in real-time.
Here are seven practical use cases that demonstrate the power of Generative AI. There are more possibilities out there of how you can achieve objectives and fortify security operations, but this list should get your creative juices flowing.
1) Information Management
Information security deals with a breadth of data that is constantly growing. Intake of new information is one challenge with managing information, but Generative AI can help distill that information. For example, there are a number of existing solutions for aggregating data, such as RSS feeds for news, but the problem of actually determining what information is useful and what isn’t still poses a problem.
Generative AI models have shown promising 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 condensed summary.
Another task where these capabilities can be useful is creating new policies in your organization’s language by providing existing documentation, such as policy documents.
2) Malware Analysis
Generative AI solutions, though they can’t solve everything, are extremely useful for security teams in performing malware analysis. AI models ‘learn’ to detect and recognize patterns within different types of malware, thanks to the massive amounts 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 detection. Malware that is plaintext (such as a decompiled executable, or a malicious python script) is generally best suited for this.
In some cases, Generative AI is even capable of de-obfuscating common techniques such as encoding schemes. Enabling the Generative AI solution to use external tools for de-obfuscation greatly enhances its capabilities. When properly applied to malware analysis use cases, Generative AI can help security teams account for lack of coding knowledge and quickly triage potential malware.
leverage external tools de-obfuscate on its own significantly improves its potential.
3) Tool Development
Generative AI can also rapidly increase a security team’s ability to produce useful and actionable tooling. Generative AI has shown a lot of potential for being capable of solving complex coding tasks. In general, with good prompting, it’s far easier for a developer to debug AI generated code than architect and recreate code from scratch. With capable, state-of-the-art models, debugging the generated code may not even be needed.
4) Risk Evaluation
Generative AI models are great at emulating a variety of personas and sticking to them. With the application of proper prompting techniques, the focus or behavior of the model can be directed to take on a particular bias. From there, a model can evaluate a variety of risk scenarios by emulating multiple personas, providing insight with different perspectives. By using a number of perspectives, Generative AI can be leveraged to provide thorough risk assessments and are much more capable of being neutral evaluators (via persona emulation) than a human would be. One can debate a model with an opposing persona and ensure that scenarios being evaluated are thoroughly red teamed.
5) Tabletops
Generative AI can be leveraged for tabletops in a variety of mechanisms. For example, provide a model with information from a recently released news article covering a new threat scenario, then have it generate a scenario that is adapted to your organization and its risks.
Generative AI can also be used for secretarial duties in a tabletop scenario, like ingesting the calendars of various stakeholders and scheduling an appropriate meeting time to conduct the tabletop.
Chat models in particular are well 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 with incident response. By creating workflows that include AI insights to analyze payloads associated with incidents, the mean time to resolve (MTTR) of incidents can be significantly reduced. It’s critical to use retrieval augmentation in these scenarios, as it’s likely impossible to train a model to account for every possible scenario. When you apply retrieval augmentation to additional external data sources, such as threat intelligence, you gain an automated workflow that is accurate and works to eliminate hallucinations.
7) Threat Intelligence
Using Generative AI to assist and enhance various threat intelligence tasks is an obvious application. Analyzing vast amounts of structured and unstructured data, such as indicators of compromise (IOCs), malware samples, and malicious URLs, generative AI can create insightful reports summarizing the current threat landscape, emerging trends, and potential vulnerabilities.
It can also synthesize reports on threat actor data with information about TTPs of various threat actors transforming data into actionable intelligence. For example, it can flag potential attack vectors, vulnerable systems, or specific detection mechanisms that could be implemented to mitigate those threats.
Generative AI holds immense potential for the future of cybersecurity. By harnessing its ability to process and analyze vast amounts of data, it’s capable of transforming how we detect, investigate, and respond to cyber threats. Read Understanding and Leveraging Generative AI in Cybersecurity to learn more.
Note: This article was expertly written and contributed by Jonathan Echavarria, Principal Research Scientist at ReliaQuest.
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