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10 Uses for Generative AI to Enhance Security Operations

Last Updated on December 3, 2023 by SPN Editor

Generative AI is a branch of artificial intelligence (AI) that focuses on the creation of new data, including images, text, or sounds. Over the past few years, it has garnered considerable interest due to its capacity to produce outputs that are both diverse and realistic. This type of AI uses specific techniques to generate this new data, making it a fascinating and rapidly evolving field. It’s like having an artist, writer, and musician all rolled into one, powered by the advanced capabilities of AI. This unique blend of creativity and technology is what makes Generative AI so intriguing and impactful.

In the field of security operations, Generative AI holds immense potential. It can be harnessed to identify and mitigate a variety of threats, including malware, phishing attempts, and data breaches. By scrutinizing patterns and behaviors in vast data sets, it can pinpoint suspicious activities and notify security teams instantly.

Here are ten practical applications that showcase the capabilities of Generative AI. While this list is not exhaustive, it should stimulate your imagination and help you explore how you can enhance security operations.

Generative AI in Information Management

The field of information security is inundated with an ever-expanding volume of data. While absorbing new information is a challenge in managing information, Generative AI can aid in processing that information. For instance, while there are numerous solutions for data aggregation, such as RSS feeds for news, the task of discerning useful information from the irrelevant remains a hurdle.

Generative AI models have demonstrated their ability to generate precise and succinct summaries of text. These models can be trained on extensive datasets of security-related information, enabling them to pinpoint key information, extract crucial details, and produce a compact summary.

These capabilities can also be employed to draft new policies in your organization’s language using existing documentation, such as policy documents.

Security Training Simulations

One of the most effective ways to prepare for cyber threats is through hands-on training. Generative AI can create realistic cyber-attack simulations for this purpose. It can generate a wide range of attack scenarios, each designed to test different aspects of a security team’s response capabilities. These simulations provide security teams with practical experience in dealing with various types of threats. This hands-on experience can significantly improve a team’s ability to respond effectively to real-world attacks, enhancing their overall preparedness and resilience

Malware Analysis

While Generative AI solutions may not be a panacea, they are invaluable for security teams in conducting malware analysis. AI models ‘learn’ to detect and recognize patterns within various types of malware, thanks to the vast amounts of labeled data they are trained on. This learned knowledge allows them to spot anomalies in previously unseen code, leading to more effective and efficient threat detection. This is particularly useful for plaintext malware, such as a decompiled executable or a malicious python script.

In some instances, Generative AI can even de-obfuscate common techniques such as encoding schemes. Allowing the Generative AI solution to leverage external tools for de-obfuscation significantly enhances its potential. When appropriately applied to malware analysis use cases, Generative AI can assist security teams in compensating for a lack of coding knowledge and swiftly triaging potential malware.

Tool Development

Generative AI can also expedite a security team’s ability to develop useful and actionable tools. Generative AI has demonstrated considerable potential in tackling complex coding tasks. Generally, it’s much easier for a developer to debug AI-generated code than to architect and recreate code from scratch. With advanced, state-of-the-art models, debugging the generated code may not even be necessary.

Generative AI in Risk Evaluation

Generative AI models excel at emulating a range of personas and adhering to them. With the application of proper prompting techniques, the model’s focus or behavior can be steered to adopt a specific bias. From there, a model can assess a variety of risk scenarios by emulating multiple personas, offering insight from different viewpoints. By employing a multitude of perspectives, Generative AI can be used to provide comprehensive risk assessments and are far more capable of being neutral evaluators (via persona emulation) than a human would be. One can engage in a debate with a model embodying an opposing persona to ensure that scenarios being evaluated

Automated Reporting

In the realm of cybersecurity, the volume of data that needs to be analyzed can be overwhelming. Generative AI can automate the process of creating security reports by analyzing data from various sources. It can sift through vast amounts of information to generate comprehensive reports that detail security incidents, threat analyses, and system vulnerabilities. This automation not only saves valuable time but also ensures consistency and accuracy in reporting. It eliminates the risk of human error and allows security teams to focus on more strategic tasks.

Tabletop Exercises

Generative AI can be a powerful tool for tabletop exercises. For instance, you could feed a model with details from a recently published news article about a new threat scenario, and then have it generate a scenario tailored to your organization and its risks. Generative AI can also take on administrative tasks in a tabletop scenario, such as ingesting the calendars of various stakeholders and scheduling a suitable meeting time to conduct the exercise.

Chat models, in particular, are well-suited for tabletop exercises. They can process tabletop data in real-time and provide immediate input and feedback.

User Behavior Analysis

Understanding user behavior within a system is crucial for detecting potential security threats. Generative AI can be trained to learn what constitutes ‘normal’ behavior within a specific system. It can analyze patterns and trends in user behavior and use this information to identify anomalies or deviations. These could be indications of a security threat, such as insider threats or compromised user accounts. By detecting these anomalies early, security teams can take swift action to mitigate potential threats and protect the system.

Incident Response

Generative AI is an excellent resource for aiding with incident response. By establishing workflows that incorporate AI insights to analyze payloads associated with incidents, the mean time to resolve (MTTR) of incidents can be significantly reduced. It’s crucial 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 create an automated workflow that is accurate and works to eliminate hallucinations.

Threat Intelligence

The application of Generative AI to assist and enhance various threat intelligence tasks is a clear use case. By 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 trends, and potential vulnerabilities.

It can also compile reports on threat actor data with information about the tactics, techniques, and procedures (TTPs) of various threat actors, transforming data into actionable intelligence. For instance, it can highlight potential attack vectors, vulnerable systems, or specific detection mechanisms that could be implemented to counter those threats.

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