Generative AI Cybersecurity

Artificial intelligence (AI) has always been a part of cybersecurity, but recently the advent of generative AI has changed the industry — both for better and for worse. Threat actors have developed their own uses for generative AI. However, generative AI in cybersecurity has also made it easier for security teams to prevent and respond to incidents.

How Does Flare Offer Generative AI in Cybersecurity?

Does Flare have a generative AI cybersecurity solution?

Flare offers Threat Flow, the security industry’s first transparent generative AI application.

The murkiness of generative AI and large language models (LLMs) can make it difficult to trust outputs in a security context. Threat Flow can show you where the report outputs come from instead of pulling them out of what seems like a “black box.” This transparency not only enables research opportunities that start with a reliable source but also scaled research and reporting.

How is Threat Flow different from other LLM tools?

Concise and current summaries: Threat Flow leverages Flare’s extensive data collection to provide actionable intelligence that is relevant and summarized.
Intelligence tailored to your context: Adjust your prompts so that the threat intelligence meets your security team and organization’s needs.
Accelerated research and reporting: Time is precious, especially in cybersecurity. Threat Flow enables your security operations to become more efficient.
Third party validated: Flare and the EconCrime Lab at the University of Montreal collaborated in the development and quality assurance of Threat Flow, ensuring 98%* average accuracy compared to manual research & primary sources.

*98% average accuracy on summarization, but 96.22% average accuracy for extraction.

A Brief Overview of Generative AI in Cybersecurity

What is generative AI?

Generative AI is a type of artificial intelligence specifically designed to generate new content using existing datasets. These AI models can generate new data, simulate scenarios, and create synthetic datasets that can be used to enhance security measures, detect threats, or improve defenses. Generative AI models are trained on huge datasets to learn the nuances of natural language before being tuned for a specific task.

How is generative AI different from other forms of AI?

AI is not new in cybersecurity. AI and machine learning (ML) have been part of cybersecurity platforms and tools for years. Their algorithms have automated manual tasks, and spotted patterns in large amounts of data, helping to identify threats. However, generative AI works differently. Unlike traditional AI models, which are trained to recognize patterns in data, generative AI uses large language models (LLMs) to create text, images, or audio.

How can generative AI be used in cybersecurity?

Generative AI has opened up a world of possibilities, empowering security teams to sift through massive amounts of threat data from all over the world. Generative AI can automatically generate detailed threat intelligence reports based on the analysis of security data. This includes summarizing incidents, predicting potential impacts, and providing actionable insights. All of this saves your team time and resources.

Why is Generative AI so Important in Today’s Cybersecurity Landscape?

Is it important to pay attention to generative AI in cybersecurity right now?

Automate Your Threat Exposure Management

Integrate the world’s easiest to use and most comprehensive cybercrime database into your security program in 30 minutes.

The launch of OpenAI’s ChatGPT tool changed the world by bringing AI to everyone. However, threat actors began abusing the tool. Threat actors who might not have had the technical skills to code malware can ask generative AI to write ransomware or other malicious apps, or to create a believable message for a social engineering attack. To stay on top of these changing attacks, it’s important that security teams also take advantage of generative AI.

Is generative AI a threat to cybersecurity jobs?

One of the ever present concerns about AI of all kinds is that it will replace human jobs. However, it’s important to remember that like any other technology, generative AI is just a tool. Without humans, it can’t protect your organization against attackers. In the hands of a skilled security professional, AI is able to scan for threats, analyze information, and free up human analysts for higher-order tasks that require creativity and strategic thinking.

What are trends in generative AI cybersecurity?

Generative AI trends are changing fast as the technology develops:

Threat detection: The integration of generative AI and threat intelligence allows for better, faster, and more proactive threat detection.
Automation: Generative AI-driven automation streamlines incident response processes and due diligence (such as reading through vendor security questionnaires), freeling up human security professionals for more advanced work.
AI as a target: Generative AI is more than a tool; it’s also a target for bad actors. As AI is used more and more, it’s necessary to make sure it’s secured against attacks from threat actors.

How does generative AI in the wrong hands pose a threat to cybersecurity?

As with any tool, if you’re using it, so are the bad guys. The adaptability and power of generative AI enable malicious actors to automate, scale, and customize attacks in ways that were previously difficult or impossible:

AI-generated phishing emails: Attackers use GenAI to create highly convincing phishing emails that mimic the tone, style, and content of legitimate communications. These emails can be tailored to specific individuals (spear-phishing), making them more likely to deceive recipients.
Deepfake technology: Generative AI can generate realistic audio or video deep fakes of individuals, such as company executives or celebrities, to trick people into divulging sensitive information or transferring funds. This adds a powerful layer of deception to social engineering attacks.
Evasion attacks: Attackers use generative AI to create adversarial examples: small, imperceptible modifications to inputs that cause AI models to make incorrect predictions. For example, they could trick image recognition systems or spam filters into misclassifying malicious content as benign.
Poisoning attacks: In a poisoning attack, attackers use generative AI to generate tainted data that, when incorporated into a machine learning model during training, compromises its performance or causes it to behave in a way that benefits the attacker.
Polymorphic malware: Generative AI can be used to automatically generate new variants of malware that evade detection by traditional signature-based antivirus systems. Each time the malware is deployed, it can be slightly altered, making it difficult for security tools to recognize it as the same threat.
Malware obfuscation: Attackers use generative AI to create code that is difficult to analyze or reverse-engineer. This includes generating complex code structures, encryption routines, and other techniques that obfuscate the true purpose of the malware.