How Generative AI Is Used in Cybersecurity

Overview of the AI Threat Landscape

Generative AI is a double-edged sword in cybersecurity. On one hand, it empowers internal cybersecurity teams with industry-leading threat analysis tools to fortify enterprise attack surfaces. But, on the other hand, threat actors also use GenAI tools to enhance their attacks, create more complex threat vectors, and identify vulnerabilities.

Over the past few years, alongside the proliferation of AI tools and their rising accessibility, the industry has seen an increase in the number of complex phishing cyberattacks and the deployment of sophisticated malware. Threat actors are actively using GenAI tools to bypass well-known security safeguards and develop precise spearphishing campaigns at a never-before-seen scale.

However, as these tools are also available to the internal cybersecurity teams defending against these threats, they have become a central pillar of modern attack surface protection.

The Role of Generative AI in Cybersecurity

To defend against the rising AI-driven cyberthreat, businesses are deploying generative AI in their own cybersecurity workflows. By drawing upon machine learning (ML) technology and deep learning, GenAI can process millions of data points, scan through potential indicators of compromise, and look for anomalies that are indicative of intrusion.

GenAI is particularly effective in:

• Network Traffic Classification (NTA): AI models in cybersecurity can collect, process, and analyze data in a fraction of the time it would take for human agents to do so. NTA uses this data to identify any indicators of compromise and alert human agents to investigate. Generative AI can transform these insights into succinct attack surface reports that security teams can use to better understand the architecture they protect
• Phishing Analysis: Generative AI models can analyze linguistic patterns in incoming emails to more accurately detect suspicious content and malicious intent, flagging these for human review
• Synthetic Attack Deployment: GenAI is extremely useful in creating simulated threats, allowing teams to conduct penetration testing at scale and identify potential vulnerabilities

Benefits of GenAI in Cybersecurity

Implementing generative AI in cybersecurity workflows is a major step forward in combating the rising cyber threat. Especially as attack vectors become AI-enriched, it’s even more important to both understand and use AI technology to keep enterprise environments safe from data breaches.

Here are some of the benefits of GenAI in cybersecurity:

• Improves Threat Response Speeds: GenAI can produce real-time threat summaries that are enriched with specific data analytics that improve threat response and better prepare cybersecurity teams
• Streamlines Testing and Vulnerability Assessment: GenAI systems can use Generative Adversarial Networks (GANs) to simulate attacks, triage emerging incidents, determine the specific threat vectors that cause an attack, and automate core processes in both blue and red teaming scenarios
• Provides Context-rich Summaries: Generative AI can rapidly produce incident reports, context-rich steps that security professionals should take to address a threat, and comprehensive cybersecurity playbooks to bring teams up to speed

Alongside GenAI, other forms of artificial intelligence and machine learning are also used in cybersecurity to automatically complete manual tasks, expedite workflows, and improve threat detection systems.

Challenges and Considerations in Implementing Generative AI

Although artificial intelligence in cybersecurity has a number of potential benefits, this emerging technology still requires an enormous amount of oversight to ensure it functions as expected.

Here are the main considerations a business should make when implementing generative AI into its workflows:

• Maintain a Human-in-the-Loop Structure: While GenAI security can certainly automate many core tasks, this should not be done without oversight from a human agent. Especially if your enterprise is new to artificial intelligence, then ensuring a cybersecurity agent checks outputs, revises actions, and corroborates data can help avoid costly AI hallucinations
• Follow Compliance Structures Where Possible: GenAI doesn’t have as many existing compliance frameworks to follow, but there are still major regulations that you should consider and comply with when using these tools. Baseline regulatory frameworks like the GDPR require you to handle the data you feed to AI tools carefully and regularly audit your systems to ensure you remain compliant
• Create GenAI Guardrails: Alongside monitoring GenAI tools with human-in-the-loop systems, your cybersecurity teams should also implement guardrails that limit AI usage, access to data, and visibility over your system. These practices will help prevent any negative outcomes of using these AI tools and protect your wider organization from potential disaster scenarios

When used correctly, artificial intelligence is a powerful tool that supplements cybersecurity teams to build a stronger, resilient security posture.