Next generation ai lab

AI-powered remediation

More advanced applications and AI-powered tools today can process security alerts and offer users step-by-step remediation instructions based on input from the user, resulting in more effective and tailored remediation recommendations. 

Enhaned threat intelligence using generative AI

Generative AI is increasingly being deployed in cybersecurity solutions to transform how analysts work. Rather than relying on complex query languages, operations, and reverse engineering to analyze vast amounts of data to understand threats, analysts can rely on generative AI algorithms that automatically scan code and network traffic for threats and provide rich insights. 

Google’s Cloud Security AI Workbench is a prominent example. This suite of cybersecurity tools is powered by a specialized AI language model called Sec-PaLM and helps analysts find, summarize, and act on security threats. Take VirusTotal Code Insight, which is powered by Security AI Workbench, for example. Code Insight produces natural language summaries of code snippets in order to help security experts analyze and explain the behavior of malicious scripts. This can enhance their ability to detect and mitigate potential attacks. 

Stronger password security using LLMs

While scary to think of this power in the hands of hackers, AI also has the potential to improve password security in the right hands. Large language models (LLMs) trained on extensive password breaches like PassGPT have the potential to enhance the complexity of generated passwords as well as password strength estimation algorithms. This can help improve individuals’ password hygiene and the accuracy of current strength estimators. 

Dynamic deception capabilities via AI

While malicious actors will look to capitalize on AI capabilities to fuel deception techniques such as deepfakes, AI can also be used to power deception techniques that defend organizations against advanced threats. 

AI-assisted development

The goal is to reduce breaches, improve the nation’s cybersecurity, and reduce developers’ ongoing maintenance and patching costs. However, it will likely increase development costs.  

AI-based patch management

AI-based patch management systems can help identify, prioritize, and even address vulnerabilities with much less manual intervention required than legacy systems. This allows security teams to reduce risk without increasing their workload. 

Automated penetration testing

Penetration testing is a complex, multi-step process that involves gathering information about a company’s environment, identifying threats and vulnerabilities, and then exploiting those vulnerabilities to try to gain access to systems or data. AI can help simplify these parts of the process by quickly and efficiently scanning networks and gathering other data and then determining the best course of action or exploitation pathway for the pen tester. 

AI-powered risk assessments

AI is also being used to automate risk assessments, improving accuracy and reliability and saving cybersecurity teams significant time. These types of AI tools can evaluate and analyze risks based on existing data from a risk library and other data sources, and automatically generate risk reports.