Generative AI (Gen AI) and Large Language Models (LLMs) are terms that are heard routinely today as nearly every major tech vendor is jumping on the hype bandwagon for generative AI. for security, the power of AI/ML goes far beyond a basic chatbot. In fact, it can play a significant role in dramatically improving cloud and SaaS threat detection and investigations.

As the volume and sophistication of cyber threats grow, manual approaches to security have no chance of ever keeping up. Gen AI helps with that challenge, bringing intelligence and automation to investigations. With organizations continuing to migrate their operations to the cloud and relying more heavily on SaaS applications, the need for sophisticated detection and investigation tools has never been more pressing.

Simplifying Complex Cybersecurity Processes

Gen AI has the power to help transform complex forensic investigations and threat hunting processes. One of the ways that Gen AI can help involves natural language interfaces for security tools. This approach allows users to interact with sophisticated security systems using plain English, rather than requiring expertise in specific query languages or complex graphical interfaces. By adding this layer of “plain-speak” communication, it can be easier for organizations to leverage powerful tools even for those without deep technical knowledge in cybersecurity.

This natural language approach serves several purposes:

• It bridges the expertise gap by making advanced security tools more user-friendly.
• It enables non-experts to perform initial threat hunting and forensic investigations.
• It simplifies the consumption of security information and reports.
• Abstracts the complexity of multiple query languages used by different security tools, providing a unified natural language approach.

Accelerating Threat Intelligence Processing

Another crucial application of AI in cloud and SaaS security is in accelerating the cycle from threat intelligence to actionable detection and response. 

This process commonly involves manually consuming threat intelligence reports, extracting relevant information, and integrating it into detection engines. With the advent of generative AI, much of this process can now be automated.

For instance, when a new threat report is published, AI can:

• Automatically extract Indicators of Compromise (IOCs) from the report.
• Identify and extract behavioral patterns described in the report.
• Transform this information into code that can be utilized.

The Gen AI powered automation significantly reduces the time to market for new threat intelligence. It means that insights from recent attacks or vulnerabilities can be rapidly understood. That can help with both protection as well as helping to accelerate incident response.

AI as a Cybersecurity Assistant

The concept of AI as an assistant (some vendors call it a "copilot”) in cybersecurity operations is gaining traction. In this role, AI acts as an intelligent assistant to human security analysts and investigators, augmenting their capabilities and improving efficiency.

Mitiga's approach to this concept involves developing systems that can act as a highly capable "intern" for security teams. These assistants can help with tasks such as:

• Sifting through large volumes of event data
• Answering specific queries about security events
• Providing frequency analysis of specific indicators within an environment

By offloading these time-consuming but necessary tasks to AI assistants, human analysts can focus on higher-level analysis and decision-making, ultimately speeding up investigations and improving resolution times.

How to Evaluate Gen AI for Cloud and SaaS Incident Response

Generative AI isn’t a silver bullet, it's not a solution that will magically solve problems and neither is it an existential threat to humanity. Generative AI doesn't solve everything but when used in the right places for the right purposes, it's a powerful capability. It's crucial to approach AI adoption strategically, focusing on solving specific, existing problems rather than implementing AI for its own sake.

When evaluating Gen AI for cloud and SaaS incident response, it's essential that it meets the following criteria:

Abstracts complexity. The technology needs to make existing processes easier, not more complex. With the use of natural language queries, users need to be able to execute sophisticated threat hunting and cybersecurity tasks that otherwise would require a specific skill set.

Accelerates threat intelligence integration. Time to response matters. Gen AI needs to be used to help automate the cycle from threat intelligence to actionable detection and response.

The integration of generative AI into cloud and SaaS detection and investigation processes represents a significant opportunity for organizations to enhance their cybersecurity posture. By using these technologies to improve accessibility, accelerate threat intelligence cycles, augment human capabilities, and enhance detection and analysis, organizations can better protect themselves against the modern threat landscape.