HomeCII/OTAutomate Routine Operational Workflows Using Generative AI

Automate Routine Operational Workflows Using Generative AI

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When you consider the daily responsibilities of security, compliance, identity, and management professionals, many of these tasks involve following repetitive processes. However, what if these processes could be automated with the help of generative artificial intelligence (GenAI)? This has the potential to revolutionize workflow efficiency and democratize knowledge within the entire security team, regardless of individual experience levels or familiarity with specific technologies or threat vectors. Instead of manually researching information, security operations center (SOC) analysts can utilize natural language processing embedded within GenAI models to ask questions and receive answers in a more natural format, tailored to their preferences.

It is important to note that GenAI is not meant to replace human expertise. Rather, its purpose is to assist analysts in responding to threats more efficiently through guided recommendations and best practices based on the organization’s own security data, known threat intelligence, and existing processes. This type of technology has the potential to greatly streamline and optimize operational workflows.

Before the automation of security, compliance, identity, or management workflows can occur, teams need to be able to trust that the information at their disposal is complete and accurate. This routine back-end work is very well-suited for automation because it is predictable and easily verified. By leveraging NLP and GenAI to automate simple help-desk tickets, incident reports, and other routine tasks, analysts can redirect their focus to more business-critical work.

Transparency is crucial in the deployment of GenAI models. Analysts need to be able to understand the sources from which the AI model pulled information and validate its accuracy in order to ensure that the recommendations being provided are correct. At Microsoft, ethical principles guide AI work, and the company has implemented constantly improving engineering and governance systems to uphold these principles. Transparency is one of the foundational principles of Microsoft’s Responsible AI framework, alongside fairness, reliability and safety, privacy and security, inclusiveness, and accountability.

There are numerous repeatable, multistep processes across security, compliance, identity, and management that are prime candidates for automation. For example, when investigating incidents, analysts often have to examine scripts, command-line arguments, or suspicious files that may have been executed on an endpoint. Instead of manually researching this information, AI models can help break down scripts and provide step-by-step analysis. This type of automated assistance saves time and upskills users who may not understand the complexities of analyzing a script or file.

Another use case for GenAI is in device management and compliance through conditional access policies. IT operations or help-desk support staff can use NLP prompts to quickly understand the compliance status of devices and provide step-by-step instructions on how to resolve any issues. This empowers individuals without direct experience in a particular tool to perform necessary tasks and avoid the need for escalation.

In conclusion, the potential of GenAI to revolutionize enterprise security, compliance, identity, and management processes is groundbreaking. By exploring new ways to apply GenAI in operational roles, practitioners can save time, gain new skills, and ensure that their time is focused on what matters most. Overall, GenAI has the ability to greatly enhance the efficiency and effectiveness of security teams.

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