HomeArtificial IntelligenceCan generative AI help close the cybersecurity resource gap?

Can generative AI help close the cybersecurity resource gap?

At every cybersecurity event I attend, CISOs and other security professionals discuss the challenges of finding, hiring, and maintaining a team of cybersecurity professionals. Not surprisingly, ISC2 recently revealed that there’s a labor shortage of just about 4 million people within the industry – and the trend is rising.

Anything we are able to do to cut back the burden on our security analysts and engineers means more time to mitigate cyber risks, which is a giant win. Fortunately, generative AI might help address this skills shortage and positively impact cybersecurity. Gen AI can enable:

Lower the bar to start

The cybersecurity industry often requires specialized training and certifications – requirements that may discourage people from finding and securing jobs in the sphere. Gen AI could be applied to technical documentation and other cybersecurity information to create more dynamic training that meets latest employees where they’re, fairly than creating training material for a selected user background and requiring latest employees to do this work before they join an organization.

Creating more user-friendly documentation

Today, there are pages and pages of technical documentation for nearly every cybersecurity tool available on the market. Users often feel overwhelmed and must depend on vendors to coach them on learn how to use their solutions. Gen AI could be used to process the identical information and distill it into something very precise and meaningful to the user.

Let's say the client must know learn how to execute a question on this tool. Instead of the client spending hours combing through technical documentation, security teams can use Gen AI to quickly provide the three to 5 steps the client needs to finish this motion. Gen AI might help corporations create more user-friendly documentation so customers can access information faster and reduce their time to implementation and risk reduction.

Reducing the danger of burnout

Security professionals often experience burnout while performing tedious tasks comparable to looking for documentation and logging their processes and results. Large Language Models (LLMs) are specifically designed for analyzing and synthesizing data. This may very well be applied to the corporate's great amount of internal and external documentation, reducing the time security analysts need to search out information to do their work and communicate with their broader team. By reducing the “busy work” burdening teams, the main target can then be on spending more time on remediation and risk mitigation.

Stay up up to now with the most recent news and research

One area that I consider would greatly profit from genetic AI is continuous education. As we all know, cybersecurity threats, attack vectors, and malicious actors are continuously changing. However, security professionals are all too often busy handling incidents, writing policies, and designing architectures. They don't have the time to find out about what's happening outside their organization. Gen AI could be used to gather and distill information relevant to an industry from a corporation's trusted sources, from favorite trade publications and industry associations to other favorite research and resource sites.

Improving cross-team organizational safety communication

Organizational training is one other ongoing challenge for cybersecurity teams. The time required to synthesize phishing information may very well be reduced through genetic AI and automation. For example, if a corporation detects persistent phishing attempts, Gen-AI could analyze the text and create custom messages based on each department's function to best equip them to mitigate risk – giving the safety organization time back and reducing the variety of incidents.

Take the time to construct the precise guardrails

These are only just a few ways in which genetic AI could be used to bring more expert people into the cybersecurity field and increase the performance of those already working in the sphere today. I'm excited to see what other use cases we'll profit from in the long run.

But with all cutting-edge technology, we must fastidiously consider how it’ll be used and put the precise policies and other protections in place.

For example, one suggestion we make to all of our customers is that when corporations select a Gen AI platform, they need to enter right into a paid contractual relationship in order that the provider can provide advice on the tool and help troubleshoot any issues. Why? Because we shouldn't let our security teams go to ChatGPT alone and create their very own accounts where there isn’t a visibility or control; We also need the power to audit our suppliers and make sure the transparency of their processes.

Organizations also needs to train generational artificial intelligence using documentation, data, and other source-only information. Finally, at all times keep in mind that while AI can do plenty of good for the generation, the whole lot it puts out must be reviewed and executed by a human.

Gen AI is already transforming the cybersecurity industry and might be instrumental in closing the cybersecurity resource gap.


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