Using AI to Improve Endpoint Security

With remote working now commonplace in the wake of the COVID-19 pandemic, businesses need to up their cybersecurity footprint. Notably, each remote employee’s home network and every smartphone accessing your company’s IT assets make attractive targets for cybercrime. In this scenario, improving endpoint security becomes an even more critical goal for your company’s SecOps team.

While the continued migration to Cloud-based tech infrastructures facilitates remote work, it also raises the stakes for cybersecurity. Thankfully, bots powered by AI are beginning to make a difference in the SecOps community. So let’s take a high-level overview of this emerging AI trend in cybersecurity.

How Can You Use AI to Improve Endpoint Security

Modern Cybersecurity Remains a Data-Intensive Process

Being able to analyze massive amounts of network data in real-time to identify potential threats requires a data-intensive approach. Needless to say, human SecOps engineers simply don’t have the time or processing power for this task. However, AI-powered bots provide the critical horsepower specifically for this purpose.

In fact, historical network data plays a critical role in training the machine learning models used in these bots. Because of this need, companies must consider archiving historical data to use for training ML models. In the end, expect these bots to work more effectively as a result.

AI Makes Endpoint Protection Possible in Today’s Cloud-based Business World

More companies transitioning to the Cloud increases the amount of network data needing analysis. Vasu Jakkal, corporate vice president for Microsoft Security, Compliance, Identity, and Privacy, commented on how AI makes this bot-based cybersecurity approach possible.

“AI is incredibly effective in processing large amounts of data to determine what is good and what’s bad. At Microsoft, we process 24 trillion signals every single day across identities and endpoints, and much more. Without AI, we could not tackle this,” said Jakkal.

AI-Powered Bots Detect Suspicious Network Activity

Many vendors at this year’s RSA Conference introduced endpoint security products leveraging bots powered by AI and machine learning. It nearly all cases, these tools rely on ML models to interpret massive amounts of network data. The models, trained by historical data as noted earlier, detect suspicious and irregular network activity.

In some cases, the bots even take proactive steps to mitigate the threat. The ultimate goal involves adopting a more automated and proactive approach to endpoint protection. This strategy also frees human SecOps engineers to focus on more value-added cyber-related tasks.

 

If your company need to hire cybersecurity talent, contact the team at Redbud Cyber. As one of the top SecOps staffing agencies in the country, we provide talented and experienced professionals to protect your IT assets. Schedule a meeting with us to discuss your current hiring plans.

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