The Growing Impact of AI on The Practice of Cybersecurity

AI continues to make a transformational impact throughout the technology world. The FinTech world leverages it for various use-cases, including customer service chatbots and automated trading powered by machine learning. Other business sectors use this tech innovation to improve efficiency, ultimately lowering costs while improving a business’s bottom line.

The practice of cybersecurity also boasts a growing impact of AI and machine learning on its operations. Unfortunately, this also includes nefarious online criminals using the tech to pierce corporate firewalls and technical infrastructures. So let’s take a high-level overview of the emergence of AI on SecOps posture at the enterprise.

What is the Impact of AI and the Practice of Cybersecurity?

AI Powers Behavior-Based Tools Used to Detect Suspicious Network Activity

One of the leading methods where AI makes a difference in cybersecurity involves behavior-based detection of suspicious network activity. Machine learning models help in this regard, comparing current network usage with established norms. This approach makes detecting potential breaches and other suspicious actions a more effective process.

Explainable AI Helps Cyber Engineers Understand ML Models

Of course, machine learning models need to be trained to identify the differences between normal and suspicious network activity. However, using a trained model on the front lines of a corporate tech infrastructure sometimes seems like a proverbial “black box.” Essentially, it provides cybersecurity engineers with few insights on why it identified a certain action as potentially harmful.

Explainable AI hopes to change this perception wherever machine learning makes an impact across the tech world. Related to SecOps, it provides engineers and their management tangible information on why the model flagged certain network actions. These extra insights also provide peace of mind to tech leadership. More importantly, they lead to designing more effective ML models, leading to improved cybersecurity protection.

Reducing The Number of Cybersecurity False Alarms

Somewhat related to that previous topic, reducing false alarms in an AI-powered cybersecurity tool increases trust in the system. Again, this is another area where machine learning comes into play. It relies on AI techniques like casual reasoning and learned dependency models to identify cyber attack sources.

This approach improves the effectiveness of real-time analysis of network activity, ultimately reducing the amount of false alarm fatigue. In the end, it also allows network engineers to focus more on preventing and responding to actual cyber-attacks.

Are You Utilizing AI with Your Cybersecurity Practices?

If your company needs an influx of talented SecOps professionals, connect with the team at Redbud Cyber. As one of the top cybersecurity staffing agencies in the country, we provide exceptional candidates to protect your IT assets. Schedule a meeting with us to discuss your current hiring plans.

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