Data is increasing quickly on a global scale. Data growth will only get worse as a result of causes like the information explosion and the increasing interconnectivity of endpoint security automation. The massive influx of data will certainly have an impact on security teams.
Analysts are under pressure due to the vast volume of data they must sort through. As a result, alert fatigue is already an issue for analysts who are overwhelmed with security-related activities. Organizations are turning to automation to cut costs and boost production due to the ongoing lack of competent workers.
According to IBM’s Cost of a Data Breach 2022 report, companies with fully implemented endpoint security automation and artificial intelligence (AI) saved an average of $3.05 million during a breach compared to those without these tools. Additionally, it took 74 days less for businesses with fully implemented security AI and automation to find and stop a breach.
Today’s organizations must successfully manage the data deluge and protect what matters while keeping expenses down.
Let’s look at some ways that businesses can employ cybersecurity automation to get around the issues mentioned above.
Security automation reduces the need for human analysts to execute the time-consuming operation of manually sorting through warnings. Endpoint detection and response (EDR) solutions powered by AI automate detection and response, allowing businesses to react instantly. This removes the “human bottleneck” issue because automation allows for almost real-time detections.
To stop attacks in their track and stop more damage from spreading throughout the infrastructure, real-time detections are important. This is very important because attacks are happening faster.
In addition, AI-driven endpoint protection is independent of malware signature databases. By quickly stopping malicious processes, such as ransomware behaviors, it protects endpoint security automation. With automation, organizations can monitor an attack’s tactics in real time and reduce the likelihood that important data will be lost.
Attacks can be successfully stopped with immediate attack identification and automated reaction, or they might leave a compromised organization with costly cleanup and recovery processes.
Instead of having to manually go through hundreds of warnings coming in from multiple sources, intelligent automation gathers data and combines it into a single, high-fidelity alert that is compressed and grouped. Automation makes it possible for analysts to quickly understand an assault and take action by giving only the information they need and removing any unnecessary data.
Even a new analyst may properly manage threats using automation. Threats can be quickly solved using features like guided remediation and one-click remote killing. By using easily accessible search parameters, automated threat hunting also enables analysts to keep a danger-free environment even without database query skills.
A modern, AI-powered EDR solution serves as a virtual assistant for the human analyst, increasing organizations’ virtual headcount. Algorithms can manage the difficult task of alert handling by using security automation. This minimizes alert fatigue for analysts and significantly lowers the number of false positives.
Innovative automation, such as one-shot learning, benefits enterprises by making threat handling easy. Even if a particular alert is only noticed once, AI-powered automation picks up on the human analyst’s decisions and applies them automatically in the future. Costs associated with retraining and training are also kept to a minimum because machine learning helps maintain knowledge despite employee turnover.
In this way, intelligent automation successfully frees up analyst time so they may concentrate on deeper security investigations and other activities. An enhanced return on investment, improved team productivity, and more effective warnings have all been advantages for security teams.
There will come a moment when the human analyst is completely overwhelmed by the growth of data. Security teams must sift through significantly more data to discover anomalies as attacks get more specialized and focused.
Security automation at endpoints is necessary to combat this trend because many attacks take place there. They give analysts little time to react because these attacks are becoming more automated.
Organizations may create an ever-evolving baseline to future-proof endpoints against new threats, manage workloads, and keep costs down by implementing intelligent automation.
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