How can AI accelerate threat detection and response?

How can AI accelerate threat detection and response?

Gabriel Lima, Sales Engineer at Hillstone Brazil, explains how AI can enhance and speed up threat detection and response.

Gabriel Lima, Sales Engineer at Hillstone Brazil

Advanced persistent threats (APTs) are a reality in Brazil and around the world. It is common for these threats to operate unnoticed on a network for long periods, collecting information and exfiltrating sensitive data.

In the context of APTs, traditional security measures such as signature-based protection are no longer sufficient to combat evolving threats. That’s where the AI-driven Network Detection and Response (NDR) strategy comes into play. This approach elevates the digital maturity of the organization.

AI and Machine learning join forces

AI-based NDR solutions use Machine Learning algorithms to continuously monitor network traffic and detect anomalies. By establishing a foundation of normal traffic patterns, these solutions are able to identify deviations that may indicate potential threats. This advanced threat detection capability is crucial for identifying and mitigating criminal actions before they can cause significant damage.

The visibility provided by AI-based NDR solutions is another critical component of this picture. These platforms provide deep visibility through comprehensive monitoring dashboards, displaying real-time information about risk trends, IOC events and geographic distribution of threats. This granular visibility enables security teams to quickly identify and address potential threats while maintaining a strong security posture. Detailed monitoring of server traffic, threat intelligence and vulnerabilities ensures that security professionals have the information they need to make informed decisions.

Analytics capabilities are also a highlight of AI-based NDR solutions. These solutions provide rich data for analysis, including detailed threat intelligence, Packet Capture (PCAP) analysis, and recommended strategies for remediation of detected threats.

This data is used to understand attack vectors and identify compromised endpoints – even if the endpoint does not have defense solutions implemented on the computer or smartphone. By mapping threats to the MITRE ATT&CK framework, security analysts can better understand the techniques used by attackers and trigger appropriate mitigation actions.

Integration with other security platforms

Integration with other security solutions, such as Next-Gen firewalls and XDR platforms, further enhances threat responsiveness. The goal is to ensure continuous monitoring and protection of companies’ digital environments.

In this model, Machine Learning algorithms perform the analysis of huge amounts of data. The goal is to speed up the identification of patterns and anomalies that indicate potential threats. With real-time monitoring and detailed dashboards, the CISO can identify and block threats, protecting their organization’s business processes.

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