• By Kammil Mahajan
• Jan 09, 2024

AI Elevates Incident Response: Optimizing Security Operations for the Future

AI is making incident response more effective

AI can detect, prioritize and respond to incidents quickly, and can also help to prevent future attacks.

Here are some of the ways that AI is being used in incident response:

• Detecting incidents: AI can analyze vast amounts of data to identify patterns that may indicate an attack. This can help businesses to spot incidents early on before they have a chance to cause significant damage. SOAR (Security Orchestration, Automation and Response): SOAR platforms provide a centralized command and control system for incident response. They can automate many of the tasks involved in incident response, such as data collection, analysis, and communication. Anomaly detection tools use machine learning to identify patterns in data that deviate from normal behaviour. This can be used to detect potential incidents, such as intrusions or malware attacks. EDR tools monitor endpoints, such as laptops and desktops, for suspicious activity. This can help to detect and contain malware attacks. 
• Prioritizing incidents: AI can assess the severity of an attack and the potential impact on the business so that businesses can focus on the most critical incidents first. Machine learning algorithms to analyze historical data, threat intelligence, and current incident information. They can identify patterns and relationships to assess the severity of an incident based on the attack vector, targeted assets, magnitude of impact and urgency of containment.
• Responding to incidents: AI can automate many of the tasks involved in responding to an incident, such as data collection and analysis. Log management tools collect and analyze log data from various sources. This can be used to identify patterns that may indicate an attack. Network traffic analysis tools can be used to monitor network traffic and identify suspicious activity. This can help to detect and prevent network-based attacks. This frees up human resources to focus on more complex tasks.
• Preventing future attacks: AI can analyze historical data and threat intelligence to identify potential vulnerabilities. Threat intelligence provides information about known threats and attackers. This can be used to identify potential threats and develop mitigation strategies.

Some market-leading AI tools for prioritizing incidents:

1. Vectra Cognito: Vectra Cognito is a machine learning-based SOAR platform that prioritizes incidents based on factors such as attack vectors, targeted assets, magnitude of impact, and urgency of containment.
2. Securonix SOAR: Securonix SOAR integrates NLP capabilities to analyze textual data from incident reports, email conversations, and chat logs to prioritize incidents based on the presence of malicious language, sensitive data disclosures, or threats to disrupt operations.
3. IBM QRadar: IBM QRadar utilizes computer vision capabilities to analyze visual data from security footage, network traffic, and endpoint screenshots to identify anomalies and suspicious activity.
4. Palo Alto Networks Cortex XSOAR: Palo Alto Networks Cortex XSOAR is a SOAR platform that incorporates threat intelligence into its prioritization algorithms, providing a comprehensive assessment of incident severity.
5. Rapid7 InsightConnect: Rapid7 InsightConnect is a SOAR platform that integrates with various AI tools and processes, including machine learning, NLP, and computer vision, to automate the prioritization of incidents based on predefined criteria and AI recommendations.