AI Enhanced Automated Incident Response and Forensics Workflow

Automated Incident Response and Forensics

1. Incident Detection

1.1 Monitoring Tools

Utilize AI-driven network monitoring tools such as Darktrace and Vectra to detect anomalies in network traffic that may indicate a security incident.

1.2 Alert Generation

Configure alerts based on predefined thresholds and AI analysis to ensure timely notification of potential incidents.

2. Incident Classification

2.1 Automated Triage

Employ AI algorithms to categorize incidents based on severity and type, using tools like IBM QRadar and Splunk Phantom.

2.2 Risk Assessment

Integrate risk assessment frameworks to evaluate the potential impact of classified incidents, leveraging AI for predictive analysis.

3. Incident Containment

3.1 Automated Response Actions

Implement automated containment strategies, such as isolating affected systems using tools like Palo Alto Networks Cortex XSOAR.

3.2 Communication Protocols

Establish automated communication protocols to inform stakeholders of containment measures using AI-driven communication tools.

4. Forensic Analysis

4.1 Data Collection

Utilize AI tools like EnCase and FTK Imager for automated data collection and preservation of evidence.

4.2 AI-Driven Analysis

Leverage machine learning algorithms to analyze collected data for patterns and indicators of compromise, using tools such as Maltego.

5. Remediation

5.1 Automated Patch Management

Integrate AI-driven patch management solutions like Automox to ensure timely application of security updates across the network.

5.2 Policy Enforcement

Utilize AI tools to enforce security policies and prevent recurrence of incidents, employing solutions like CrowdStrike Falcon.

6. Post-Incident Review

6.1 Reporting

Generate automated incident reports using AI-powered documentation tools, summarizing the incident response process and outcomes.

6.2 Continuous Improvement

Analyze incident data to identify areas for improvement in the incident response workflow, utilizing AI for trend analysis and reporting.

7. Training and Simulation

7.1 AI-Driven Simulations

Conduct regular training simulations using AI-based tools like Cyberbit to prepare the team for real-world incident scenarios.

7.2 Knowledge Base Updates

Continuously update the incident response knowledge base with lessons learned from incidents, aided by AI-driven documentation tools.