AI Driven Cybersecurity Threat Detection and Mitigation Workflow

Cybersecurity Threat Detection and Mitigation

1. Threat Identification

1.1 Data Collection

Utilize AI-driven tools such as Splunk or IBM QRadar to aggregate data from various sources, including network traffic, user activity logs, and system alerts.

1.2 Anomaly Detection

Implement machine learning algorithms to analyze historical data and identify deviations from normal behavior. Tools such as Darktrace can be utilized for real-time anomaly detection.

2. Threat Analysis

2.1 Risk Assessment

Employ AI-powered risk assessment tools like RiskLens to evaluate the potential impact of identified threats on critical infrastructure.

2.2 Threat Intelligence

Integrate threat intelligence platforms such as Recorded Future to provide context around identified threats, leveraging AI to correlate data from multiple sources.

3. Threat Mitigation

3.1 Automated Response

Utilize AI-driven automation tools like Palo Alto Networks Cortex XSOAR to initiate predefined responses to detected threats, minimizing human intervention.

3.2 Patch Management

Implement AI-based solutions such as Automox for automated patch management to ensure that vulnerabilities are addressed promptly.

4. Continuous Monitoring

4.1 Real-Time Surveillance

Deploy AI-enhanced security information and event management (SIEM) systems like Azure Sentinel to monitor network activity continuously for suspicious behavior.

4.2 Feedback Loop

Establish a feedback mechanism where AI systems learn from past incidents to improve future threat detection capabilities. Tools like Elastic Security can facilitate this process.

5. Reporting and Compliance

5.1 Incident Reporting

Utilize reporting tools integrated with AI capabilities to streamline incident reporting and documentation processes, ensuring compliance with industry regulations.

5.2 Compliance Audits

Leverage AI-driven compliance tools such as LogicGate to automate audit processes and ensure adherence to cybersecurity standards.