How do you leverage artificial intelligence or machine learning for incident handling?

1 Detection and Triage

Detecting and triaging security incidents quickly is essential to prevent further damage, but it can be challenging when dealing with high volumes of data, alerts, and logs from various sources. AI or ML can automate and improve the detection and triage process by using algorithms and models that can learn from data, recognize patterns, and classify incidents. For instance, AI or ML can filter out false positives and prioritize the most critical alerts, correlate and analyze data from different sources to identify anomalies, indicators of compromise, and root causes of incidents, as well as generate and assign tickets, tasks, and workflows to the appropriate incident handlers or teams based on the incident characteristics and the organization's policies.

2 Analysis and Investigation

Once an incident has been detected and triaged, further analysis and investigation is required to understand its scope, impact, and origin. This can involve collecting and preserving evidence, performing forensic analysis, tracing back the attack vectors, and identifying the attackers and their motives. AI or ML can expedite and enhance this process by utilizing techniques and tools such as natural language processing, data mining, visualization, pattern recognition, anomaly detection, similarity analysis, behavioral analysis, causal inference, and predictive modeling to extract and interpret relevant information from large and complex data sets such as logs, files, memory dumps, and network packets. These tools can also compare evidence and data against known or unknown threats, vulnerabilities, and attack signatures to reconstruct attack scenarios and timelines.

3 Containment and Remediation

Once you have analyzed and investigated an incident, you need to contain and remediate it to prevent further damage and restore normal operations. This can involve isolating and blocking the affected systems or networks, removing or repairing the compromised components, applying patches or updates, and implementing countermeasures or mitigations. AI or ML can help automate and optimize this process by using methods and solutions that monitor and control the incident lifecycle, status, and progress with feedback loops, adaptive learning, and dynamic decision making. Additionally, these solutions can execute and verify the containment and remediation actions using scripting, orchestration, and automation tools. Moreover, AI or ML can learn from the incident outcomes and feedback with reinforcement learning, self-healing, and continuous improvement.

4 Recovery and Improvement

The final step in incident handling is to recover and improve from the incident, which involves restoring affected systems or networks, evaluating the incident response performance, and identifying lessons learned and best practices. AI or ML can help enhance and sustain this process by assessing and measuring the incident impact, cost, and effectiveness with metrics, indicators, and benchmarks. Additionally, it can review and improve the incident response plan, process, and procedures with root cause analysis, gap analysis, and recommendations. Moreover, AI or ML can update and refine models, algorithms, and techniques using feedback, validation, and testing.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?