In today’s cybersecurity landscape, the volume and variety of alerts generated by security tools can easily overwhelm even the most seasoned security operations teams. Between noise, false positives, and the difficulty of detecting advanced threats, the challenge isn’t just in spotting potential issues—it’s in understanding how they interrelate, prioritizing them, and identifying the root cause. AI-powered grouping and visualization offers a powerful solution, enabling cybersecurity teams to detect attack patterns, contextualize issues, and quickly zero in on the root cause.

The Need for Grouping and Visualization in Cyber Investigations

The average security operations center (SOC) contends with an overwhelming number of alerts daily, many of which are low-priority or false positives. Without automation tools to correlate and contextualize these alerts, analysts can waste hours on irrelevant events, while serious threats may go undetected. Grouping and visualization are essential tools to help cybersecurity teams:

• Reduce Alert Fatigue: By clustering related alerts, analysts can focus on entire incidents rather than isolated alerts, reducing alert fatigue and ensuring that critical issues are prioritized.
• Detect Multi-Stage Attacks: Advanced threats often unfold across multiple stages - such as reconnaissance, initial compromise, privilege escalation, and data exfiltration. Grouping helps connect these events based on MITRE models, allowing analysts to understand the full attack path.
• Accelerate Root Cause Analysis: AI-powered grouping can reveal connections between alerts, helping analysts trace the path back to the root cause, whether it’s a misconfiguration, compromised credential, or intentional misuse.
  

How AI Enhances Grouping in Cybersecurity Investigations

Traditional grouping in cybersecurity often relies on static rules or filters, which are manual, limited in scope, and lack the flexibility needed to detect advanced patterns. AI enables a dynamic, adaptive approach to grouping by continuously learning from new data and correlating events based on key attributes.

Approach to Composite AI-Powered Grouping:

• Clustering Similar Alerts: AI uses unsupervised clustering methods to group similar alerts based on features such as IP addresses, type of activity, and other alert metadata.
• AI-Based Event Classifier: Natural language processing (NLP) techniques and pre-trained language models extract and classify key event attributes from event fields, such as event type, severity, source/destination, and specific threat indicators.
• Sequential Pattern Recognition: Sequence-based models such as Recurrent Neural Networks (RNNs) detect temporal patterns, like repeated access attempts over a short time, indicative of brute-force attacks or gradual privilege escalation within a user’s session.
• Mapping to MITRE ATT&CK Framework: By correlating alerts to the MITRE ATT&CK framework, analysts gain insight into adversary methods and motivations. Grouping related events under common TTPs (tactics, techniques, and procedures) helps identify stages in an attack chain and informs proactive defensive measures.

Real-World Scenarios in AI-Powered Grouping and Visualization:

Case Study 1: Credential Sharing

In the scenario, multiple alerts related to the same user - Sarah Flores sharing credentials – are grouped to allow the security team to quickly investigate which applications are involved, identify recipients, and determine any common relationship among users. Grouping and visualization highlight potential credential sharing in a single, streamlined view.

Case Study 2: Identifying Poor Security Hygiene

For alerts indicating poor hygiene, such as permissions being directly assigned to the users instead of groups, AI-powered grouping and visualization can show which users and permissions are affected. Analysts can quickly determine the common patterns, identify high-risk assignments, and enforce stronger controls across the environment.

Case Study 3: MITRE ATT&CK-Based Analysis

In the scenario, grouping correlates alerts that individually may seem low priority but collectively indicate a toxic combination aligned with MITRE ATT&CK framework. By viewing these alerts holistically, analysts can detect patterns that signal an impending compromise of a vulnerable host, enabling them to act before a breach occurs.

‍

Conclusion: AiStrike Elevates Cyber Investigations with AI-Powered Grouping and Visualization

At AiStrike, we believe in the power of Composite AI to enhance cybersecurity at every level. Our platform combines machine learning, rule-based automation, NLP, and knowledge graphs into a cohesive solution that integrates with existing security tools—such as SIEM, CNAPP, and XDR—offering a seamless defense strategy for today’s complex threat landscape.

AI-powered grouping and visualization transform cyber investigations from reactive responses to proactive, insightful analysis. By enabling analysts to correlate events, identify patterns, and visualize relationships, AiStrike significantly reduces the time and effort needed to investigate and respond to incidents. With Composite AI, organizations can achieve a new level of investigative efficiency and accuracy, allowing them to stay ahead of attackers and minimize the impact of security breaches.