Incident Score: Analysis & Impact (CYBIBMSALNETMIC1790275657)

In this Rankiteo incident briefing, we review the Cyberhaven breach identified under incident ID CYBIBMSALNETMIC1790275657.

The analysis begins with a detailed overview of Cyberhaven's information like the linkedin page: https://www.linkedin.com/company/cyberhaven, the number of followers: 20263, the industry type: Computer and Network Security and the number of employees: 304 employees

After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 551 and after the incident was 407 with a difference of -144 which is could be a good indicator of the severity and impact of the incident.

In the next step of the video, we will analyze in more details the incident and the impact it had on Cyberhaven and their customers.

A newly reported cybersecurity incident, "Shadow AI: The Unseen Data Leakage Threat in Enterprises", has drawn attention.

A growing cybersecurity risk dubbed *shadow AI* is exposing organizations to costly data breaches as employees increasingly turn to unsanctioned generative AI tools for work tasks.

The disruption is felt across the environment, affecting Consumer generative AI tools (e.g., ChatGPT), personal devices/hotspots, and exposing Sensitive corporate data (customer records, financial models, internal documents, proprietary identifiers), plus an estimated financial loss of $5.39 million (average cost per breach, IBM 2026).

In response, moved swiftly to contain the threat with measures like Context-aware AI gateways, graduated enforcement (blocking/warnings/logging), data masking, and began remediation that includes Single control point for AI traffic, custom sensitivity rules, model-specific policies, SIEM integration.

The case underscores how teams are taking away lessons such as Traditional DLP tools (pattern matching) are ineffective for AI governance due to lack of context awareness. Blanket bans push employees to unsanctioned tools, relocating risk. Effective mitigation requires context-aware AI gateways, graduated enforcement, and custom sensitivity rules, and recommending next steps like Implement a single control point for all AI traffic (commercial and internal models), Adopt context-aware guardrails that assess data usage intent, not just patterns and Use graduated enforcement (blocking, warnings, logging, data masking).

Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.

The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.