Incident Score: Analysis & Impact (FAS1772094320)
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Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Fastly's Cyber Attack and lateral movement inside company's environment.
- Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
- How Rankiteo’s incident engine converts technical details into a normalized incident score.
- How this cyber incident impacts Fastly Rankiteo cyber scoring and cyber rating.
- Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.
Full Incident Analysis Transcript
In this Rankiteo incident briefing, we review the Fastly breach identified under incident ID FAS1772094320.
The analysis begins with a detailed overview of Fastly's information like the linkedin page: https://www.linkedin.com/company/fastly, the number of followers: 65866, the industry type: Software Development and the number of employees: 1364 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 760 and after the incident was 740 with a difference of -20 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 Fastly and their customers.
A newly reported cybersecurity incident, "AI-First Organizations Face Longer Recovery Times and Higher Costs After Cyber Incidents", has drawn attention.
Fastly’s latest Global Security Research Report for Australia and New Zealand reveals that organizations identifying as 'AI-first' experience significantly longer recovery times and higher financial impacts from cybersecurity incidents compared to their peers.
The disruption is felt across the environment, plus an estimated financial loss of 135% higher than non-AI-first counterparts.
In response, and began remediation that includes Agentic discoverability (59%), API security (53%) and Web application firewalls (51%).
The case underscores how teams are taking away lessons such as AI adoption expands attack surfaces and introduces complexity for security teams, requiring modernization of security measures to address AI-specific threats like DDoS attacks on AI agents and AI-related blind spots, and recommending next steps like Invest in agentic discoverability, Strengthen API security and Deploy web application firewalls.
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.
MITRE ATT&CK® Correlation Analysis
Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Exploit Public-Facing Application (T1190) with moderate confidence (60%), supported by evidence indicating aI was directly exploited in their most recent incident (48% of AI-first orgs) and Supply Chain Compromise: Compromise Software Dependencies and Development Tools (T1195.002) with moderate confidence (50%), supported by evidence indicating agentic workflows and decentralized data flows introduce complexity. Under the Persistence tactic, the analysis identified Valid Accounts (T1078) with lower confidence (40%), supported by evidence indicating aI-related blind spots (42% of AI-first orgs) may enable account misuse. Under the Defense Evasion tactic, the analysis identified Hide Artifacts: Hidden Files and Directories (T1564.001) with moderate confidence (60%), supported by evidence indicating aI-related blind spots cited as contributing factors (42% of AI-first orgs) and Valid Accounts: Cloud Accounts (T1078.004) with moderate confidence (50%), supported by evidence indicating decentralized data flows introduce visibility challenges. Under the Impact tactic, the analysis identified Endpoint Denial of Service: Application or System Exploitation (T1499.004) with moderate to high confidence (70%), supported by evidence indicating operational disruptions (48%), slow load times or broken functionality (33%) and Resource Hijacking (T1496) with moderate to high confidence (80%), supported by evidence indicating aI scraping as material expense (75% of orgs), avg AUD $595K impact. Under the Exfiltration tactic, the analysis identified Automated Exfiltration (T1020) with moderate confidence (50%), supported by evidence indicating aI exploitation (48%) may involve automated data extraction. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Fastly Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/fastly/incident/FAS1772094320
- Fastly CyberSecurity Rating page: https://www.rankiteo.com/company/fastly
- Fastly Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/fas1772094320-fastly-cyber-attack-october-2025/
- Fastly CyberSecurity Score History: https://www.rankiteo.com/company/fastly/history
- Fastly CyberSecurity Incident Source: https://itbrief.com.au/story/ai-first-firms-hit-by-slower-costlier-cyber-recoveries
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf