DeepSeek AI Breach Incident Score: Analysis & Impact (DEE3893138111125)

In this Rankiteo incident briefing, we review the DeepSeek AI breach identified under incident ID DEE3893138111125.

The analysis begins with a detailed overview of DeepSeek AI's information like the linkedin page: https://www.linkedin.com/company/deepseek-ai, the number of followers: 167839, the industry type: Technology, Information and Internet and the number of employees: 129 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 491 and after the incident was 406 with a difference of -85 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 DeepSeek AI and their customers.

A newly reported cybersecurity incident, "Risks and Impacts of Shadow AI in Corporate Environments", has drawn attention.

The article discusses the growing threat of 'shadow AI'—unsanctioned use of AI tools (e.g., ChatGPT, Gemini, Claude) by employees without IT oversight.

The disruption is felt across the environment, affecting Employee Devices (BYOD, laptops), Corporate Networks (via unauthorized AI agents) and Business Software (AI features enabled without IT knowledge), and exposing Personally Identifiable Information (PII), Intellectual Property (IP) and Proprietary Code, plus an estimated financial loss of Up to $670,000 per breach (IBM estimate); potential compliance fines (e.g., GDPR, CCPA).

In response, moved swiftly to contain the threat with measures like Network monitoring to detect unsanctioned AI usage and Restricting access to high-risk AI tools, and began remediation that includes Developing realistic acceptable use policies for AI, Vendor due diligence for AI tools and Providing sanctioned AI alternatives, and stakeholders are being briefed through Internal advisories on shadow AI risks and Training programs for employees and executives.

The case underscores how Ongoing (industry-wide trend, not a single incident), teams are taking away lessons such as Shadow AI introduces significant blind spots in corporate security, exacerbating data leakage and compliance risks, Traditional 'deny lists' are ineffective; proactive policies and education are critical and Vendor due diligence for AI tools is essential to mitigate third-party risks, and recommending next steps like Conduct a risk assessment to identify shadow AI usage within the organization, Develop and enforce an acceptable use policy tailored to corporate risk appetite and Implement vendor security assessments for all AI tools in use, with advisories going out to stakeholders covering IT and security leaders should prioritize shadow AI as a critical blind spot, Executives must align AI adoption strategies with security and compliance goals and Employees should be trained on the risks of unsanctioned AI tools.

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.

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 Valid Accounts: Cloud Accounts (T1078.004) with moderate to high confidence (85%), supported by evidence indicating employee use of unsanctioned AI tools (e.g., ChatGPT, Gemini, Claude) via corporate devices/networks and Phishing: Spearphishing Link (Fake AI Tools) (T1566.002) with moderate to high confidence (70%), supported by evidence indicating malicious fake AI tools designed to exfiltrate data listed under attack vectors. Under the Collection tactic, the analysis identified Automated Collection (T1119) with high confidence (90%), supported by evidence indicating aI features in legitimate business software enabled without IT approval collecting data autonomously and Data from Local System (T1005) with moderate to high confidence (85%), supported by evidence indicating pII, proprietary code, meeting notes inputted into unsanctioned AI models by employees. Under the Exfiltration tactic, the analysis identified Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol (T1048.003) with high confidence (90%), supported by evidence indicating data exfiltration via AI model training or third-party breaches to external servers and Exfiltration Over Command and Control Channel (T1041) with moderate to high confidence (75%), supported by evidence indicating agentic AI (autonomous agents acting without oversight) potentially transmitting data to C2. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (70%), supported by evidence indicating unsecured digital identities for AI agents could expose embedded credentials in config files. Under the Defense Evasion tactic, the analysis identified Indicator Removal: File Deletion (T1070.004) with moderate confidence (65%), supported by evidence indicating lack of visibility into employee AI tool usage suggests logs/artifacts may be cleared or obfuscated and Hide Artifacts: Email Hiding Rules (T1564.008) with moderate confidence (60%), supported by evidence indicating browser extensions with embedded AI may hide malicious traffic within legitimate AI communications. Under the Persistence tactic, the analysis identified Server Software Component: Web Shell (T1505.003) with moderate confidence (60%), supported by evidence indicating software vulnerabilities in AI tools (e.g., backdoors, bugs) could enable persistent web shells. Under the Impact tactic, the analysis identified Data Destruction (T1485) with moderate confidence (50%), supported by evidence indicating potential corporate inertia or stalled digital transformation as operational impact and Defacement: Internal Defacement (T1491.002) with moderate confidence (55%), supported by evidence indicating flawed decision-making due to biased/low-quality AI outputs could deface internal processes. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.