DeepSeek AI Breach Incident Score: Analysis & Impact (DEE5293552111725)

In this Rankiteo incident briefing, we review the DeepSeek AI breach identified under incident ID DEE5293552111725.

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 587 and after the incident was 464 with a difference of -123 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.

On 01 October 2024, OpenAI disclosed Data Leakage, Privacy Violation and Shadow IT Risk issues under the banner "Shadow AI Data Leakage and Privacy Risks in Corporate Environments (2024-2025)".

The incident highlights the systemic risks of 'Shadow AI'—unauthorized use of consumer-grade AI tools (e.g., ChatGPT, Claude, DeepSeek) by employees in corporate environments.

The disruption is felt across the environment, affecting Corporate AI Tools (e.g., Slack AI), Third-Party LLMs (ChatGPT, Claude, DeepSeek) and Enterprise Workflows Integrating Unsanctioned AI, and exposing Proprietary Code (e.g., Samsung 2023 incident), Financial Records (22% of UK employees use shadow AI for financial tasks) and Internal Memos/Trade Secrets, with nearly Unknown (potentially millions across affected platforms) records at risk, plus an estimated financial loss of Up to $670,000 per breach (IBM 2025); Potential GDPR fines up to €20M or 4% global revenue.

In response, teams activated the incident response plan, moved swiftly to contain the threat with measures like Blanket AI Bans (e.g., Samsung 2023), Employee Training (e.g., Anagram’s compliance programs) and AI Runtime Controls (Gartner 2025 recommendation), and began remediation that includes Centralized AI Inventory (IBM’s lifecycle governance), Penetration Testing for AI Systems and Network Monitoring for Unauthorized AI Usage, while recovery efforts such as AI Policy Overhauls, Ethical AI Usage Guidelines and Incident Response Playbooks for Shadow AI continue, and stakeholders are being briefed through Public Disclosures (e.g., OpenAI’s transparency reports), Employee Advisories (e.g., Microsoft’s UK survey findings) and Stakeholder Reports (e.g., IBM’s Cost of Data Breach 2025).

The case underscores how Ongoing (industry-wide; no single investigation), teams are taking away lessons such as Shadow AI is pervasive (90% of companies affected, per MIT 2025) and often invisible to IT teams, Employee convenience trumps compliance (58% admit sharing sensitive data; 40% would violate policies for efficiency) and AI governance lags behind adoption (63% of organizations lack frameworks, per IBM 2025), and recommending next steps like {'technical': ['Implement AI runtime controls and network monitoring for unauthorized tool usage.', 'Deploy centralized inventories to track AI models/data flows (IBM’s lifecycle governance).', 'Enforce strict data retention policies (e.g., immediate deletion of temporary chats).', 'Conduct penetration testing for AI systems and prompt injection vulnerabilities.', 'Use adaptive behavioral analysis to detect anomalous AI interactions.']}, {'policy': ['Develop clear AI usage policies with tiered access controls (e.g., ban high-risk tools like DeepSeek).', 'Mandate regular training on shadow AI risks (e.g., Anagram’s compliance programs).', 'Align AI governance with GDPR/CCPA requirements (e.g., data minimization by design).', 'Establish incident response playbooks specifically for AI-related breaches.']} and {'cultural': ['Foster innovation while setting guardrails (Gartner’s 2025 approach: ‘harness shadow AI’).', 'Encourage reporting of unauthorized AI use without punishment (to reduce hiding behavior).', 'Involve employees in vetting AI tools for enterprise adoption (Leigh McMullen’s suggestion).', 'Highlight real-world consequences (e.g., $243K voice-cloning scam) in awareness campaigns.']}, with advisories going out to stakeholders covering CISOs: Prioritize AI governance frameworks and employee training, Legal Teams: Audit AI data retention policies for compliance conflicts and HR: Integrate AI usage into acceptable use policies and disciplinary codes.

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 high confidence (95%), with evidence including employees bypassing sanctioned tools to use DeepSeek’s consumer-grade LLM, and employee Bypass of Sanctioned Tools under vulnerabilities and Exploit Public-Facing Application (T1190) with high confidence (90%), with evidence including vulnerable database operated by DeepSeek, exposing highly sensitive corporate data, and misconfigured AI Databases (e.g., DeepSeek) under attack vectors. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Credentials In Files (T1552.001) with high confidence (90%), with evidence including exposure of authentication credentials and backend infrastructure details, and secret Keys/Backend System Details under compromised data and Unsecured Credentials: Private Keys (T1552.004) with moderate to high confidence (85%), supported by evidence indicating secret API keys, backend system details exposed in breach. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (95%), with evidence including chat histories, secret API keys, backend system details, proprietary workflows harvested, and data from Local System via misconfigured DeepSeek database and Data from Information Repositories: Sharepoint or Code Repositories (T1213.002) with moderate to high confidence (80%), with evidence including proprietary code, internal memos, financial forecasts shared via DeepSeek, and code Repositories listed under exposed file types. Under the Exfiltration tactic, the analysis identified Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted/Obfuscated Non-C2 Protocol (T1048.003) with moderate to high confidence (85%), with evidence including unmonitored data exfiltration via AI prompts, and data exfiltration confirmed (e.g., DeepSeek, Slack AI, Shadow AI leaks) and Exfiltration Over Command and Control Channel (T1041) with moderate to high confidence (75%), with evidence including data sold on dark web (e.g., chat histories, proprietary data), and aI-trained datasets sold by initial access brokers. Under the Defense Evasion tactic, the analysis identified Indicator Removal: File Deletion (T1070.004) with moderate to high confidence (70%), with evidence including default Data Retention Policies in LLMs (e.g., OpenAI’s 30-day deletion lag), and silent breaches are more damaging such as firms may not realize data is compromised and Impair Defenses: Disable or Modify Tools (T1562.001) with moderate confidence (65%), with evidence including lack of AI runtime controls and network monitoring for unauthorized tool usage, and no Runtime Controls for AI Interactions under root causes. Under the Reconnaissance tactic, the analysis identified Gather Victim Host Information (T1592) with high confidence (90%), with evidence including years of prompt-engineered data, including employee thought processes, financial forecasts, operational strategies, and high-value intelligence for threat actors such as ‘master key’ to internal systems and Gather Victim Identity Information: Email Addresses (T1589.002) with moderate to high confidence (80%), with evidence including spear-phishing, insider impersonation risks from exposed chat histories, and employee Health Records and Internal Memos listed as compromised data. Under the Impact tactic, the analysis identified Data Destruction (T1485) with moderate confidence (60%), with evidence including potential GDPR fines up to €20M or 4% global revenue, and loss of Intellectual Property and Erosion of Competitive Advantage and Resource Hijacking (T1496) with moderate to high confidence (70%), with evidence including aI-trained datasets sold on dark web for follow-on attacks, and exploitation of AI Training Data under threat motivations. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.