DeepSeek AI
Company Details
Linkedin ID:
deepseek-ai
Website:
Employees number:
129
Number of followers:
167,839
NAICS:
513
Industry Type:
Homepage:
deepseek.com
IP Addresses:
0
Company ID:
DEE_6226520
Scan Status:
In-progress
DeepSeek AI Company CyberSecurity Posture
deepseek.com
DeepSeek (深度求索), founded in 2023, is a Chinese company dedicated to making AGI a reality. Unravel the mystery of AGI with curiosity. Answer the essential question with long-termism. 🐋
DeepSeek AI A.I CyberSecurity Scoring
DeepSeek AI Risk Score (AI oriented)Between 0 and 549
DeepSeek AI
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DeepSeek AI Global Score (TPRM)XXXX
DeepSeek AI
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- Vulnerabilities
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DeepSeek AI Company CyberSecurity News & History
Past Incidents
6
Attack Types
3
DeepSeek
Breach
Rankiteo Explanation
Attack with significant impact with customers data leaks
Description: In January 2025, Chinese AI specialist **DeepSeek** suffered a critical data leak exposing over **1 million sensitive log streams**, including **chat histories, secret keys, and internal operational data**. The breach stemmed from a **publicly accessible ClickHouse database** with misconfigured access controls, granting unauthorized parties **full administrative privileges**—enabling potential data exfiltration, manipulation, or deletion. While Wiz Research promptly alerted DeepSeek, which secured the exposure, the incident highlighted vulnerabilities in **cloud storage misconfigurations** and **endpoint security**. The leaked data posed risks of **intellectual property theft, credential compromise, and regulatory non-compliance** (e.g., GDPR/CCPA fines). Given the scale and sensitivity of the exposed logs—likely containing **proprietary AI model interactions and authentication tokens**—the breach could undermine **customer trust, competitive advantage, and operational integrity**, with potential downstream effects like **fraud, reputational damage, or supply chain attacks**. The root cause aligned with **unintentional leakage** via **misconfigured infrastructure**, though insider threats or targeted exploitation remained plausible secondary risks.
DeepSeek
Breach
Rankiteo Explanation
Attack with significant impact with customers data leaks
Description: DeepSeek's database was left exposed on the internet, leaking over 1 million records, including system logs, user submissions, and API tokens. Due to the database's nature as an analytics type, the breach of user interaction data and authentication keys poses a significant risk to user privacy. The issue was resolved within 30 minutes after Wiz researchers attempted to notify the company, by which time the database was secured.
DeepSeek
Breach
Rankiteo Explanation
Attack with significant impact with customers data leaks
Description: DeepSeek, a Chinese AI provider, suffered a **data breach** linked to unsanctioned AI use, where sensitive corporate or user data—potentially including PII, proprietary code, or internal documents—was exposed due to employees inputting confidential information into unapproved AI models (e.g., public chatbots). The breach stemmed from shadow AI practices, where third-party AI tools (like DeepSeek’s own or others) stored and processed data without adequate security controls, leading to unauthorized access or leaks. The incident aligns with risks highlighted in the article: employees bypassing IT policies to use AI tools, resulting in data being retained on external servers with weaker protections. The breach not only violated data protection regulations (e.g., GDPR-like standards) but also risked further exploitation, such as adversaries accessing the leaked data or the AI model itself being compromised to exfiltrate additional information. The financial and reputational fallout included regulatory fines, loss of trust, and potential operational disruptions, compounded by the challenge of tracing all exposed data.
DeepSeek
Breach
Rankiteo Explanation
Attack with significant impact with customers data leaks
Description: In early 2025, researchers at Wiz uncovered a **vulnerable database operated by DeepSeek**, exposing highly sensitive corporate and user data. The breach included **chat histories, secret API keys, backend system details, and proprietary workflows** shared by employees via the platform. The leaked data originated from **shadow AI usage**—employees bypassing sanctioned tools to use DeepSeek’s consumer-grade LLM for tasks involving confidential spreadsheets, internal memos, and potentially trade secrets. While no direct financial fraud or ransomware was confirmed, the exposure of **authentication credentials and backend infrastructure details** created a severe risk of follow-on attacks, such as **spear-phishing, insider impersonation, or supply-chain compromises**. The incident highlighted the dangers of ungoverned AI adoption, where **ephemeral interactions with LLMs accumulate into high-value intelligence for threat actors**. DeepSeek’s database misconfiguration enabled attackers to harvest **years of prompt-engineered data**, including employee thought processes, financial forecasts, and operational strategies—effectively handing adversaries a **‘master key’ to internal systems**. Though DeepSeek patched the vulnerability, the breach underscored how **shadow AI expands attack surfaces silently**, with potential long-term repercussions for intellectual property theft, regulatory noncompliance (e.g., GDPR violations), and reputational damage. The exposure aligned with broader trends where **20% of organizations in an IBM study linked data breaches directly to unapproved AI tool usage**, with average costs exceeding **$670,000 per incident**.
DeepSeek
Cyber Attack
Rankiteo Explanation
Attack with significant impact with customers data leaks
Description: DeepSeek, a Chinese AI research lab, is under scrutiny for potentially compromising user data privacy. Recently popularized for its generative AI model, DeepSeek experienced a large-scale malicious attack causing limitation of new sign-ups. Concerns have been raised due to its policy of sending user data, including conversations and queries, to servers located in China. Incidents of censorship regarding content critical of China have been reported, raising the question of the extent of data privacy initiatives by DeepSeek. The company's data practices exemplify the challenges facing users around data privacy and the control companies hold over personal information.
DeepSeek
Vulnerability
Rankiteo Explanation
Attack with significant impact with customers data leaks
Description: DeepSeek, a generative AI platform, faced heightened concerns over privacy and security as it stores user data on servers in China. Security researchers discovered that DeepSeek exposed a critical database online, leaking over 1 million records, including user prompts, system logs, and API authentication tokens. The leaked information could lead to unauthorized access and misuse of user data, posing serious privacy and security risks. Furthermore, the platform's safety protections were found to be lacking when tested against various jailbreaks, illustrating a potential vulnerability to cyber threats.
DeepSeek AI Company Scoring based on AI Models
Cyber Incidents Likelihood 3 - 6 - 9 months
A.I Risk Score Likelihood 3 - 6 - 9 months
Underwriter Stats for DeepSeek AI
Incidents vs Technology, Information and Internet Industry Average (This Year)
DeepSeek AI has 650.0% more incidents than the average of same-industry companies with at least one recorded incident.
Incidents vs All-Companies Average (This Year)
DeepSeek AI has 823.08% more incidents than the average of all companies with at least one recorded incident.
Incident Types DeepSeek AI vs Technology, Information and Internet Industry Avg (This Year)
DeepSeek AI reported 6 incidents this year: 1 cyber attacks, 0 ransomware, 1 vulnerabilities, 4 data breaches, compared to industry peers with at least 1 incident.
Incident History — DeepSeek AI (X = Date, Y = Severity)
DeepSeek AI cyber incidents detection timeline including parent company and subsidiaries
DeepSeek AI Company Subsidiaries
DeepSeek (深度求索), founded in 2023, is a Chinese company dedicated to making AGI a reality. Unravel the mystery of AGI with curiosity. Answer the essential question with long-termism. 🐋
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DeepSeek AI CyberSecurity News
November 27, 2025 02:03 PM
KawaiiGPT - Free WormGPT Variant Leveraging DeepSeek, Gemini, and Kimi-K2 AI Models
KawaiiGPT emerges as an accessible, open-source tool that mimics the controversial WormGPT, providing unrestricted AI assistance via...
November 24, 2025 03:49 PM
Severe Security Risks Emerge as DeepSeek-R1 Produces Vulnerable Code
Chinese AI startup DeepSeek released its flagship language model DeepSeek-R1 in January 2025 as a cost-effective alternative to Western AI...
November 24, 2025 11:07 AM
Chinese DeepSeek-R1 AI Generates Insecure Code When Prompts Mention Tibet or Uyghurs
New research from CrowdStrike has revealed that DeepSeek's artificial intelligence (AI) reasoning model DeepSeek-R1 produces more security...
November 24, 2025 07:26 AM
DeepSeek-R1 Makes Code for Prompts With Severe Security Vulnerabilities
A concerning vulnerability in DeepSeek-R1, a Chinese-developed artificial intelligence coding assistant. When the AI model encounters...
October 08, 2025 07:00 AM
What CISOs should know about DeepSeek cybersecurity risks
DeepSeek, the Chinese AI model, has garnered global attention, but it also puts Western enterprises at risk. Learn how to manage the threat.
October 02, 2025 07:00 AM
DeepSeek AI Models Are Unsafe and Unreliable, Finds NIST-Backed Study
The US Commerce Chief has also issued a warning about DeepSeek that reliance on those AI models is "dangerous and shortsighted."
October 02, 2025 07:00 AM
NIST-Backed Study Declares DeepSeek AI Models Unsafe and Unreliable, Raising Global Alarm
A groundbreaking study, backed by the U.S. National Institute of Standards and Technology (NIST) through its Center for AI Standards and...
October 01, 2025 07:00 AM
NIST Report Pinpoints Risks of DeepSeek AI Models
The U.S. government agency said DeepSeek's models lag behind U.S. counterparts in cybersecurity and reasoning capabilities.
September 30, 2025 07:00 AM
CAISI Evaluation of DeepSeek AI Models Finds Shortcomings and Risks | NIST
The Center for AI Standards and Innovation at NIST evaluated several leading models from DeepSeek, an AI company based in the People's...
Frequently Asked Questions
Explore insights on cybersecurity incidents, risk posture, and Rankiteo's assessments.
DeepSeek AI CyberSecurity History Information
Official Website of DeepSeek AI
The official website of DeepSeek AI is https://www.deepseek.com.
DeepSeek AI’s AI-Generated Cybersecurity Score
According to Rankiteo, DeepSeek AI’s AI-generated cybersecurity score is 411, reflecting their Critical security posture.
How many security badges does DeepSeek AI’ have ?
According to Rankiteo, DeepSeek AI currently holds 0 security badges, indicating that no recognized compliance certifications are currently verified for the organization.
Does DeepSeek AI have SOC 2 Type 1 certification ?
According to Rankiteo, DeepSeek AI is not certified under SOC 2 Type 1.
Does DeepSeek AI have SOC 2 Type 2 certification ?
According to Rankiteo, DeepSeek AI does not hold a SOC 2 Type 2 certification.
Does DeepSeek AI comply with GDPR ?
According to Rankiteo, DeepSeek AI is not listed as GDPR compliant.
Does DeepSeek AI have PCI DSS certification ?
According to Rankiteo, DeepSeek AI does not currently maintain PCI DSS compliance.
Does DeepSeek AI comply with HIPAA ?
According to Rankiteo, DeepSeek AI is not compliant with HIPAA regulations.
Does DeepSeek AI have ISO 27001 certification ?
According to Rankiteo,DeepSeek AI is not certified under ISO 27001, indicating the absence of a formally recognized information security management framework.
Industry Classification of DeepSeek AI
DeepSeek AI operates primarily in the Technology, Information and Internet industry.
Number of Employees at DeepSeek AI
DeepSeek AI employs approximately 129 people worldwide.
Subsidiaries Owned by DeepSeek AI
DeepSeek AI presently has no subsidiaries across any sectors.
DeepSeek AI’s LinkedIn Followers
DeepSeek AI’s official LinkedIn profile has approximately 167,839 followers.
NAICS Classification of DeepSeek AI
DeepSeek AI is classified under the NAICS code 513, which corresponds to Others.
DeepSeek AI’s Presence on Crunchbase
No, DeepSeek AI does not have a profile on Crunchbase.
DeepSeek AI’s Presence on LinkedIn
Yes, DeepSeek AI maintains an official LinkedIn profile, which is actively utilized for branding and talent engagement, which can be accessed here: https://www.linkedin.com/company/deepseek-ai.
Cybersecurity Incidents Involving DeepSeek AI
As of December 04, 2025, Rankiteo reports that DeepSeek AI has experienced 6 cybersecurity incidents.
Number of Peer and Competitor Companies
DeepSeek AI has an estimated 12,848 peer or competitor companies worldwide.
What types of cybersecurity incidents have occurred at DeepSeek AI ?
Incident Types: The types of cybersecurity incidents that have occurred include Vulnerability, Cyber Attack and Breach.
What was the total financial impact of these incidents on DeepSeek AI ?
Total Financial Loss: The total financial loss from these incidents is estimated to be $670 billion.
How does DeepSeek AI detect and respond to cybersecurity incidents ?
Detection and Response: The company detects and responds to cybersecurity incidents through an third party assistance with wiz researchers, and containment measures with secured the database, and incident response plan activated with yes (prompt securing of database by deepseek), and third party assistance with yes (wiz research reported the issue), and containment measures with securing the publicly accessible database, and containment measures with network monitoring to detect unsanctioned ai usage, containment measures with restricting access to high-risk ai tools, and remediation measures with developing realistic acceptable use policies for ai, remediation measures with vendor due diligence for ai tools, remediation measures with providing sanctioned ai alternatives, remediation measures with employee education on shadow ai risks, and communication strategy with internal advisories on shadow ai risks, communication strategy with training programs for employees and executives, and enhanced monitoring with recommended for detecting ai-related data leakage, and incident response plan activated with partial (e.g., samsung’s 2023 chatgpt ban), and third party assistance with wiz (deepseek vulnerability disclosure), third party assistance with promptarmor (slack ai attack research), third party assistance with ibm/gartner (governance frameworks), and containment measures with blanket ai bans (e.g., samsung 2023), containment measures with employee training (e.g., anagram’s compliance programs), containment measures with ai runtime controls (gartner 2025 recommendation), and remediation measures with centralized ai inventory (ibm’s lifecycle governance), remediation measures with penetration testing for ai systems, remediation measures with network monitoring for unauthorized ai usage, remediation measures with 30-day data deletion policies (openai’s post-lawsuit commitment), and recovery measures with ai policy overhauls, recovery measures with ethical ai usage guidelines, recovery measures with incident response playbooks for shadow ai, and communication strategy with public disclosures (e.g., openai’s transparency reports), communication strategy with employee advisories (e.g., microsoft’s uk survey findings), communication strategy with stakeholder reports (e.g., ibm’s cost of data breach 2025), and network segmentation with recommended (ibm/gartner), and enhanced monitoring with recommended (e.g., tracking unauthorized ai tool usage)..
Incident Details
Can you provide details on each incident ?
Title: DeepSeek Data Privacy Incident
Description: DeepSeek, a Chinese AI research lab, is under scrutiny for potentially compromising user data privacy. Recently popularized for its generative AI model, DeepSeek experienced a large-scale malicious attack causing limitation of new sign-ups. Concerns have been raised due to its policy of sending user data, including conversations and queries, to servers located in China. Incidents of censorship regarding content critical of China have been reported, raising the question of the extent of data privacy initiatives by DeepSeek. The company's data practices exemplify the challenges facing users around data privacy and the control companies hold over personal information.
Type: Data Privacy Incident
Attack Vector: Large-scale malicious attack
Title: DeepSeek Database Exposure
Description: DeepSeek's database was left exposed on the internet, leaking over 1 million records, including system logs, user submissions, and API tokens. The issue was resolved within 30 minutes after Wiz researchers attempted to notify the company, by which time the database was secured.
Type: Data Leak
Attack Vector: Exposed Database
Vulnerability Exploited: Misconfiguration
Title: DeepSeek Data Leak
Description: DeepSeek, a generative AI platform, faced heightened concerns over privacy and security as it stores user data on servers in China. Security researchers discovered that DeepSeek exposed a critical database online, leaking over 1 million records, including user prompts, system logs, and API authentication tokens. The leaked information could lead to unauthorized access and misuse of user data, posing serious privacy and security risks. Furthermore, the platform's safety protections were found to be lacking when tested against various jailbreaks, illustrating a potential vulnerability to cyber threats.
Type: Data Leak
Attack Vector: Exposed Database
Vulnerability Exploited: Improper Database Security
Title: DeepSeek Data Leak via Publicly Accessible ClickHouse Database
Description: In January 2025, Wiz Research discovered that Chinese AI specialist DeepSeek had suffered a data leak exposing over 1 million sensitive log streams. The leak stemmed from a publicly accessible ClickHouse database, allowing full control over database operations, including access to internal data such as chat history and secret keys. Wiz Research reported the issue to DeepSeek, which promptly secured the exposure. The incident highlighted risks associated with data leakage, whether intentional (e.g., insider threats, phishing) or unintentional (e.g., misconfigurations, human error). Potential consequences included regulatory fines (e.g., GDPR, CCPA), intellectual property loss, reputational damage, and financial harm like credit card fraud or share price declines.
Date Detected: January 2025
Date Publicly Disclosed: January 2025
Type: Data Leak
Attack Vector: Misconfigured Cloud Storage (Publicly Accessible ClickHouse Database)Potential Insider Threats (Unconfirmed)Potential Phishing/Social Engineering (Unconfirmed)
Vulnerability Exploited: Improper Access Controls (Publicly Accessible Database)
Title: Risks and Impacts of Shadow AI in Corporate Environments
Description: The article discusses the growing threat of 'shadow AI'—unsanctioned use of AI tools (e.g., ChatGPT, Gemini, Claude) by employees without IT oversight. This practice exposes organizations to significant security, compliance, and operational risks, including data leakage (e.g., PII, IP, or proprietary code shared with third-party AI models), introduction of vulnerabilities via buggy AI-generated code, regulatory non-compliance (e.g., GDPR, CCPA), and potential breaches. Shadow AI can also enable unauthorized access, malicious AI agents, or biased decision-making due to flawed AI outputs. IBM reports that 20% of organizations experienced breaches linked to shadow AI in 2023, with costs reaching up to $670,000 per incident. Mitigation strategies include policy updates, vendor due diligence, employee education, and network monitoring.
Type: Data Leakage
Attack Vector: Employee use of unsanctioned AI tools (e.g., ChatGPT, Gemini, Claude)Browser extensions with embedded AIAI features in legitimate business software enabled without IT approvalAgentic AI (autonomous agents acting without oversight)Malicious fake AI tools designed to exfiltrate data
Vulnerability Exploited: Lack of visibility into employee AI tool usageInadequate acceptable use policies for AIAbsence of vendor security assessments for AI toolsUnsecured digital identities for AI agentsSoftware vulnerabilities in AI tools (e.g., backdoors, bugs)
Threat Actor: Internal Employees (unintentional)Third-Party AI Providers (potential data exposure)Cybercriminals (via fake AI tools or compromised agents)
Motivation: Employee productivity gains (unintentional risk)Corporate inertia in adopting sanctioned AI toolsFinancial gain (by threat actors exploiting shadow AI)
Title: Shadow AI Data Leakage and Privacy Risks in Corporate Environments (2024-2025)
Description: 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. Sensitive corporate data, including proprietary code, financial records, internal memos, and employee health records, is routinely shared with these tools, expanding attack surfaces. Legal orders (e.g., OpenAI’s 2025 court case with the New York Times) and vulnerabilities (e.g., DeepSeek’s exposed database, Slack AI’s prompt engineering attack) demonstrate how AI interactions can be weaponized by cybercriminals to mimic employees, exfiltrate data, or craft targeted phishing attacks. The lack of governance frameworks (63% of organizations per IBM 2025) exacerbates risks, with breach costs reaching up to $670,000 for high-shadow-AI firms. Regulatory noncompliance (e.g., GDPR) and employee nonadherence to policies (58% admit sharing sensitive data) further compound the threat.
Date Publicly Disclosed: 2024-10-01
Type: Data Leakage
Attack Vector: Unauthorized AI Tool Usage (Shadow AI)Prompt Engineering Attacks (e.g., Slack AI exploitation)Misconfigured AI Databases (e.g., DeepSeek)Legal Data Retention Orders (e.g., OpenAI’s 2025 lawsuit)Social Engineering via AI-Generated Content (e.g., voice cloning, phishing)
Vulnerability Exploited: Lack of AI Governance FrameworksDefault Data Retention Policies in LLMs (e.g., OpenAI’s 30-day deletion lag)Employee Bypass of Sanctioned ToolsWeak Authentication in AI PlatformsUnmonitored Data Exfiltration via AI Prompts
Threat Actor: Opportunistic CybercriminalsState-Sponsored Actors (Potential)Insider Threats (Unintentional)Competitors (Industrial Espionage Risk)AI Platform Misconfigurations (e.g., DeepSeek)
Motivation: Financial Gain (e.g., $243,000 scam via AI voice cloning in 2019)Corporate EspionageData Harvesting for Dark Web SalesDisruption of Business OperationsExploitation of AI Training Data
What are the most common types of attacks the company has faced ?
Common Attack Types: The most common types of attacks the company has faced is Breach.
How does the company identify the attack vectors used in incidents ?
Identification of Attack Vectors: The company identifies the attack vectors used in incidents through Employee-downloaded AI tools (e.g., ChatGPT, Gemini)Browser extensions with AI capabilitiesUnauthorized activation of AI features in business software, Employee Use of Unsanctioned AI ToolsMisconfigured AI Databases (e.g., DeepSeek)Prompt Injection Attacks (e.g., Slack AI)Legal Data Retention Orders (e.g. and OpenAI 2025).
Impact of the Incidents
What was the impact of each incident ?
Incident : Data Privacy Incident DEE000012825
Data Compromised: User data, Conversations, Queries
Operational Impact: Limitation of new sign-ups
Incident : Data Leak DEE000013125
Data Compromised: System logs, User submissions, Api tokens
Systems Affected: Database
Incident : Data Leak DEE001021525
Data Compromised: User prompts, System logs, Api authentication tokens
Incident : Data Leak DEE456090325
Data Compromised: Chat history, Secret keys, Log streams (1m+ records)
Systems Affected: ClickHouse Database
Operational Impact: High (Exposure of Sensitive Internal Data)
Brand Reputation Impact: Potential Long-Term Damage (Unquantified)
Legal Liabilities: Potential GDPR Fines (EU)Potential CCPA Fines (California)
Identity Theft Risk: High (Exposure of Secret Keys)
Payment Information Risk: Potential (If Secret Keys Included Payment-Related Credentials)
Incident : Data Leakage DEE3893138111125
Financial Loss: Up to $670,000 per breach (IBM estimate); potential compliance fines (e.g., GDPR, CCPA)
Data Compromised: Personally identifiable information (pii), Intellectual property (ip), Proprietary code, Meeting notes, Customer/employee data
Systems Affected: Employee Devices (BYOD, laptops)Corporate Networks (via unauthorized AI agents)Business Software (AI features enabled without IT knowledge)Third-Party AI Servers (data storage in unregulated jurisdictions)
Operational Impact: Flawed decision-making due to biased/low-quality AI outputsIntroduction of exploitable bugs in customer-facing productsPotential corporate inertia or stalled digital transformation
Brand Reputation Impact: High (due to data breaches, compliance violations, or flawed AI-driven decisions)
Legal Liabilities: Regulatory fines (e.g., GDPR, CCPA)Litigation from affected customers/employees
Identity Theft Risk: High (if PII is shared with AI models or leaked)
Incident : Data Leakage DEE5293552111725
Financial Loss: Up to $670,000 per breach (IBM 2025); Potential GDPR fines up to €20M or 4% global revenue
Data Compromised: Proprietary code (e.g., samsung 2023 incident), Financial records (22% of uk employees use shadow ai for financial tasks), Internal memos/trade secrets, Employee health records, Client data (58% of employees admit sharing sensitive data), Chat histories (e.g., deepseek’s exposed database), Secret keys/backend details
Systems Affected: Corporate AI Tools (e.g., Slack AI)Third-Party LLMs (ChatGPT, Claude, DeepSeek)Enterprise Workflows Integrating Unsanctioned AILegal/Compliance Systems (Data retention conflicts)
Operational Impact: Loss of Intellectual PropertyErosion of Competitive AdvantageDisruption of Internal Communications (e.g., AI-drafted memos leaking secrets)Increased Scrutiny from Regulators
Revenue Loss: Potential 4% global revenue (GDPR fines) + breach costs
Customer Complaints: Likely (due to privacy violations)
Brand Reputation Impact: High (publicized breaches, regulatory actions)
Legal Liabilities: GDPR Noncompliance (Fines up to €20M)Lawsuits (e.g., New York Times vs. OpenAI 2025)Contractual Violations with Clients
Identity Theft Risk: High (AI-generated impersonation attacks)
Payment Information Risk: Moderate (22% use shadow AI for financial tasks)
What is the average financial loss per incident ?
Average Financial Loss: The average financial loss per incident is $111.67 billion.
What types of data are most commonly compromised in incidents ?
Commonly Compromised Data Types: The types of data most commonly compromised in incidents are User Data, Conversations, Queries, , System Logs, User Submissions, Api Tokens, , User Prompts, System Logs, Api Authentication Tokens, , Log Streams, Chat History, Secret Keys, , Pii (Customer/Employee), Intellectual Property, Proprietary Code, Corporate Meeting Notes, , Chat Histories, Proprietary Code, Financial Data, Internal Documents, Secret Keys, Backend System Details, Employee/Patient Health Records, Trade Secrets and .
Which entities were affected by each incident ?
Incident : Data Privacy Incident DEE000012825
Entity Name: DeepSeek
Entity Type: AI Research Lab
Industry: Technology
Location: China
Incident : Data Leak DEE001021525
Entity Name: DeepSeek
Entity Type: Company
Industry: Generative AI
Location: China
Incident : Data Leak DEE456090325
Entity Name: DeepSeek
Entity Type: Private Company
Industry: Artificial Intelligence
Location: China
Incident : Data Leakage DEE3893138111125
Entity Type: Corporate Organizations (General)
Industry: Cross-Industry
Location: Global
Incident : Data Leakage DEE5293552111725
Entity Name: OpenAI
Entity Type: AI Developer
Industry: Technology
Location: Global (HQ: USA)
Size: Large
Customers Affected: Millions (ChatGPT users, including corporate employees)
Incident : Data Leakage DEE5293552111725
Entity Name: Anthropic
Entity Type: AI Developer
Industry: Technology
Location: Global (HQ: USA)
Size: Medium
Customers Affected: Corporate users of Claude
Incident : Data Leakage DEE5293552111725
Entity Name: DeepSeek
Entity Type: AI Developer
Industry: Technology
Location: Global
Size: Unknown
Customers Affected: Users of DeepSeek’s vulnerable database
Incident : Data Leakage DEE5293552111725
Entity Name: Slack (Salesforce)
Entity Type: Enterprise Software
Industry: Technology
Location: Global
Size: Large
Customers Affected: Organizations using Slack AI
Incident : Data Leakage DEE5293552111725
Entity Name: Samsung
Entity Type: Conglomerate
Industry: Electronics/Technology
Location: Global (HQ: South Korea)
Size: Large
Customers Affected: Internal (proprietary code leak in 2023)
Incident : Data Leakage DEE5293552111725
Entity Name: Unspecified UK Energy Company
Entity Type: Energy
Industry: Utilities
Location: UK
Size: Unknown
Customers Affected: $243,000 scam via AI voice cloning (2019)
Incident : Data Leakage DEE5293552111725
Entity Name: General Corporate Sector
Entity Type: Cross-Industry
Industry: All
Location: Global
Size: All
Customers Affected: 90% of companies (MIT Project NANDA 2025)
Response to the Incidents
What measures were taken in response to each incident ?
Incident : Data Leak DEE000013125
Third Party Assistance: Wiz researchers
Containment Measures: Secured the database
Incident : Data Leak DEE456090325
Incident Response Plan Activated: Yes (Prompt Securing of Database by DeepSeek)
Third Party Assistance: Yes (Wiz Research Reported the Issue)
Containment Measures: Securing the Publicly Accessible Database
Incident : Data Leakage DEE3893138111125
Containment Measures: Network monitoring to detect unsanctioned AI usageRestricting access to high-risk AI tools
Remediation Measures: Developing realistic acceptable use policies for AIVendor due diligence for AI toolsProviding sanctioned AI alternativesEmployee education on shadow AI risks
Communication Strategy: Internal advisories on shadow AI risksTraining programs for employees and executives
Enhanced Monitoring: Recommended for detecting AI-related data leakage
Incident : Data Leakage DEE5293552111725
Incident Response Plan Activated: Partial (e.g., Samsung’s 2023 ChatGPT ban)
Third Party Assistance: Wiz (Deepseek Vulnerability Disclosure), Promptarmor (Slack Ai Attack Research), Ibm/Gartner (Governance Frameworks).
Containment Measures: Blanket AI Bans (e.g., Samsung 2023)Employee Training (e.g., Anagram’s compliance programs)AI Runtime Controls (Gartner 2025 recommendation)
Remediation Measures: Centralized AI Inventory (IBM’s lifecycle governance)Penetration Testing for AI SystemsNetwork Monitoring for Unauthorized AI Usage30-Day Data Deletion Policies (OpenAI’s post-lawsuit commitment)
Recovery Measures: AI Policy OverhaulsEthical AI Usage GuidelinesIncident Response Playbooks for Shadow AI
Communication Strategy: Public Disclosures (e.g., OpenAI’s transparency reports)Employee Advisories (e.g., Microsoft’s UK survey findings)Stakeholder Reports (e.g., IBM’s Cost of Data Breach 2025)
Network Segmentation: Recommended (IBM/Gartner)
Enhanced Monitoring: Recommended (e.g., tracking unauthorized AI tool usage)
What is the company's incident response plan?
Incident Response Plan: The company's incident response plan is described as Yes (Prompt Securing of Database by DeepSeek), Partial (e.g., Samsung’s 2023 ChatGPT ban).
How does the company involve third-party assistance in incident response ?
Third-Party Assistance: The company involves third-party assistance in incident response through Wiz researchers, Yes (Wiz Research Reported the Issue), Wiz (DeepSeek vulnerability disclosure), PromptArmor (Slack AI attack research), IBM/Gartner (governance frameworks), .
Data Breach Information
What type of data was compromised in each breach ?
Incident : Data Privacy Incident DEE000012825
Type of Data Compromised: User data, Conversations, Queries
Incident : Data Leak DEE000013125
Type of Data Compromised: System logs, User submissions, Api tokens
Number of Records Exposed: Over 1 million
Sensitivity of Data: User interaction data and authentication keys
Incident : Data Leak DEE001021525
Type of Data Compromised: User prompts, System logs, Api authentication tokens
Number of Records Exposed: 1 million
Incident : Data Leak DEE456090325
Type of Data Compromised: Log streams, Chat history, Secret keys
Number of Records Exposed: 1,000,000+
Sensitivity of Data: High (Includes Authentication Credentials and Internal Communications)
Data Encryption: No (Data Was Publicly Accessible)
File Types Exposed: Log FilesPotential Configuration Files
Incident : Data Leakage DEE3893138111125
Type of Data Compromised: Pii (customer/employee), Intellectual property, Proprietary code, Corporate meeting notes
Sensitivity of Data: High (regulated data under GDPR, CCPA, etc.)
Data Exfiltration: Potential (via AI model training or third-party breaches)
Personally Identifiable Information: Yes (shared with AI models or leaked)
Incident : Data Leakage DEE5293552111725
Type of Data Compromised: Chat histories, Proprietary code, Financial data, Internal documents, Secret keys, Backend system details, Employee/patient health records, Trade secrets
Number of Records Exposed: Unknown (potentially millions across affected platforms)
Sensitivity of Data: High (includes PII, financial, proprietary, and health data)
Data Exfiltration: Confirmed (e.g., DeepSeek, Slack AI, Shadow AI leaks)
Data Encryption: Partial (e.g., OpenAI encrypts data at rest, but retention policies create risks)
File Types Exposed: Text (prompts/outputs)Spreadsheets (e.g., confidential financial data)Code RepositoriesAudio (e.g., voice cloning samples)Internal Memos
Personally Identifiable Information: Yes (employee/client records, health data)
What measures does the company take to prevent data exfiltration ?
Prevention of Data Exfiltration: The company takes the following measures to prevent data exfiltration: Developing realistic acceptable use policies for AI, Vendor due diligence for AI tools, Providing sanctioned AI alternatives, Employee education on shadow AI risks, , Centralized AI Inventory (IBM’s lifecycle governance), Penetration Testing for AI Systems, Network Monitoring for Unauthorized AI Usage, 30-Day Data Deletion Policies (OpenAI’s post-lawsuit commitment), .
How does the company handle incidents involving personally identifiable information (PII) ?
Handling of PII Incidents: The company handles incidents involving personally identifiable information (PII) through by secured the database, securing the publicly accessible database, , network monitoring to detect unsanctioned ai usage, restricting access to high-risk ai tools, , blanket ai bans (e.g., samsung 2023), employee training (e.g., anagram’s compliance programs), ai runtime controls (gartner 2025 recommendation) and .
Ransomware Information
How does the company recover data encrypted by ransomware ?
Data Recovery from Ransomware: The company recovers data encrypted by ransomware through AI Policy Overhauls, Ethical AI Usage Guidelines, Incident Response Playbooks for Shadow AI, .
Regulatory Compliance
Were there any regulatory violations and fines imposed for each incident ?
Incident : Data Leak DEE456090325
Regulations Violated: Potential GDPR (EU), Potential CCPA (California),
Incident : Data Leakage DEE3893138111125
Regulations Violated: GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act), Other jurisdiction-specific data protection laws,
Incident : Data Leakage DEE5293552111725
Regulations Violated: GDPR (Article 5: Data Minimization), CCPA (California Consumer Privacy Act), Sector-Specific Regulations (e.g., HIPAA for health data),
Fines Imposed: Potential: Up to €20M or 4% global revenue (GDPR)
Legal Actions: New York Times vs. OpenAI (2025, data retention lawsuit), Unspecified lawsuits from affected corporations,
Regulatory Notifications: Likely required under GDPR/CCPA for breachesOpenAI’s court-mandated data retention (2025, later reversed)
How does the company ensure compliance with regulatory requirements ?
Ensuring Regulatory Compliance: The company ensures compliance with regulatory requirements through New York Times vs. OpenAI (2025, data retention lawsuit), Unspecified lawsuits from affected corporations, .
Lessons Learned and Recommendations
What lessons were learned from each incident ?
Incident : Data Leak DEE456090325
Lessons Learned: Importance of Least-Privilege Access Controls, Need for Regular Audits of Cloud Configurations, Risks of Publicly Accessible Databases, Value of Third-Party Security Research (e.g., Wiz Research), Criticality of Data Classification and DLP Solutions
Incident : Data Leakage DEE3893138111125
Lessons Learned: 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., Vendor due diligence for AI tools is essential to mitigate third-party risks., Employee awareness programs must highlight the risks of unsanctioned AI usage, including job losses and corporate inertia., Balancing productivity and security requires sanctioned AI alternatives and seamless access request processes.
Incident : Data Leakage DEE5293552111725
Lessons Learned: 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)., AI governance lags behind adoption (63% of organizations lack frameworks, per IBM 2025)., Legal risks extend beyond breaches: data retention policies can conflict with lawsuits (e.g., OpenAI 2025)., AI platforms’ default settings (e.g., 30-day deletion lags) create unintended compliance gaps., Prompt engineering attacks can bypass traditional security controls (e.g., Slack AI leak)., Silent breaches are more damaging: firms may not realize data is compromised until exploited (e.g., AI-generated phishing).
What recommendations were made to prevent future incidents ?
Incident : Data Leak DEE456090325
Recommendations: Enforce Least-Privilege Access Policies, Implement Data Loss Prevention (DLP) Solutions, Classify Sensitive Data and Prioritize Protection, Conduct Regular Internal/External Security Audits, Provide Comprehensive Employee Security Training, Monitor for Shadow IT and Unauthorized Data Sharing, Use Tools Like Outpost24’s CompassDRP for Leak DetectionEnforce Least-Privilege Access Policies, Implement Data Loss Prevention (DLP) Solutions, Classify Sensitive Data and Prioritize Protection, Conduct Regular Internal/External Security Audits, Provide Comprehensive Employee Security Training, Monitor for Shadow IT and Unauthorized Data Sharing, Use Tools Like Outpost24’s CompassDRP for Leak DetectionEnforce Least-Privilege Access Policies, Implement Data Loss Prevention (DLP) Solutions, Classify Sensitive Data and Prioritize Protection, Conduct Regular Internal/External Security Audits, Provide Comprehensive Employee Security Training, Monitor for Shadow IT and Unauthorized Data Sharing, Use Tools Like Outpost24’s CompassDRP for Leak DetectionEnforce Least-Privilege Access Policies, Implement Data Loss Prevention (DLP) Solutions, Classify Sensitive Data and Prioritize Protection, Conduct Regular Internal/External Security Audits, Provide Comprehensive Employee Security Training, Monitor for Shadow IT and Unauthorized Data Sharing, Use Tools Like Outpost24’s CompassDRP for Leak DetectionEnforce Least-Privilege Access Policies, Implement Data Loss Prevention (DLP) Solutions, Classify Sensitive Data and Prioritize Protection, Conduct Regular Internal/External Security Audits, Provide Comprehensive Employee Security Training, Monitor for Shadow IT and Unauthorized Data Sharing, Use Tools Like Outpost24’s CompassDRP for Leak DetectionEnforce Least-Privilege Access Policies, Implement Data Loss Prevention (DLP) Solutions, Classify Sensitive Data and Prioritize Protection, Conduct Regular Internal/External Security Audits, Provide Comprehensive Employee Security Training, Monitor for Shadow IT and Unauthorized Data Sharing, Use Tools Like Outpost24’s CompassDRP for Leak DetectionEnforce Least-Privilege Access Policies, Implement Data Loss Prevention (DLP) Solutions, Classify Sensitive Data and Prioritize Protection, Conduct Regular Internal/External Security Audits, Provide Comprehensive Employee Security Training, Monitor for Shadow IT and Unauthorized Data Sharing, Use Tools Like Outpost24’s CompassDRP for Leak Detection
Incident : Data Leakage DEE3893138111125
Recommendations: Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Implement vendor security assessments for all AI tools in use., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Deploy network monitoring tools to detect and mitigate data leakage via AI., Educate employees on the risks of shadow AI, including data exposure and compliance violations., Establish a process for employees to request access to new AI tools., Monitor the evolution of agentic AI and autonomous agents for emerging risks.Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Implement vendor security assessments for all AI tools in use., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Deploy network monitoring tools to detect and mitigate data leakage via AI., Educate employees on the risks of shadow AI, including data exposure and compliance violations., Establish a process for employees to request access to new AI tools., Monitor the evolution of agentic AI and autonomous agents for emerging risks.Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Implement vendor security assessments for all AI tools in use., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Deploy network monitoring tools to detect and mitigate data leakage via AI., Educate employees on the risks of shadow AI, including data exposure and compliance violations., Establish a process for employees to request access to new AI tools., Monitor the evolution of agentic AI and autonomous agents for emerging risks.Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Implement vendor security assessments for all AI tools in use., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Deploy network monitoring tools to detect and mitigate data leakage via AI., Educate employees on the risks of shadow AI, including data exposure and compliance violations., Establish a process for employees to request access to new AI tools., Monitor the evolution of agentic AI and autonomous agents for emerging risks.Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Implement vendor security assessments for all AI tools in use., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Deploy network monitoring tools to detect and mitigate data leakage via AI., Educate employees on the risks of shadow AI, including data exposure and compliance violations., Establish a process for employees to request access to new AI tools., Monitor the evolution of agentic AI and autonomous agents for emerging risks.Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Implement vendor security assessments for all AI tools in use., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Deploy network monitoring tools to detect and mitigate data leakage via AI., Educate employees on the risks of shadow AI, including data exposure and compliance violations., Establish a process for employees to request access to new AI tools., Monitor the evolution of agentic AI and autonomous agents for emerging risks.Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Implement vendor security assessments for all AI tools in use., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Deploy network monitoring tools to detect and mitigate data leakage via AI., Educate employees on the risks of shadow AI, including data exposure and compliance violations., Establish a process for employees to request access to new AI tools., Monitor the evolution of agentic AI and autonomous agents for emerging risks.Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Implement vendor security assessments for all AI tools in use., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Deploy network monitoring tools to detect and mitigate data leakage via AI., Educate employees on the risks of shadow AI, including data exposure and compliance violations., Establish a process for employees to request access to new AI tools., Monitor the evolution of agentic AI and autonomous agents for emerging risks.
Incident : Data Leakage DEE5293552111725
Recommendations: Strategic: Treat AI as a critical third-party risk (e.g., vendor assessments for LLM providers)., Budget for AI-specific cyber insurance to cover shadow AI breaches., Collaborate with regulators to shape AI data protection standards., Monitor dark web for leaked AI-trained datasets (e.g., employee prompts sold by initial access brokers)., Strategic: Treat AI as a critical third-party risk (e.g., vendor assessments for LLM providers)., Budget for AI-specific cyber insurance to cover shadow AI breaches., Collaborate with regulators to shape AI data protection standards., Monitor dark web for leaked AI-trained datasets (e.g., employee prompts sold by initial access brokers)., Strategic: Treat AI as a critical third-party risk (e.g., vendor assessments for LLM providers)., Budget for AI-specific cyber insurance to cover shadow AI breaches., Collaborate with regulators to shape AI data protection standards., Monitor dark web for leaked AI-trained datasets (e.g., employee prompts sold by initial access brokers)., Strategic: Treat AI as a critical third-party risk (e.g., vendor assessments for LLM providers)., Budget for AI-specific cyber insurance to cover shadow AI breaches., Collaborate with regulators to shape AI data protection standards., Monitor dark web for leaked AI-trained datasets (e.g., employee prompts sold by initial access brokers)..
What are the key lessons learned from past incidents ?
Key Lessons Learned: The key lessons learned from past incidents are Importance of Least-Privilege Access Controls,Need for Regular Audits of Cloud Configurations,Risks of Publicly Accessible Databases,Value of Third-Party Security Research (e.g., Wiz Research),Criticality of Data Classification and DLP SolutionsShadow 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.,Vendor due diligence for AI tools is essential to mitigate third-party risks.,Employee awareness programs must highlight the risks of unsanctioned AI usage, including job losses and corporate inertia.,Balancing productivity and security requires sanctioned AI alternatives and seamless access request processes.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).,AI governance lags behind adoption (63% of organizations lack frameworks, per IBM 2025).,Legal risks extend beyond breaches: data retention policies can conflict with lawsuits (e.g., OpenAI 2025).,AI platforms’ default settings (e.g., 30-day deletion lags) create unintended compliance gaps.,Prompt engineering attacks can bypass traditional security controls (e.g., Slack AI leak).,Silent breaches are more damaging: firms may not realize data is compromised until exploited (e.g., AI-generated phishing).
What recommendations has the company implemented to improve cybersecurity ?
Implemented Recommendations: The company has implemented the following recommendations to improve cybersecurity: Monitor the evolution of agentic AI and autonomous agents for emerging risks., Deploy network monitoring tools to detect and mitigate data leakage via AI., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Conduct a risk assessment to identify shadow AI usage within the organization., Establish a process for employees to request access to new AI tools., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Implement vendor security assessments for all AI tools in use., Educate employees on the risks of shadow AI and including data exposure and compliance violations..
References
Where can I find more information about each incident ?
Incident : Data Leak DEE456090325
Source: Wiz Research
Incident : Data Leak DEE456090325
Source: IBM (Data Leakage Definition)
Incident : Data Leak DEE456090325
Source: Cloud Security Alliance (Cloud Misconfigurations)
Incident : Data Leak DEE456090325
Source: UK National Cyber Security Centre (NCSC) - Shadow IT Risks
Incident : Data Leak DEE456090325
Source: Outpost24 CompassDRP (Data Leakage Detection)
Incident : Data Leakage DEE3893138111125
Source: Microsoft Research
Incident : Data Leakage DEE3893138111125
Source: IBM Cost of a Data Breach Report (2023)
Incident : Data Leakage DEE3893138111125
Source: DeepSeek AI Breach (Example of third-party AI provider leakage)
Incident : Data Leakage DEE5293552111725
Source: MIT Project NANDA: State of AI in Business 2025
Date Accessed: 2025-01-01
Incident : Data Leakage DEE5293552111725
Source: IBM Cost of Data Breach Report 2025
URL: https://www.ibm.com/reports/data-breach
Date Accessed: 2025-06-01
Incident : Data Leakage DEE5293552111725
Source: Gartner Security and Risk Management Summit 2025
URL: https://www.gartner.com/en/conferences
Date Accessed: 2025-05-01
Incident : Data Leakage DEE5293552111725
Source: Anagram: Employee Compliance Report 2025
Date Accessed: 2025-03-01
Incident : Data Leakage DEE5293552111725
Source: Wiz Research: DeepSeek Vulnerability Disclosure
URL: https://www.wiz.io
Date Accessed: 2025-01-01
Incident : Data Leakage DEE5293552111725
Source: PromptArmor: Slack AI Exploitation Study
URL: https://www.promptarmor.com
Date Accessed: 2024-09-01
Incident : Data Leakage DEE5293552111725
Source: New York Times vs. OpenAI (2025 Court Documents)
Date Accessed: 2025-06-01
Where can stakeholders find additional resources on cybersecurity best practices ?
Additional Resources: Stakeholders can find additional resources on cybersecurity best practices at and Source: Wiz Research, and Source: IBM (Data Leakage Definition), and Source: Cloud Security Alliance (Cloud Misconfigurations), and Source: UK National Cyber Security Centre (NCSC) - Shadow IT Risks, and Source: Outpost24 CompassDRP (Data Leakage Detection), and Source: Microsoft Research, and Source: IBM Cost of a Data Breach Report (2023), and Source: DeepSeek AI Breach (Example of third-party AI provider leakage), and Source: ITProUrl: https://www.itpro.comDate Accessed: 2024-10-01, and Source: MIT Project NANDA: State of AI in Business 2025Date Accessed: 2025-01-01, and Source: IBM Cost of Data Breach Report 2025Url: https://www.ibm.com/reports/data-breachDate Accessed: 2025-06-01, and Source: Gartner Security and Risk Management Summit 2025Url: https://www.gartner.com/en/conferencesDate Accessed: 2025-05-01, and Source: Anagram: Employee Compliance Report 2025Date Accessed: 2025-03-01, and Source: Wiz Research: DeepSeek Vulnerability DisclosureUrl: https://www.wiz.ioDate Accessed: 2025-01-01, and Source: PromptArmor: Slack AI Exploitation StudyUrl: https://www.promptarmor.comDate Accessed: 2024-09-01, and Source: New York Times vs. OpenAI (2025 Court Documents)Date Accessed: 2025-06-01.
Investigation Status
What is the current status of the investigation for each incident ?
Incident : Data Leak DEE456090325
Investigation Status: Resolved (Database Secured)
Incident : Data Leakage DEE3893138111125
Investigation Status: Ongoing (industry-wide trend, not a single incident)
Incident : Data Leakage DEE5293552111725
Investigation Status: Ongoing (industry-wide; no single investigation)
How does the company communicate the status of incident investigations to stakeholders ?
Communication of Investigation Status: The company communicates the status of incident investigations to stakeholders through Internal Advisories On Shadow Ai Risks, Training Programs For Employees And Executives, Public Disclosures (E.G., Openai’S Transparency Reports), Employee Advisories (E.G., Microsoft’S Uk Survey Findings), Stakeholder Reports (E.G. and Ibm’S Cost Of Data Breach 2025).
Stakeholder and Customer Advisories
Were there any advisories issued to stakeholders or customers for each incident ?
Incident : Data Leakage DEE3893138111125
Stakeholder Advisories: It And Security Leaders Should Prioritize Shadow Ai As A Critical Blind Spot., Executives Must Align Ai Adoption Strategies With Security And Compliance Goals., Employees Should Be Trained On The Risks Of Unsanctioned Ai Tools..
Incident : Data Leakage DEE5293552111725
Stakeholder Advisories: Cisos: Prioritize Ai Governance Frameworks And Employee Training., Legal Teams: Audit Ai Data Retention Policies For Compliance Conflicts., Hr: Integrate Ai Usage Into Acceptable Use Policies And Disciplinary Codes., Board Members: Treat Shadow Ai As A Top-Tier Enterprise Risk..
Customer Advisories: Corporate Clients: Demand transparency from AI vendors on data handling/retention.End Users: Avoid sharing sensitive data with consumer AI tools; use enterprise-approved alternatives.Partners: Include AI data protection clauses in contracts (e.g., right to audit LLM interactions).
What advisories does the company provide to stakeholders and customers following an incident ?
Advisories Provided: The company provides the following advisories to stakeholders and customers following an incident: were It And Security Leaders Should Prioritize Shadow Ai As A Critical Blind Spot., Executives Must Align Ai Adoption Strategies With Security And Compliance Goals., Employees Should Be Trained On The Risks Of Unsanctioned Ai Tools., Cisos: Prioritize Ai Governance Frameworks And Employee Training., Legal Teams: Audit Ai Data Retention Policies For Compliance Conflicts., Hr: Integrate Ai Usage Into Acceptable Use Policies And Disciplinary Codes., Board Members: Treat Shadow Ai As A Top-Tier Enterprise Risk., Corporate Clients: Demand Transparency From Ai Vendors On Data Handling/Retention., End Users: Avoid Sharing Sensitive Data With Consumer Ai Tools; Use Enterprise-Approved Alternatives., Partners: Include Ai Data Protection Clauses In Contracts (E.G., Right To Audit Llm Interactions). and .
Initial Access Broker
How did the initial access broker gain entry for each incident ?
Incident : Data Leakage DEE3893138111125
Entry Point: Employee-Downloaded Ai Tools (E.G., Chatgpt, Gemini), Browser Extensions With Ai Capabilities, Unauthorized Activation Of Ai Features In Business Software,
Backdoors Established: Potential (via vulnerable AI tools or agents)
High Value Targets: Sensitive Data Stores (Pii, Ip, Proprietary Code), Corporate Decision-Making Processes (Via Biased Ai Outputs),
Data Sold on Dark Web: Sensitive Data Stores (Pii, Ip, Proprietary Code), Corporate Decision-Making Processes (Via Biased Ai Outputs),
Incident : Data Leakage DEE5293552111725
Entry Point: Employee Use Of Unsanctioned Ai Tools, Misconfigured Ai Databases (E.G., Deepseek), Prompt Injection Attacks (E.G., Slack Ai), Legal Data Retention Orders (E.G., Openai 2025),
Reconnaissance Period: Ongoing (years of accumulated prompts in some cases)
Backdoors Established: Potential (e.g., AI-trained datasets sold on dark web)
High Value Targets: Financial Forecasts, Product Roadmaps, Legal Strategies, M&A Plans, Employee Health Records,
Data Sold on Dark Web: Financial Forecasts, Product Roadmaps, Legal Strategies, M&A Plans, Employee Health Records,
Post-Incident Analysis
What were the root causes and corrective actions taken for each incident ?
Incident : Data Leak DEE000013125
Root Causes: Misconfiguration
Incident : Data Leak DEE456090325
Root Causes: Misconfigured Clickhouse Database (Publicly Accessible), Inadequate Access Controls, Lack Of Monitoring For Unauthorized Access,
Corrective Actions: Secured The Database, Likely Reviewed Access Controls (Assumed), Potential Implementation Of Dlp Or Monitoring Tools (Assumed),
Incident : Data Leakage DEE3893138111125
Root Causes: Lack Of Visibility Into Employee Ai Tool Usage, Absence Of Clear Acceptable Use Policies For Ai, Slow Corporate Adoption Of Sanctioned Ai Tools, Inadequate Vendor Security Assessments, Employee Frustration With Productivity Barriers,
Corrective Actions: Implement Comprehensive Ai Governance Frameworks., Enhance Monitoring For Unsanctioned Ai Usage., Foster A Culture Of Security Awareness Around Ai Risks., Accelerate Adoption Of Sanctioned Ai Tools To Meet Employee Needs.,
Incident : Data Leakage DEE5293552111725
Root Causes: Lack Of Ai-Specific Governance (63% Of Orgs Per Ibm 2025)., Over-Reliance On Employee Compliance (58% Admit Policy Violations)., Default Data Retention In Llms (E.G., Openai’S 30-Day Deletion Lag)., Inadequate Vendor Risk Management For Ai Tools., Cultural Prioritization Of Convenience Over Security (71% Uk Employees Use Shadow Ai)., Technical Gaps: No Runtime Controls For Ai Interactions.,
Corrective Actions: Mandate Ai Lifecycle Governance (Ibm’S 4-Pillar Framework)., Deploy Ai Firewalls To Block Unauthorized Tools., Enforce ‘Zero Trust’ For Ai: Verify All Prompts/Outputs., Conduct Red-Team Exercises For Prompt Injection Attacks., Partner With Ai Vendors For Enterprise-Grade Controls (E.G., Private Llms)., Establish Cross-Functional Ai Risk Committees (It, Legal, Hr).,
What is the company's process for conducting post-incident analysis ?
Post-Incident Analysis Process: The company's process for conducting post-incident analysis is described as Wiz researchers, , Recommended for detecting AI-related data leakage, Wiz (Deepseek Vulnerability Disclosure), Promptarmor (Slack Ai Attack Research), Ibm/Gartner (Governance Frameworks), , Recommended (e.g., tracking unauthorized AI tool usage).
What corrective actions has the company taken based on post-incident analysis ?
Corrective Actions Taken: The company has taken the following corrective actions based on post-incident analysis: Secured The Database, Likely Reviewed Access Controls (Assumed), Potential Implementation Of Dlp Or Monitoring Tools (Assumed), , Implement Comprehensive Ai Governance Frameworks., Enhance Monitoring For Unsanctioned Ai Usage., Foster A Culture Of Security Awareness Around Ai Risks., Accelerate Adoption Of Sanctioned Ai Tools To Meet Employee Needs., , Mandate Ai Lifecycle Governance (Ibm’S 4-Pillar Framework)., Deploy Ai Firewalls To Block Unauthorized Tools., Enforce ‘Zero Trust’ For Ai: Verify All Prompts/Outputs., Conduct Red-Team Exercises For Prompt Injection Attacks., Partner With Ai Vendors For Enterprise-Grade Controls (E.G., Private Llms)., Establish Cross-Functional Ai Risk Committees (It, Legal, Hr)., .
Additional Questions
General Information
Who was the attacking group in the last incident ?
Last Attacking Group: The attacking group in the last incident were an Internal Employees (unintentional)Third-Party AI Providers (potential data exposure)Cybercriminals (via fake AI tools or compromised agents), Opportunistic CybercriminalsState-Sponsored Actors (Potential)Insider Threats (Unintentional)Competitors (Industrial Espionage Risk)AI Platform Misconfigurations (e.g. and DeepSeek).
Incident Details
What was the most recent incident detected ?
Most Recent Incident Detected: The most recent incident detected was on January 2025.
What was the most recent incident publicly disclosed ?
Most Recent Incident Publicly Disclosed: The most recent incident publicly disclosed was on 2024-10-01.
Impact of the Incidents
What was the most significant data compromised in an incident ?
Most Significant Data Compromised: The most significant data compromised in an incident were User data, Conversations, Queries, , System logs, User submissions, API tokens, , user prompts, system logs, API authentication tokens, , Chat History, Secret Keys, Log Streams (1M+ records), , Personally Identifiable Information (PII), Intellectual Property (IP), Proprietary Code, Meeting Notes, Customer/Employee Data, , Proprietary Code (e.g., Samsung 2023 incident), Financial Records (22% of UK employees use shadow AI for financial tasks), Internal Memos/Trade Secrets, Employee Health Records, Client Data (58% of employees admit sharing sensitive data), Chat Histories (e.g., DeepSeek’s exposed database), Secret Keys/Backend Details and .
What was the most significant system affected in an incident ?
Most Significant System Affected: The most significant system affected in an incident were ClickHouse Database and Employee Devices (BYOD, laptops)Corporate Networks (via unauthorized AI agents)Business Software (AI features enabled without IT knowledge)Third-Party AI Servers (data storage in unregulated jurisdictions) and Corporate AI Tools (e.g., Slack AI)Third-Party LLMs (ChatGPT, Claude, DeepSeek)Enterprise Workflows Integrating Unsanctioned AILegal/Compliance Systems (Data retention conflicts).
Response to the Incidents
What third-party assistance was involved in the most recent incident ?
Third-Party Assistance in Most Recent Incident: The third-party assistance involved in the most recent incident was Wiz researchers, , wiz (deepseek vulnerability disclosure), promptarmor (slack ai attack research), ibm/gartner (governance frameworks), .
What containment measures were taken in the most recent incident ?
Containment Measures in Most Recent Incident: The containment measures taken in the most recent incident were Secured the database, Securing the Publicly Accessible Database, Network monitoring to detect unsanctioned AI usageRestricting access to high-risk AI tools, Blanket AI Bans (e.g., Samsung 2023)Employee Training (e.g. and Anagram’s compliance programs)AI Runtime Controls (Gartner 2025 recommendation).
Data Breach Information
What was the most sensitive data compromised in a breach ?
Most Sensitive Data Compromised: The most sensitive data compromised in a breach were Queries, User submissions, Log Streams (1M+ records), Chat Histories (e.g., DeepSeek’s exposed database), user prompts, Internal Memos/Trade Secrets, System logs, Conversations, API tokens, Financial Records (22% of UK employees use shadow AI for financial tasks), system logs, Meeting Notes, User data, Secret Keys, Personally Identifiable Information (PII), Client Data (58% of employees admit sharing sensitive data), Secret Keys/Backend Details, Customer/Employee Data, Proprietary Code (e.g., Samsung 2023 incident), API authentication tokens, Intellectual Property (IP), Chat History, Proprietary Code and Employee Health Records.
What was the number of records exposed in the most significant breach ?
Number of Records Exposed in Most Significant Breach: The number of records exposed in the most significant breach was 3.0M.
Regulatory Compliance
What was the highest fine imposed for a regulatory violation ?
Highest Fine Imposed: The highest fine imposed for a regulatory violation was Potential: Up to €20M or 4% global revenue (GDPR).
What was the most significant legal action taken for a regulatory violation ?
Most Significant Legal Action: The most significant legal action taken for a regulatory violation was New York Times vs. OpenAI (2025, data retention lawsuit), Unspecified lawsuits from affected corporations, .
Lessons Learned and Recommendations
What was the most significant lesson learned from past incidents ?
Most Significant Lesson Learned: The most significant lesson learned from past incidents was Silent breaches are more damaging: firms may not realize data is compromised until exploited (e.g., AI-generated phishing).
What was the most significant recommendation implemented to improve cybersecurity ?
Most Significant Recommendation Implemented: The most significant recommendation implemented to improve cybersecurity was Monitor the evolution of agentic AI and autonomous agents for emerging risks., Deploy network monitoring tools to detect and mitigate data leakage via AI., Monitor for Shadow IT and Unauthorized Data Sharing, Conduct a risk assessment to identify shadow AI usage within the organization., Develop and enforce an acceptable use policy tailored to corporate risk appetite., Establish a process for employees to request access to new AI tools., Provide approved AI alternatives to reduce reliance on unsanctioned tools., Implement Data Loss Prevention (DLP) Solutions, Classify Sensitive Data and Prioritize Protection, Use Tools Like Outpost24’s CompassDRP for Leak Detection, Enforce Least-Privilege Access Policies, Provide Comprehensive Employee Security Training, Implement vendor security assessments for all AI tools in use., Conduct Regular Internal/External Security Audits, Educate employees on the risks of shadow AI and including data exposure and compliance violations..
References
What is the most recent source of information about an incident ?
Most Recent Source: The most recent source of information about an incident are Wiz Research, IBM (Data Leakage Definition), Gartner Security and Risk Management Summit 2025, Microsoft Research, Cloud Security Alliance (Cloud Misconfigurations), New York Times vs. OpenAI (2025 Court Documents), ITPro, Outpost24 CompassDRP (Data Leakage Detection), IBM Cost of a Data Breach Report (2023), IBM Cost of Data Breach Report 2025, Anagram: Employee Compliance Report 2025, PromptArmor: Slack AI Exploitation Study, Wiz Research: DeepSeek Vulnerability Disclosure, MIT Project NANDA: State of AI in Business 2025, DeepSeek AI Breach (Example of third-party AI provider leakage) and UK National Cyber Security Centre (NCSC) - Shadow IT Risks.
What is the most recent URL for additional resources on cybersecurity best practices ?
Most Recent URL for Additional Resources: The most recent URL for additional resources on cybersecurity best practices is https://www.itpro.com, https://www.ibm.com/reports/data-breach, https://www.gartner.com/en/conferences, https://www.wiz.io, https://www.promptarmor.com .
Investigation Status
What is the current status of the most recent investigation ?
Current Status of Most Recent Investigation: The current status of the most recent investigation is Resolved (Database Secured).
Stakeholder and Customer Advisories
What was the most recent stakeholder advisory issued ?
Most Recent Stakeholder Advisory: The most recent stakeholder advisory issued was IT and security leaders should prioritize shadow AI as a critical blind spot., Executives must align AI adoption strategies with security and compliance goals., Employees should be trained on the risks of unsanctioned AI tools., CISOs: Prioritize AI governance frameworks and employee training., Legal Teams: Audit AI data retention policies for compliance conflicts., HR: Integrate AI usage into acceptable use policies and disciplinary codes., Board Members: Treat shadow AI as a top-tier enterprise risk., .
What was the most recent customer advisory issued ?
Most Recent Customer Advisory: The most recent customer advisory issued were an Corporate Clients: Demand transparency from AI vendors on data handling/retention.End Users: Avoid sharing sensitive data with consumer AI tools; use enterprise-approved alternatives.Partners: Include AI data protection clauses in contracts (e.g. and right to audit LLM interactions).
Initial Access Broker
What was the most recent reconnaissance period for an incident ?
Most Recent Reconnaissance Period: The most recent reconnaissance period for an incident was Ongoing (years of accumulated prompts in some cases).
Post-Incident Analysis
What was the most significant root cause identified in post-incident analysis ?
Most Significant Root Cause: The most significant root cause identified in post-incident analysis was Misconfiguration, Misconfigured ClickHouse Database (Publicly Accessible)Inadequate Access ControlsLack of Monitoring for Unauthorized Access, Lack of visibility into employee AI tool usageAbsence of clear acceptable use policies for AISlow corporate adoption of sanctioned AI toolsInadequate vendor security assessmentsEmployee frustration with productivity barriers, Lack of AI-Specific Governance (63% of orgs per IBM 2025).Over-Reliance on Employee Compliance (58% admit policy violations).Default Data Retention in LLMs (e.g., OpenAI’s 30-day deletion lag).Inadequate Vendor Risk Management for AI Tools.Cultural Prioritization of Convenience Over Security (71% UK employees use shadow AI).Technical Gaps: No Runtime Controls for AI Interactions..
What was the most significant corrective action taken based on post-incident analysis ?
Most Significant Corrective Action: The most significant corrective action taken based on post-incident analysis was Secured the DatabaseLikely Reviewed Access Controls (Assumed)Potential Implementation of DLP or Monitoring Tools (Assumed), Implement comprehensive AI governance frameworks.Enhance monitoring for unsanctioned AI usage.Foster a culture of security awareness around AI risks.Accelerate adoption of sanctioned AI tools to meet employee needs., Mandate AI Lifecycle Governance (IBM’s 4-pillar framework).Deploy AI Firewalls to Block Unauthorized Tools.Enforce ‘Zero Trust’ for AI: Verify All Prompts/Outputs.Conduct Red-Team Exercises for Prompt Injection Attacks.Partner with AI Vendors for Enterprise-Grade Controls (e.g., private LLMs).Establish Cross-Functional AI Risk Committees (IT, Legal, HR)..
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Latest Global CVEs (Not Company-Specific)
Description
MCP Server Kubernetes is an MCP Server that can connect to a Kubernetes cluster and manage it. Prior to 2.9.8, there is a security issue exists in the exec_in_pod tool of the mcp-server-kubernetes MCP Server. The tool accepts user-provided commands in both array and string formats. When a string format is provided, it is passed directly to shell interpretation (sh -c) without input validation, allowing shell metacharacters to be interpreted. This vulnerability can be exploited through direct command injection or indirect prompt injection attacks, where AI agents may execute commands without explicit user intent. This vulnerability is fixed in 2.9.8.
Risk Information
cvss3
Base: 6.4
Severity: HIGH
CVSS:3.1/AV:N/AC:H/PR:H/UI:R/S:U/C:H/I:H/A:H
Description
XML external entity (XXE) injection in eyoucms v1.7.1 allows remote attackers to cause a denial of service via crafted body of a POST request.
Description
An issue was discovered in Fanvil x210 V2 2.12.20 allowing unauthenticated attackers on the local network to access administrative functions of the device (e.g. file upload, firmware update, reboot...) via a crafted authentication bypass.
Description
Cal.com is open-source scheduling software. Prior to 5.9.8, A flaw in the login credentials provider allows an attacker to bypass password verification when a TOTP code is provided, potentially gaining unauthorized access to user accounts. This issue exists due to problematic conditional logic in the authentication flow. This vulnerability is fixed in 5.9.8.
Risk Information
cvss4
Base: 9.9
Severity: LOW
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Description
Rhino is an open-source implementation of JavaScript written entirely in Java. Prior to 1.8.1, 1.7.15.1, and 1.7.14.1, when an application passed an attacker controlled float poing number into the toFixed() function, it might lead to high CPU consumption and a potential Denial of Service. Small numbers go through this call stack: NativeNumber.numTo > DToA.JS_dtostr > DToA.JS_dtoa > DToA.pow5mult where pow5mult attempts to raise 5 to a ridiculous power. This vulnerability is fixed in 1.8.1, 1.7.15.1, and 1.7.14.1.
Risk Information
cvss4
Base: 5.5
Severity: LOW
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
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