[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"kb-article-eightfold-ai-linkedin-scraping-allegations-and-the-new-era-of-ai-data-governance-en":3,"ArticleBody_qVxbOA7Cv3iw27VHIXX4v7qgHEBK5dM7GKFNhZSIV9w":98},{"article":4,"relatedArticles":66,"locale":56},{"id":5,"title":6,"slug":7,"content":8,"htmlContent":9,"excerpt":10,"category":11,"tags":12,"metaDescription":10,"wordCount":13,"readingTime":14,"publishedAt":15,"sources":16,"sourceCoverage":50,"transparency":51,"seo":55,"language":56,"featuredImage":57,"featuredImageCredit":58,"isFreeGeneration":62,"trendSlug":50,"niche":63,"geoTakeaways":50,"geoFaq":50,"entities":50},"69bfcd4b2f63650529e7cb98","Eightfold AI, LinkedIn Scraping Allegations and the New Era of AI Data Governance","eightfold-ai-linkedin-scraping-allegations-and-the-new-era-of-ai-data-governance","Allegations that an AI vendor scraped LinkedIn-style profiles without authorization highlight a structural weakness: enterprises rely on AI built on data supply chains they neither own nor fully understand.\n\nMeanwhile, state AI acts, de facto standards like NIST’s AI Risk Management Framework, and copyright litigation are creating a far stricter compliance perimeter than most current AI practices. [5][7]\n\nFor CISOs, CDOs, and counsel, this is a live-fire test. The organizations that will fare best can already trace where data comes from, how models are trained, and what obligations attach at every stage of the AI lifecycle.\n\n---\n\n## 1. Reframing the Eightfold–LinkedIn Case as an AI Governance Stress Test\n\nTreating alleged scraping of LinkedIn-style profiles as a narrow vendor issue misses the point. Incidents like this are stress tests of AI governance maturity.\n\nColorado’s AI Act assumes organizations already know: [5]  \n\n- Every AI system in use  \n- How each system works  \n- What data it collects, uses, and outputs  \n\nIn practice, many cannot even inventory AI tools, let alone map data flows.\n\n💡 **Governance reframing question**  \nInstead of asking, “Did this vendor scrape LinkedIn?” ask, “Could we detect if any AI vendor in our stack scraped LinkedIn-style data?”\n\nEmerging AI rules resemble early privacy laws: fragmented, but all demanding lineage of personal data—where it is collected, stored, shared, and processed. [5] Any blind spot magnifies the impact of a single scraping allegation, especially in hiring, promotion, or risk scoring.\n\nSecurity leaders are now expected to ask for every AI system:  \n\n- Where is the data going?  \n- Who has access to it?  \n- What risks does it pose to security and compliance? [2]\n\nThese are the same questions raised when a model is suspected of ingesting social profile data at scale.\n\n⚠️ **Opaque data capture is not hypothetical**  \nSome AI platforms already log:  \n\n- All conversations and prompts  \n- Keystroke patterns and device identifiers  \n- Cross-device tracking data, sometimes stored abroad [1]  \n\nUnmanaged, this collides with expectations of lawful, minimal, and transparent use.\n\nMini-conclusion: treat LinkedIn-style scraping allegations as a forcing function to build a repeatable method to evaluate, approve, and monitor every AI vendor’s data sourcing practices—not as a one-off crisis.\n\n---\n\n## 2. Lessons from DeepSeek and Other AI Platforms on Data Collection Risk\n\nThe broader risk becomes clearer when you look beyond LinkedIn-style scraping to how some AI platforms already operate. DeepSeek is a useful example.\n\nAnalyses of its privacy posture indicate it can collect: [1]  \n\n- User conversations and prompts  \n- Device information and keystroke patterns  \n- Cross-device identifiers, with data reportedly stored in China  \n\nThis mix of broad capture and jurisdictional risk alarms security teams.\n\nCommunity rankings of LLM privacy place: [3]  \n\n- Self-hosted open-source models at the top  \n- DeepSeek at the bottom  \n\nThe lesson: deployment model and data path matter as much as model quality.\n\n💼 **Vendor due diligence proxy test**  \nIf a vendor cannot clearly answer:  \n\n- What they collect  \n- Where it is stored  \n- How it is governed  \n\n…assume similar or greater risk around any scraping of public or semi-public web data.\n\nSecurity guidance for DeepSeek urges organizations to evaluate: [2][4]  \n\n- Data storage and processing locations  \n- Who can access data (including foreign affiliates\u002Fgovernments)  \n- How local laws may compel disclosure  \n\nThe same triad applies when assessing whether training data may include scraped professional profiles.\n\nPrivacy specialists also warn that models such as DeepSeek, ChatGPT, Gemini, and Claude may store or reuse user inputs to improve performance. [8] Sending them sensitive business information or regulated personal data without guardrails can create shadow training corpora you never intended.\n\n⚠️ **Key insight**  \nDeepSeek is a proxy for a broader rule: when AI providers are vague about data collection or retention, assume aggressive scraping and reuse are possible—even when inputs appear “public.”\n\n---\n\n## 3. Regulatory and IP Signals: From AI Governance Acts to Copyright Clashes\n\nThese vendor and data risks now collide with a tightening regulatory and IP environment.\n\nColorado’s AI Act (effective June 2026) requires organizations to document: [5]  \n\n- AI system behavior  \n- Data flows and use  \n- Risk and impact assessments  \n\nIt assumes traceability many enterprises lack today.\n\nIn parallel, the NIST AI Risk Management Framework is emerging as a de facto safe harbor, similar to the NIST Cybersecurity Framework. 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System\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215618792-flowchart-B-1\" data-look=\"classic\" transform=\"translate(317.7109375, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-125.5234375\" y=\"-27\" width=\"251.046875\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-95.5234375, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"191.046875\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Inventory & Classification\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215618792-flowchart-C-3\" data-look=\"classic\" 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opacity=\"0.7\">coreprose.com\u003C\u002Ftext>\u003C\u002Fsvg>\n\u003C\u002Fdiv>\n\nIntellectual property law is also converging on AI training practices. ByteDance’s Seedance 2.0 video model faced rapid escalation after Disney alleged it was trained on copyrighted characters and bundled with a pirated character library. [7] The global launch was paused while safeguards were added to block infringing outputs. [7]\n\nSome governments have also restricted or banned specific AI tools, including certain deployments of DeepSeek, over data sovereignty and outdated safeguards. [4]\n\n⚠️ **Convergence risk**  \nCombine AI governance acts, NIST-style expectations, and copyright disputes like Seedance, and the message is clear: undisclosed scraping of social or professional profiles creates overlapping privacy, IP, and compliance liabilities. [5][7]\n\nA LinkedIn-style scraping allegation can therefore trigger scrutiny from privacy, labor, consumer protection, and copyright regulators—multiplying downside risk.\n\n---\n\n## 4. Technical and Contractual Safeguards Against Risky Data Sourcing\n\nEnterprises must move from concern to control using both technical guardrails and hardened contracts.\n\nSecurity leaders evaluating platforms such as DeepSeek are urged to ask explicitly: [2]  \n\n- Where data flows  \n- Who can access it  \n- How this affects regulatory compliance  \n\nThese questions should be standard in every AI RFP and vendor assessment.\n\nAI privacy guidance further recommends: [8]  \n\n- Avoid sending highly sensitive information to external LLMs  \n- Keep security questionnaires and regulated personal data off public models  \n- Assume user inputs may be stored, reused, or inspected by humans  \n\n💡 **Architecture choice as a control**  \nRunning open-source models on your own infrastructure is repeatedly described as the highest-privacy option: [1][3]  \n\n- Full control over training and fine-tuning data  \n- Control over telemetry and logging  \n- Ability to enforce strict retention and deletion  \n\nLocal tools such as LM Studio let enterprises run models like DeepSeek fully offline so prompts and outputs never leave their environment. 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center;\">\u003Cspan class=\"edgeLabel \">\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"edgeLabel\">\u003Cg class=\"label\" data-id=\"L_E_F_0\" transform=\"translate(0, 0)\">\u003CforeignObject width=\"0\" height=\"0\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" class=\"labelBkg\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"edgeLabel \">\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"nodes\">\u003Cg class=\"node default  \" id=\"diagram-1775215131361-flowchart-A-0\" data-look=\"classic\" transform=\"translate(128.296875, 35)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-67.0703125\" y=\"-27\" width=\"134.140625\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-37.0703125, 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text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Security Review\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215131361-flowchart-C-3\" data-look=\"classic\" transform=\"translate(128.296875, 243)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-120.296875\" y=\"-27\" width=\"240.59375\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-90.296875, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"180.59375\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Pen Testing & Red Team\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" 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transform=\"translate(-58.7265625, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"117.453125\" height=\"24\">\u003Cdiv style=\"color: rgb(255, 255, 255) !important; display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\">\u003Cspan style=\"color:#fff !important\" class=\"nodeLabel \">\u003Cp>Ongoing Audits\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cdefs>\u003Cfilter id=\"diagram-1775215131361-drop-shadow\" height=\"130%\" width=\"130%\">\u003CfeDropShadow dx=\"4\" dy=\"4\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Cdefs>\u003Cfilter id=\"diagram-1775215131361-drop-shadow-small\" height=\"150%\" width=\"150%\">\u003CfeDropShadow dx=\"2\" dy=\"2\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Ctext x=\"251.59375\" y=\"610\" text-anchor=\"end\" fill=\"#6b7280\" stroke=\"#ffffff\" stroke-width=\"3\" paint-order=\"stroke\" font-size=\"11\" font-family=\"system-ui, sans-serif\" opacity=\"0.7\">coreprose.com\u003C\u002Ftext>\u003C\u002Fsvg>\n\u003C\u002Fdiv>\n\nLeading organizations translate this into contracts by: [5][6]  \n\n- Mandating disclosure of material training data sources  \n- Prohibiting unauthorized scraping of named platforms (e.g., LinkedIn)  \n- Requiring alignment with NIST AI RMF or equivalent  \n- Reserving audit rights to validate data provenance claims  \n\nMini-conclusion: your technical architecture and contracts must reinforce each other. One without the other leaves you exposed.\n\n---\n\n## 5. CISO–Legal Playbook for Scraping Allegations and Future-Proofing\n\nWhen scraping allegations hit a tool you use, you need a repeatable playbook.\n\n**Step 1 – Scope and mapping**  \nImmediately:  \n\n- Map all workflows and business units using the implicated tool  \n- Mirror the visibility that laws like Colorado’s AI Act assume you have [5]  \n\nWithout this, you cannot quantify exposure.\n\n**Step 2 – Data classification**  \nIdentify which inputs may have included:  \n\n- Sensitive business information  \n- Regulated or high-risk personal data  \n\nDo this with the understanding that AI chatbots can retain and repurpose what users submit. 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line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Technical Mitigations\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215132076-flowchart-F-9\" data-look=\"classic\" transform=\"translate(1361.640625, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-113.609375\" y=\"-27\" width=\"227.21875\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-83.609375, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"167.21875\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Migration \u002F Hardening\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215132076-flowchart-G-11\" data-look=\"classic\" transform=\"translate(1627.6796875, 47)\">\u003Crect class=\"basic label-container\" style=\"fill:#22c55e !important\" x=\"-102.4296875\" y=\"-27\" width=\"204.859375\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"color:#fff !important\" transform=\"translate(-72.4296875, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"144.859375\" height=\"24\">\u003Cdiv style=\"color: rgb(255, 255, 255) !important; display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\">\u003Cspan style=\"color:#fff !important\" class=\"nodeLabel \">\u003Cp>Continuous Testing\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cdefs>\u003Cfilter id=\"diagram-1775215132076-drop-shadow\" height=\"130%\" width=\"130%\">\u003CfeDropShadow dx=\"4\" dy=\"4\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Cdefs>\u003Cfilter id=\"diagram-1775215132076-drop-shadow-small\" height=\"150%\" width=\"150%\">\u003CfeDropShadow dx=\"2\" dy=\"2\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Ctext x=\"1733.109375\" y=\"114\" text-anchor=\"end\" fill=\"#6b7280\" stroke=\"#ffffff\" stroke-width=\"3\" paint-order=\"stroke\" font-size=\"11\" font-family=\"system-ui, sans-serif\" opacity=\"0.7\">coreprose.com\u003C\u002Ftext>\u003C\u002Fsvg>\n\u003C\u002Fdiv>\n\n**Step 3 – Legal and procurement response**  \n\n- Reopen contracts with implicated vendors  \n- Add clauses on training data sources and attestations  \n- Explicitly prohibit scraping of specified platforms  \n- Tighten jurisdictional and data residency controls [4]  \n\n**Step 4 – Architectural shifts where needed**  \n\nFor higher-risk use cases, consider: [2][9]  \n\n- Self-hosted open-source models (e.g., DeepSeek V3 or R1)  \n- Deployment on your own infrastructure  \n- Leveraging mixture-of-experts and multitoken prediction for strong performance at lower compute cost  \n\nThis makes private deployment viable while preserving strict control over fine-tuning data.\n\n**Step 5 – Continuous testing and hardening**  \n\n- Embed red teaming and penetration testing into AI lifecycle governance  \n- Continuously test for behaviors suggesting training on unauthorized or high-risk scraped data [6]  \n\n⚠️ **Outcome to aim for**  \nThe goal is not zero AI risk, but the ability to rapidly answer, when the next scraping scandal emerges:  \n\n- Where the model is used  \n- What data it touched  \n- What you are contractually entitled to demand from the vendor  \n\n---\n\nAlleged unauthorized scraping of LinkedIn-style data is less an anomaly than a symptom of immature AI supply chains. By combining lessons from DeepSeek’s privacy controversies, Seedance’s copyright clash, and emerging AI governance laws, enterprises can move from reactive outrage to proactive control—demanding transparency into training data, favoring self-hosted architectures, and aligning with frameworks like NIST AI RMF. When you know where your data goes and what your models are trained on, lawsuits become signals to refine governance, not existential threats to your AI strategy.","\u003Cp>Allegations that an AI vendor scraped LinkedIn-style profiles without authorization highlight a structural weakness: enterprises rely on AI built on data supply chains they neither own nor fully understand.\u003C\u002Fp>\n\u003Cp>Meanwhile, state AI acts, de facto standards like NIST’s AI Risk Management Framework, and copyright litigation are creating a far stricter compliance perimeter than most current AI practices. \u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>For CISOs, CDOs, and counsel, this is a live-fire test. The organizations that will fare best can already trace where data comes from, how models are trained, and what obligations attach at every stage of the AI lifecycle.\u003C\u002Fp>\n\u003Chr>\n\u003Ch2>1. Reframing the Eightfold–LinkedIn Case as an AI Governance Stress Test\u003C\u002Fh2>\n\u003Cp>Treating alleged scraping of LinkedIn-style profiles as a narrow vendor issue misses the point. Incidents like this are stress tests of AI governance maturity.\u003C\u002Fp>\n\u003Cp>Colorado’s AI Act assumes organizations already know: \u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Every AI system in use\u003C\u002Fli>\n\u003Cli>How each system works\u003C\u002Fli>\n\u003Cli>What data it collects, uses, and outputs\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>In practice, many cannot even inventory AI tools, let alone map data flows.\u003C\u002Fp>\n\u003Cp>💡 \u003Cstrong>Governance reframing question\u003C\u002Fstrong>\u003Cbr>\nInstead of asking, “Did this vendor scrape LinkedIn?” ask, “Could we detect if any AI vendor in our stack scraped LinkedIn-style data?”\u003C\u002Fp>\n\u003Cp>Emerging AI rules resemble early privacy laws: fragmented, but all demanding lineage of personal data—where it is collected, stored, shared, and processed. \u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa> Any blind spot magnifies the impact of a single scraping allegation, especially in hiring, promotion, or risk scoring.\u003C\u002Fp>\n\u003Cp>Security leaders are now expected to ask for every AI system:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Where is the data going?\u003C\u002Fli>\n\u003Cli>Who has access to it?\u003C\u002Fli>\n\u003Cli>What risks does it pose to security and compliance? \u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>These are the same questions raised when a model is suspected of ingesting social profile data at scale.\u003C\u002Fp>\n\u003Cp>⚠️ \u003Cstrong>Opaque data capture is not hypothetical\u003C\u002Fstrong>\u003Cbr>\nSome AI platforms already log:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>All conversations and prompts\u003C\u002Fli>\n\u003Cli>Keystroke patterns and device identifiers\u003C\u002Fli>\n\u003Cli>Cross-device tracking data, sometimes stored abroad \u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Unmanaged, this collides with expectations of lawful, minimal, and transparent use.\u003C\u002Fp>\n\u003Cp>Mini-conclusion: treat LinkedIn-style scraping allegations as a forcing function to build a repeatable method to evaluate, approve, and monitor every AI vendor’s data sourcing practices—not as a one-off crisis.\u003C\u002Fp>\n\u003Chr>\n\u003Ch2>2. Lessons from DeepSeek and Other AI Platforms on Data Collection Risk\u003C\u002Fh2>\n\u003Cp>The broader risk becomes clearer when you look beyond LinkedIn-style scraping to how some AI platforms already operate. DeepSeek is a useful example.\u003C\u002Fp>\n\u003Cp>Analyses of its privacy posture indicate it can collect: \u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>User conversations and prompts\u003C\u002Fli>\n\u003Cli>Device information and keystroke patterns\u003C\u002Fli>\n\u003Cli>Cross-device identifiers, with data reportedly stored in China\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>This mix of broad capture and jurisdictional risk alarms security teams.\u003C\u002Fp>\n\u003Cp>Community rankings of LLM privacy place: \u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Self-hosted open-source models at the top\u003C\u002Fli>\n\u003Cli>DeepSeek at the bottom\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>The lesson: deployment model and data path matter as much as model quality.\u003C\u002Fp>\n\u003Cp>💼 \u003Cstrong>Vendor due diligence proxy test\u003C\u002Fstrong>\u003Cbr>\nIf a vendor cannot clearly answer:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>What they collect\u003C\u002Fli>\n\u003Cli>Where it is stored\u003C\u002Fli>\n\u003Cli>How it is governed\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>…assume similar or greater risk around any scraping of public or semi-public web data.\u003C\u002Fp>\n\u003Cp>Security guidance for DeepSeek urges organizations to evaluate: \u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Data storage and processing locations\u003C\u002Fli>\n\u003Cli>Who can access data (including foreign affiliates\u002Fgovernments)\u003C\u002Fli>\n\u003Cli>How local laws may compel disclosure\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>The same triad applies when assessing whether training data may include scraped professional profiles.\u003C\u002Fp>\n\u003Cp>Privacy specialists also warn that models such as DeepSeek, ChatGPT, Gemini, and Claude may store or reuse user inputs to improve performance. \u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa> Sending them sensitive business information or regulated personal data without guardrails can create shadow training corpora you never intended.\u003C\u002Fp>\n\u003Cp>⚠️ \u003Cstrong>Key insight\u003C\u002Fstrong>\u003Cbr>\nDeepSeek is a proxy for a broader rule: when AI providers are vague about data collection or retention, assume aggressive scraping and reuse are possible—even when inputs appear “public.”\u003C\u002Fp>\n\u003Chr>\n\u003Ch2>3. Regulatory and IP Signals: From AI Governance Acts to Copyright Clashes\u003C\u002Fh2>\n\u003Cp>These vendor and data risks now collide with a tightening regulatory and IP environment.\u003C\u002Fp>\n\u003Cp>Colorado’s AI Act (effective June 2026) requires organizations to document: \u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>AI system behavior\u003C\u002Fli>\n\u003Cli>Data flows and use\u003C\u002Fli>\n\u003Cli>Risk and impact assessments\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>It assumes traceability many enterprises lack today.\u003C\u002Fp>\n\u003Cp>In parallel, the NIST AI Risk Management Framework is emerging as a de facto safe harbor, similar to the NIST Cybersecurity Framework. \u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa> It expects:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Structured AI risk assessments\u003C\u002Fli>\n\u003Cli>Impact analyses for high-risk use cases\u003C\u002Fli>\n\u003Cli>Continuous monitoring and testing\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>📊 \u003Cstrong>Regulatory alignment flow\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cdiv class=\"mermaid-diagram not-prose my-6\" role=\"img\" aria-label=\"Diagram\">\n\u003Csvg id=\"diagram-1775215618792\" width=\"100%\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" class=\"flowchart\" style=\"max-width: 1549.8125px;\" viewBox=\"0 0 1549.8125 119\" role=\"graphics-document document\" aria-roledescription=\"flowchart-v2\">\u003Cstyle>#diagram-1775215618792{font-family:system-ui,-apple-system,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#diagram-1775215618792 .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#diagram-1775215618792 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xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" class=\"labelBkg\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"edgeLabel \">\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"nodes\">\u003Cg class=\"node default  \" id=\"diagram-1775215618792-flowchart-A-0\" data-look=\"classic\" transform=\"translate(75.09375, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-67.09375\" y=\"-27\" width=\"134.1875\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-37.09375, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"74.1875\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>AI System\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215618792-flowchart-B-1\" data-look=\"classic\" transform=\"translate(317.7109375, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-125.5234375\" y=\"-27\" width=\"251.046875\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-95.5234375, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"191.046875\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Inventory & Classification\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215618792-flowchart-C-3\" data-look=\"classic\" 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width=\"199.375\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-69.6875, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"139.375\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Controls & Policies\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215618792-flowchart-E-7\" data-look=\"classic\" transform=\"translate(1161.7421875, 47)\">\u003Crect class=\"basic label-container\" style=\"fill:#22c55e !important\" x=\"-109.1328125\" y=\"-27\" width=\"218.265625\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"color:#fff !important\" transform=\"translate(-79.1328125, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"158.265625\" height=\"24\">\u003Cdiv style=\"color: rgb(255, 255, 255) !important; display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\">\u003Cspan style=\"color:#fff !important\" class=\"nodeLabel \">\u003Cp>Monitoring & Testing\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215618792-flowchart-F-9\" data-look=\"classic\" transform=\"translate(1431.34375, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-110.46875\" y=\"-27\" width=\"220.9375\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-80.46875, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"160.9375\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Regulatory Reporting\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cdefs>\u003Cfilter id=\"diagram-1775215618792-drop-shadow\" height=\"130%\" width=\"130%\">\u003CfeDropShadow dx=\"4\" dy=\"4\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Cdefs>\u003Cfilter id=\"diagram-1775215618792-drop-shadow-small\" height=\"150%\" width=\"150%\">\u003CfeDropShadow dx=\"2\" dy=\"2\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Ctext x=\"1544.8125\" y=\"114\" text-anchor=\"end\" fill=\"#6b7280\" stroke=\"#ffffff\" stroke-width=\"3\" paint-order=\"stroke\" font-size=\"11\" font-family=\"system-ui, sans-serif\" opacity=\"0.7\">coreprose.com\u003C\u002Ftext>\u003C\u002Fsvg>\n\u003C\u002Fdiv>\n\u003Cp>Intellectual property law is also converging on AI training practices. ByteDance’s Seedance 2.0 video model faced rapid escalation after Disney alleged it was trained on copyrighted characters and bundled with a pirated character library. \u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa> The global launch was paused while safeguards were added to block infringing outputs. \u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>Some governments have also restricted or banned specific AI tools, including certain deployments of DeepSeek, over data sovereignty and outdated safeguards. \u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>⚠️ \u003Cstrong>Convergence risk\u003C\u002Fstrong>\u003Cbr>\nCombine AI governance acts, NIST-style expectations, and copyright disputes like Seedance, and the message is clear: undisclosed scraping of social or professional profiles creates overlapping privacy, IP, and compliance liabilities. \u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>A LinkedIn-style scraping allegation can therefore trigger scrutiny from privacy, labor, consumer protection, and copyright regulators—multiplying downside risk.\u003C\u002Fp>\n\u003Chr>\n\u003Ch2>4. Technical and Contractual Safeguards Against Risky Data Sourcing\u003C\u002Fh2>\n\u003Cp>Enterprises must move from concern to control using both technical guardrails and hardened contracts.\u003C\u002Fp>\n\u003Cp>Security leaders evaluating platforms such as DeepSeek are urged to ask explicitly: \u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Where data flows\u003C\u002Fli>\n\u003Cli>Who can access it\u003C\u002Fli>\n\u003Cli>How this affects regulatory compliance\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>These questions should be standard in every AI RFP and vendor assessment.\u003C\u002Fp>\n\u003Cp>AI privacy guidance further recommends: \u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Avoid sending highly sensitive information to external LLMs\u003C\u002Fli>\n\u003Cli>Keep security questionnaires and regulated personal data off public models\u003C\u002Fli>\n\u003Cli>Assume user inputs may be stored, reused, or inspected by humans\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>💡 \u003Cstrong>Architecture choice as a control\u003C\u002Fstrong>\u003Cbr>\nRunning open-source models on your own infrastructure is repeatedly described as the highest-privacy option: \u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Full control over training and fine-tuning data\u003C\u002Fli>\n\u003Cli>Control over telemetry and logging\u003C\u002Fli>\n\u003Cli>Ability to enforce strict retention and deletion\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Local tools such as LM Studio let enterprises run models like DeepSeek fully offline so prompts and outputs never leave their environment. \u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>From a security perspective, LLM penetration testing emphasizes: \u003Ca href=\"#source-6\" class=\"citation-link\" title=\"View source [6]\">[6]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Sandboxing models\u003C\u002Fli>\n\u003Cli>Red teaming for prompt injection and jailbreaks\u003C\u002Fli>\n\u003Cli>Automated scans for poisoned data and unexpected behaviors\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>These methods can reveal signs that a model behaves as though trained on scraped or unauthorized sources.\u003C\u002Fp>\n\u003Cdiv class=\"mermaid-diagram not-prose my-6\" role=\"img\" aria-label=\"Diagram\">\n\u003Csvg id=\"diagram-1775215131361\" width=\"100%\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" class=\"flowchart\" style=\"max-width: 256.59375px;\" viewBox=\"0 0 256.59375 615\" role=\"graphics-document document\" 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width=\"0\" height=\"0\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" class=\"labelBkg\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"edgeLabel \">\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"edgeLabel\">\u003Cg class=\"label\" data-id=\"L_E_F_0\" transform=\"translate(0, 0)\">\u003CforeignObject width=\"0\" height=\"0\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" class=\"labelBkg\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"edgeLabel \">\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"nodes\">\u003Cg class=\"node default  \" id=\"diagram-1775215131361-flowchart-A-0\" data-look=\"classic\" transform=\"translate(128.296875, 35)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-67.0703125\" y=\"-27\" width=\"134.140625\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-37.0703125, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"74.140625\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>AI Vendor\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215131361-flowchart-B-1\" data-look=\"classic\" transform=\"translate(128.296875, 139)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-88.203125\" y=\"-27\" width=\"176.40625\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-58.203125, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"116.40625\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Security Review\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215131361-flowchart-C-3\" data-look=\"classic\" transform=\"translate(128.296875, 243)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-120.296875\" y=\"-27\" width=\"240.59375\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-90.296875, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"180.59375\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Pen Testing & Red Team\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215131361-flowchart-D-5\" data-look=\"classic\" transform=\"translate(128.296875, 347)\">\u003Crect class=\"basic label-container\" style=\"fill:#f59e0b !important\" x=\"-116.453125\" y=\"-27\" width=\"232.90625\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"color:#000 !important\" transform=\"translate(-86.453125, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"172.90625\" height=\"24\">\u003Cdiv style=\"color: rgb(0, 0, 0) !important; display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\">\u003Cspan style=\"color:#000 !important\" class=\"nodeLabel \">\u003Cp>Data Source Disclosure\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215131361-flowchart-E-7\" data-look=\"classic\" transform=\"translate(128.296875, 451)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-107.2734375\" y=\"-27\" width=\"214.546875\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-77.2734375, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"154.546875\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Contractual Controls\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215131361-flowchart-F-9\" data-look=\"classic\" transform=\"translate(128.296875, 555)\">\u003Crect class=\"basic label-container\" style=\"fill:#22c55e !important\" x=\"-88.7265625\" y=\"-27\" width=\"177.453125\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"color:#fff !important\" transform=\"translate(-58.7265625, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"117.453125\" height=\"24\">\u003Cdiv style=\"color: rgb(255, 255, 255) !important; display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\">\u003Cspan style=\"color:#fff !important\" class=\"nodeLabel \">\u003Cp>Ongoing Audits\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cdefs>\u003Cfilter id=\"diagram-1775215131361-drop-shadow\" height=\"130%\" width=\"130%\">\u003CfeDropShadow dx=\"4\" dy=\"4\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Cdefs>\u003Cfilter id=\"diagram-1775215131361-drop-shadow-small\" height=\"150%\" width=\"150%\">\u003CfeDropShadow dx=\"2\" dy=\"2\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Ctext x=\"251.59375\" y=\"610\" text-anchor=\"end\" fill=\"#6b7280\" stroke=\"#ffffff\" stroke-width=\"3\" paint-order=\"stroke\" font-size=\"11\" font-family=\"system-ui, sans-serif\" opacity=\"0.7\">coreprose.com\u003C\u002Ftext>\u003C\u002Fsvg>\n\u003C\u002Fdiv>\n\u003Cp>Leading organizations translate this into contracts by: \u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003Ca href=\"#source-6\" class=\"citation-link\" title=\"View source [6]\">[6]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Mandating disclosure of material training data sources\u003C\u002Fli>\n\u003Cli>Prohibiting unauthorized scraping of named platforms (e.g., LinkedIn)\u003C\u002Fli>\n\u003Cli>Requiring alignment with NIST AI RMF or equivalent\u003C\u002Fli>\n\u003Cli>Reserving audit rights to validate data provenance claims\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Mini-conclusion: your technical architecture and contracts must reinforce each other. One without the other leaves you exposed.\u003C\u002Fp>\n\u003Chr>\n\u003Ch2>5. CISO–Legal Playbook for Scraping Allegations and Future-Proofing\u003C\u002Fh2>\n\u003Cp>When scraping allegations hit a tool you use, you need a repeatable playbook.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Step 1 – Scope and mapping\u003C\u002Fstrong>\u003Cbr>\nImmediately:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Map all workflows and business units using the implicated tool\u003C\u002Fli>\n\u003Cli>Mirror the visibility that laws like Colorado’s AI Act assume you have \u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Without this, you cannot quantify exposure.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Step 2 – Data classification\u003C\u002Fstrong>\u003Cbr>\nIdentify which inputs may have included:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Sensitive business information\u003C\u002Fli>\n\u003Cli>Regulated or high-risk personal data\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Do this with the understanding that AI chatbots can retain and repurpose what users submit. \u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>⚡ \u003Cstrong>Incident response playbook\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cdiv class=\"mermaid-diagram not-prose my-6\" role=\"img\" aria-label=\"Diagram\">\n\u003Csvg id=\"diagram-1775215132076\" width=\"100%\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" class=\"flowchart\" style=\"max-width: 1738.109375px;\" viewBox=\"0 0 1738.109375 119\" role=\"graphics-document document\" aria-roledescription=\"flowchart-v2\">\u003Cstyle>#diagram-1775215132076{font-family:system-ui,-apple-system,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#diagram-1775215132076 .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#diagram-1775215132076 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-12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"75.796875\" height=\"24\">\u003Cdiv style=\"color: rgb(255, 255, 255) !important; display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\">\u003Cspan style=\"color:#fff !important\" class=\"nodeLabel \">\u003Cp>Allegation\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215132076-flowchart-B-1\" data-look=\"classic\" transform=\"translate(282.9921875, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-89.1953125\" y=\"-27\" width=\"178.390625\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-59.1953125, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"118.390625\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Usage Mapping\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215132076-flowchart-C-3\" data-look=\"classic\" transform=\"translate(521.1953125, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-99.0078125\" y=\"-27\" width=\"198.015625\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-69.0078125, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"138.015625\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Data Classification\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215132076-flowchart-D-5\" data-look=\"classic\" transform=\"translate(800.203125, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-130\" y=\"-39\" width=\"260\" height=\"78\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-100, -24)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"200\" height=\"48\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table; white-space: break-spaces; line-height: 1.5; max-width: 200px; text-align: center; width: 200px;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Legal & Procurement Review\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215132076-flowchart-E-7\" data-look=\"classic\" transform=\"translate(1089.1171875, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-108.9140625\" y=\"-27\" width=\"217.828125\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-78.9140625, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"157.828125\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Technical Mitigations\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215132076-flowchart-F-9\" data-look=\"classic\" transform=\"translate(1361.640625, 47)\">\u003Crect class=\"basic label-container\" style=\"\" x=\"-113.609375\" y=\"-27\" width=\"227.21875\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"\" transform=\"translate(-83.609375, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"167.21875\" height=\"24\">\u003Cdiv xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\" style=\"display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\">\u003Cspan class=\"nodeLabel \">\u003Cp>Migration \u002F Hardening\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003Cg class=\"node default  \" id=\"diagram-1775215132076-flowchart-G-11\" data-look=\"classic\" transform=\"translate(1627.6796875, 47)\">\u003Crect class=\"basic label-container\" style=\"fill:#22c55e !important\" x=\"-102.4296875\" y=\"-27\" width=\"204.859375\" height=\"54\">\u003C\u002Frect>\u003Cg class=\"label\" style=\"color:#fff !important\" transform=\"translate(-72.4296875, -12)\">\u003Crect>\u003C\u002Frect>\u003CforeignObject width=\"144.859375\" height=\"24\">\u003Cdiv style=\"color: rgb(255, 255, 255) !important; display: table-cell; white-space: nowrap; line-height: 1.5; max-width: 200px; text-align: center;\" xmlns=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxhtml\">\u003Cspan style=\"color:#fff !important\" class=\"nodeLabel \">\u003Cp>Continuous Testing\u003C\u002Fp>\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002FforeignObject>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003C\u002Fg>\u003Cdefs>\u003Cfilter id=\"diagram-1775215132076-drop-shadow\" height=\"130%\" width=\"130%\">\u003CfeDropShadow dx=\"4\" dy=\"4\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Cdefs>\u003Cfilter id=\"diagram-1775215132076-drop-shadow-small\" height=\"150%\" width=\"150%\">\u003CfeDropShadow dx=\"2\" dy=\"2\" stdDeviation=\"0\" flood-opacity=\"0.06\" flood-color=\"#000000\">\u003C\u002FfeDropShadow>\u003C\u002Ffilter>\u003C\u002Fdefs>\u003Ctext x=\"1733.109375\" y=\"114\" text-anchor=\"end\" fill=\"#6b7280\" stroke=\"#ffffff\" stroke-width=\"3\" paint-order=\"stroke\" font-size=\"11\" font-family=\"system-ui, sans-serif\" opacity=\"0.7\">coreprose.com\u003C\u002Ftext>\u003C\u002Fsvg>\n\u003C\u002Fdiv>\n\u003Cp>\u003Cstrong>Step 3 – Legal and procurement response\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Reopen contracts with implicated vendors\u003C\u002Fli>\n\u003Cli>Add clauses on training data sources and attestations\u003C\u002Fli>\n\u003Cli>Explicitly prohibit scraping of specified platforms\u003C\u002Fli>\n\u003Cli>Tighten jurisdictional and data residency controls \u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>Step 4 – Architectural shifts where needed\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>For higher-risk use cases, consider: \u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003Ca href=\"#source-9\" class=\"citation-link\" title=\"View source [9]\">[9]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Self-hosted open-source models (e.g., DeepSeek V3 or R1)\u003C\u002Fli>\n\u003Cli>Deployment on your own infrastructure\u003C\u002Fli>\n\u003Cli>Leveraging mixture-of-experts and multitoken prediction for strong performance at lower compute cost\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>This makes private deployment viable while preserving strict control over fine-tuning data.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Step 5 – Continuous testing and hardening\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Embed red teaming and penetration testing into AI lifecycle governance\u003C\u002Fli>\n\u003Cli>Continuously test for behaviors suggesting training on unauthorized or high-risk scraped data \u003Ca href=\"#source-6\" class=\"citation-link\" title=\"View source [6]\">[6]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>⚠️ \u003Cstrong>Outcome to aim for\u003C\u002Fstrong>\u003Cbr>\nThe goal is not zero AI risk, but the ability to rapidly answer, when the next scraping scandal emerges:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Where the model is used\u003C\u002Fli>\n\u003Cli>What data it touched\u003C\u002Fli>\n\u003Cli>What you are contractually entitled to demand from the vendor\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Chr>\n\u003Cp>Alleged unauthorized scraping of LinkedIn-style data is less an anomaly than a symptom of immature AI supply chains. By combining lessons from DeepSeek’s privacy controversies, Seedance’s copyright clash, and emerging AI governance laws, enterprises can move from reactive outrage to proactive control—demanding transparency into training data, favoring self-hosted architectures, and aligning with frameworks like NIST AI RMF. When you know where your data goes and what your models are trained on, lawsuits become signals to refine governance, not existential threats to your AI strategy.\u003C\u002Fp>\n","Allegations that an AI vendor scraped LinkedIn-style profiles without authorization highlight a structural weakness: enterprises rely on AI built on data supply chains they neither own nor fully under...","privacy",[],1599,8,"2026-03-22T11:08:36.596Z",[17,22,26,30,34,38,42,46],{"title":18,"url":19,"summary":20,"type":21},"EXPOSED: DeepSeek AI's Privacy Issues (& How to Run it 100% Offline)","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=iHXslhEdRLA","# EXPOSED: DeepSeek AI's Privacy Issues (& How to Run it 100% Offline)\n\nAI in Education\n\n8,135 views 1 year ago\n\nDeepSeek AI is collecting more data than you think! In this video, I break down their p...","kb",{"title":23,"url":24,"summary":25,"type":21},"DeepSeek AI: What Security Leaders Need to Know About Its Security Risks","https:\u002F\u002Fwww.esentire.com\u002Fblog\u002Fdeepseek-ai-what-security-leaders-need-to-know-about-its-security-risks","Artificial intelligence is evolving at a rapid pace, and organizations are increasingly looking for ways to leverage it without compromising security. DeepSeek AI, a Chinese-developed model, has gaine...",{"title":27,"url":28,"summary":29,"type":21},"Privacy Concerns with LLM Models (and DeepSeek in particular)","https:\u002F\u002Fwww.reddit.com\u002Fr\u002FLocalLLaMA\u002Fcomments\u002F1i1ugj5\u002Fprivacy_concerns_with_llm_models_and_deepseek_in\u002F","By MindIndividual4397 • 1y ago\n\nThere have been growing concerns about privacy when it comes to using AI models like DeepSeek, and these concerns are valid. To help clarify, here's a quick ranking of ...",{"title":31,"url":32,"summary":33,"type":21},"AI Governance Regulations in the US: What You Need to Know","https:\u002F\u002Fseekmaro.com\u002Fblog\u002Fai-governance-regulations","AI Governance Regulations in the US: What You Need to Know\n\nJadon Montero \u002F\u002F March 4, 2026\n\nA guide to the state laws currently shaping AI governance and what it takes to actually comply, plus why thi...",{"title":35,"url":36,"summary":37,"type":21},"AI Model Penetration: Testing LLMs for Prompt Injection & Jailbreaks","https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xOQW_qMZdlc","AI models aren’t impenetrable—prompt injections, jailbreaks, and poisoned data can compromise them. 🔒 Jeff Crume explains penetration testing methods like sandboxing, red teaming, and automated scans...",{"title":39,"url":40,"summary":41,"type":21},"ByteDance delays Seedance 2.0 launch over copyright disputes","https:\u002F\u002Fwww.techinasia.com\u002Fnews\u002Fbytedance-delays-seedance-2-0-launch-over-copyright-disputes","ByteDance paused the planned global launch of its AI video model Seedance 2.0 after a series of copyright disputes with major Hollywood studios and streaming platforms, The Information reported.\n\nDisn...",{"title":43,"url":44,"summary":45,"type":21},"AI Privacy Risks: Is DeepSeek Safe for Your Business Data?","https:\u002F\u002Fvendict.com\u002Fblog\u002Fai-privacy-risks-is-deepseek-safe-for-your-business-data","AI is advancing fast, and DeepSeek, a Chinese AI model, is gaining attention for its reasoning skills, open-source approach, and integration into platforms like WeChat (the versatile and hugely popula...",{"title":47,"url":48,"summary":49,"type":21},"What is DeepSeek? A full breakdown of the disruptive open-source LLM","https:\u002F\u002Fwww.tenable.com\u002Fcybersecurity-guide\u002Flearn\u002Fdeepseek-ai-guide","What is DeepSeek AI?\n\nFounded in 2023 by Liang Wenfeng, DeepSeek is a China-based AI company that develops high-performance large language models (LLMs). 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