[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"kb-article-ai-social-workers-gone-wrong-why-chatgpt-should-never-decide-a-child-s-future-en":3,"ArticleBody_Ruxzja0mRUtOsEzPU9wV7Lf6rzquGfNyfIsbiyt6w":105},{"article":4,"relatedArticles":75,"locale":65},{"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":58,"transparency":59,"seo":64,"language":65,"featuredImage":66,"featuredImageCredit":67,"isFreeGeneration":71,"trendSlug":58,"niche":72,"geoTakeaways":58,"geoFaq":58,"entities":58},"699047b1f49ebddd2143dd8b","AI Social Workers Gone Wrong: Why ChatGPT Should Never Decide a Child’s Future","ai-social-workers-gone-wrong-why-chatgpt-should-never-decide-a-child-s-future","Child welfare agencies face crushing caseloads and budget pressure. Generative AI looks tempting: draft notes, flag risk, suggest placements.  \n\nBut tools like ChatGPT are probabilistic text engines, not evidence‑based decision‑makers. Treating them as informal “social workers” collapses the gap between drafting a note and deciding a child’s fate, importing unresolved AI risks into one of the most fragile systems in public life.  \n\n> ⚠️ Key warning: In child protection, “good enough” automation is not good enough. The bar must be closer to “failure is unacceptable.”\n\n---\n\n## 1. Why AI “Social Workers” Are Structurally Unsafe for Child Decisions\n\nLLMs generate the *most likely next word*, not verified facts. They:\n\n- Produce different answers to the same prompt, some fabricated but fluent[2]  \n- Hallucinate details, omit context, and contradict themselves, even with guardrails[2][3]  \n- Have already hallucinated insurance coverage and bank policies, quietly breaking compliance[3]  \n\nIn child welfare, an AI that invents a risk indicator or misstates legal thresholds is not just wrong; it may be unlawful.\n\nReal‑world failures show how “non‑malicious” AI harms vulnerable people:\n\n- Facial recognition misidentifications have contributed to wrongful arrests when treated as proof[11]  \n- Chatbots have given confident but incorrect medical and financial guidance, undermining safety and trust[11]  \n\nIf workers over‑trust an AI‑generated risk score, an error can separate a child from a fit parent.\n\nAccountability is diffuse:\n\n- Designers choose data and architectures  \n- Executives decide where models are embedded  \n- Front‑line staff operationalize outputs[1]  \n\nWhen semi‑autonomous agents go wrong, blame spreads across this chain, creating the liability vacuum child protection law is meant to prevent.[6]\n\nPrivacy risks are built in:\n\n- Pasting family histories or court documents into public chatbots can store or reuse that data, leaking highly sensitive information about children.[5][10]\n\nBias is also intrinsic:\n\n- LLMs inherit biases from opaque datasets and can amplify discrimination against already over‑surveilled communities.[12][9]  \n- In a system with documented racial and socioeconomic disparities, letting a biased model “score” families threatens equal protection.\n\n> 💡 Key takeaway: A system that naturally hallucinates, drifts, and encodes bias is structurally incompatible with being treated as a decision‑maker in child protection.\n\n---\n\n## 2. How Misuse of ChatGPT Creeps into Child Welfare Workflows\n\nThese structural problems surface through “shadow tools” and weak governance.\n\nInformal use is already common:\n\n- Staff in many sectors copy internal documents into public LLMs despite policies, exposing confidential data.[5][10]  \n- Overloaded child welfare workers will predictably do the same unless blocked and trained.\n\nA 2026 survey found:\n\n- 87% of companies use AI in core operations  \n- AI‑related errors and rework cost over $67 billion annually[8]  \n\nThe push to “use AI everywhere” is reaching public services. In HR, over‑reliance on AI has:\n\n- Filtered out 38% of top‑level candidates before human review because models overweighted keywords[8]  \n\nSimilar triage in child welfare could quietly sideline families who do not “speak the system’s language.”\n\nBusiness alignment failures are common: chatbots hallucinate coverage, omit conditions, or contradict policy while sounding authoritative.[3] In child welfare, this could mean:\n\n- Invented risk factors (“prior neglect report” that never happened)  \n- Misapplied legal standards (“imminent danger” misdefined)  \n- Fabricated rationales inserted into case narratives  \n\nThese errors may be invisible to families and overworked supervisors yet shape life‑altering decisions.\n\nIf agencies connect LLM agents to case management systems, threat models worsen. Prompt injection attacks already:\n\n- Coerce AI agents into exfiltrating data  \n- Bypass internal instructions[4][7][10]  \n\nIn child welfare, adversarial text in an email, social media post, or uploaded document could push an AI “assistant” to:\n\n- Disclose sealed or anonymized records  \n- Alter risk assessments  \n- Generate recommendations that contradict statute or policy  \n\nUnder‑secured deployments are not hypothetical:\n\n- Microsoft’s Tay and Bing’s Sydney produced offensive, manipulative content without robust red‑teaming[9]  \n- A Lenovo chatbot was tricked with a short prompt into generating malicious code because guardrails were absent[4]  \n\nMost social agencies lack the budget and expertise for this level of security testing before plugging AI into child protection workflows.\n\n> ⚠️ Critical point: The same failure patterns that embarrass banks or retailers can irreparably damage children’s lives when transposed into welfare systems.\n\n---\n\n## 3. Governance Blueprint: Safe, Limited AI Use in Child Protection\n\nThe answer is not “no AI ever,” but “no AI near the decision lever.” Agencies can adopt a constrained, governance‑first model.\n\n**1. Block unsanctioned AI**\n\n- Use network controls to prevent access to public LLMs from agency devices  \n- Route any approved use through secure gateways with logging and data‑loss prevention[5]  \n- Prefer private or on‑prem models with guarantees that children’s data is not used for training[5][10]\n\n**2. Wrap sanctioned tools in guardrails**\n\n- Validators to detect\u002Fredact personal data  \n- Bias scoring and mitigation  \n- Hallucination checks against authoritative sources  \n- Enforcement of domain‑specific rules, with violations auto‑routed to human review[2][4][3]\n\n**3. Keep AI strictly assistive**\n\n- Limit AI to narrow tasks (summaries, checklists, drafting)  \n- Preserve human control over all final decisions, with clear accountability records[8][6]  \n- Make this principle non‑negotiable in child protection.\n\n**4. Institutionalize red‑teaming**\n\n- Use structured scenario testing, inspired by the MIT AI Incident Database, to probe privacy, fairness, and reliability before deployment.[9][11][7]\n\n**5. Ensure transparent governance**\n\nCommunities should see:\n\n- Data sources and exclusions  \n- Fairness and bias evaluation methods  \n- Oversight structures and appeal processes[12][6][1]  \n\nChild welfare must not become another opaque AI black box.\n\n> 💡 Key takeaway: Safe use of AI in child protection is possible only when models are tightly scoped, technically constrained, and always subordinate to accountable human professionals.\n\n---\n\nTreating ChatGPT as an informal social worker erases the boundary between drafting text and determining a child’s future, importing unresolved AI risks into an already overstretched system.  \n\nIf you oversee child or family services, urgently audit how staff use AI, freeze unsupported high‑stakes use, and convene practitioners, technologists, and ethicists to design a disciplined, rights‑respecting AI policy—before the technology quietly rewrites how your agency makes life‑changing decisions.","\u003Cp>Child welfare agencies face crushing caseloads and budget pressure. Generative AI looks tempting: draft notes, flag risk, suggest placements.\u003C\u002Fp>\n\u003Cp>But tools like ChatGPT are probabilistic text engines, not evidence‑based decision‑makers. Treating them as informal “social workers” collapses the gap between drafting a note and deciding a child’s fate, importing unresolved AI risks into one of the most fragile systems in public life.\u003C\u002Fp>\n\u003Cblockquote>\n\u003Cp>⚠️ Key warning: In child protection, “good enough” automation is not good enough. The bar must be closer to “failure is unacceptable.”\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Chr>\n\u003Ch2>1. Why AI “Social Workers” Are Structurally Unsafe for Child Decisions\u003C\u002Fh2>\n\u003Cp>LLMs generate the \u003Cem>most likely next word\u003C\u002Fem>, not verified facts. They:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Produce different answers to the same prompt, some fabricated but fluent\u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Hallucinate details, omit context, and contradict themselves, even with guardrails\u003Ca href=\"#source-2\" class=\"citation-link\" title=\"View source [2]\">[2]\u003C\u002Fa>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Have already hallucinated insurance coverage and bank policies, quietly breaking compliance\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>In child welfare, an AI that invents a risk indicator or misstates legal thresholds is not just wrong; it may be unlawful.\u003C\u002Fp>\n\u003Cp>Real‑world failures show how “non‑malicious” AI harms vulnerable people:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Facial recognition misidentifications have contributed to wrongful arrests when treated as proof\u003Ca href=\"#source-11\" class=\"citation-link\" title=\"View source [11]\">[11]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Chatbots have given confident but incorrect medical and financial guidance, undermining safety and trust\u003Ca href=\"#source-11\" class=\"citation-link\" title=\"View source [11]\">[11]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>If workers over‑trust an AI‑generated risk score, an error can separate a child from a fit parent.\u003C\u002Fp>\n\u003Cp>Accountability is diffuse:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Designers choose data and architectures\u003C\u002Fli>\n\u003Cli>Executives decide where models are embedded\u003C\u002Fli>\n\u003Cli>Front‑line staff operationalize outputs\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>When semi‑autonomous agents go wrong, blame spreads across this chain, creating the liability vacuum child protection law is meant to prevent.\u003Ca href=\"#source-6\" class=\"citation-link\" title=\"View source [6]\">[6]\u003C\u002Fa>\u003C\u002Fp>\n\u003Cp>Privacy risks are built in:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Pasting family histories or court documents into public chatbots can store or reuse that data, leaking highly sensitive information about children.\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003Ca href=\"#source-10\" class=\"citation-link\" title=\"View source [10]\">[10]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Bias is also intrinsic:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>LLMs inherit biases from opaque datasets and can amplify discrimination against already over‑surveilled communities.\u003Ca href=\"#source-12\" class=\"citation-link\" title=\"View source [12]\">[12]\u003C\u002Fa>\u003Ca href=\"#source-9\" class=\"citation-link\" title=\"View source [9]\">[9]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>In a system with documented racial and socioeconomic disparities, letting a biased model “score” families threatens equal protection.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cblockquote>\n\u003Cp>💡 Key takeaway: A system that naturally hallucinates, drifts, and encodes bias is structurally incompatible with being treated as a decision‑maker in child protection.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Chr>\n\u003Ch2>2. How Misuse of ChatGPT Creeps into Child Welfare Workflows\u003C\u002Fh2>\n\u003Cp>These structural problems surface through “shadow tools” and weak governance.\u003C\u002Fp>\n\u003Cp>Informal use is already common:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Staff in many sectors copy internal documents into public LLMs despite policies, exposing confidential data.\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003Ca href=\"#source-10\" class=\"citation-link\" title=\"View source [10]\">[10]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Overloaded child welfare workers will predictably do the same unless blocked and trained.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>A 2026 survey found:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>87% of companies use AI in core operations\u003C\u002Fli>\n\u003Cli>AI‑related errors and rework cost over $67 billion annually\u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>The push to “use AI everywhere” is reaching public services. In HR, over‑reliance on AI has:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Filtered out 38% of top‑level candidates before human review because models overweighted keywords\u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Similar triage in child welfare could quietly sideline families who do not “speak the system’s language.”\u003C\u002Fp>\n\u003Cp>Business alignment failures are common: chatbots hallucinate coverage, omit conditions, or contradict policy while sounding authoritative.\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa> In child welfare, this could mean:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Invented risk factors (“prior neglect report” that never happened)\u003C\u002Fli>\n\u003Cli>Misapplied legal standards (“imminent danger” misdefined)\u003C\u002Fli>\n\u003Cli>Fabricated rationales inserted into case narratives\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>These errors may be invisible to families and overworked supervisors yet shape life‑altering decisions.\u003C\u002Fp>\n\u003Cp>If agencies connect LLM agents to case management systems, threat models worsen. Prompt injection attacks already:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Coerce AI agents into exfiltrating data\u003C\u002Fli>\n\u003Cli>Bypass internal instructions\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa>\u003Ca href=\"#source-10\" class=\"citation-link\" title=\"View source [10]\">[10]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>In child welfare, adversarial text in an email, social media post, or uploaded document could push an AI “assistant” to:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Disclose sealed or anonymized records\u003C\u002Fli>\n\u003Cli>Alter risk assessments\u003C\u002Fli>\n\u003Cli>Generate recommendations that contradict statute or policy\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Under‑secured deployments are not hypothetical:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Microsoft’s Tay and Bing’s Sydney produced offensive, manipulative content without robust red‑teaming\u003Ca href=\"#source-9\" class=\"citation-link\" title=\"View source [9]\">[9]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>A Lenovo chatbot was tricked with a short prompt into generating malicious code because guardrails were absent\u003Ca href=\"#source-4\" class=\"citation-link\" title=\"View source [4]\">[4]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Most social agencies lack the budget and expertise for this level of security testing before plugging AI into child protection workflows.\u003C\u002Fp>\n\u003Cblockquote>\n\u003Cp>⚠️ Critical point: The same failure patterns that embarrass banks or retailers can irreparably damage children’s lives when transposed into welfare systems.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Chr>\n\u003Ch2>3. Governance Blueprint: Safe, Limited AI Use in Child Protection\u003C\u002Fh2>\n\u003Cp>The answer is not “no AI ever,” but “no AI near the decision lever.” Agencies can adopt a constrained, governance‑first model.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>1. Block unsanctioned AI\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Use network controls to prevent access to public LLMs from agency devices\u003C\u002Fli>\n\u003Cli>Route any approved use through secure gateways with logging and data‑loss prevention\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Prefer private or on‑prem models with guarantees that children’s data is not used for training\u003Ca href=\"#source-5\" class=\"citation-link\" title=\"View source [5]\">[5]\u003C\u002Fa>\u003Ca href=\"#source-10\" class=\"citation-link\" title=\"View source [10]\">[10]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>2. Wrap sanctioned tools in guardrails\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Validators to detect\u002Fredact personal data\u003C\u002Fli>\n\u003Cli>Bias scoring and mitigation\u003C\u002Fli>\n\u003Cli>Hallucination checks against authoritative sources\u003C\u002Fli>\n\u003Cli>Enforcement of domain‑specific rules, with violations auto‑routed to human review\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>\u003Ca href=\"#source-3\" class=\"citation-link\" title=\"View source [3]\">[3]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>3. Keep AI strictly assistive\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Limit AI to narrow tasks (summaries, checklists, drafting)\u003C\u002Fli>\n\u003Cli>Preserve human control over all final decisions, with clear accountability records\u003Ca href=\"#source-8\" class=\"citation-link\" title=\"View source [8]\">[8]\u003C\u002Fa>\u003Ca href=\"#source-6\" class=\"citation-link\" title=\"View source [6]\">[6]\u003C\u002Fa>\u003C\u002Fli>\n\u003Cli>Make this principle non‑negotiable in child protection.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>4. Institutionalize red‑teaming\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Use structured scenario testing, inspired by the MIT AI Incident Database, to probe privacy, fairness, and reliability before deployment.\u003Ca href=\"#source-9\" class=\"citation-link\" title=\"View source [9]\">[9]\u003C\u002Fa>\u003Ca href=\"#source-11\" class=\"citation-link\" title=\"View source [11]\">[11]\u003C\u002Fa>\u003Ca href=\"#source-7\" class=\"citation-link\" title=\"View source [7]\">[7]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cstrong>5. Ensure transparent governance\u003C\u002Fstrong>\u003C\u002Fp>\n\u003Cp>Communities should see:\u003C\u002Fp>\n\u003Cul>\n\u003Cli>Data sources and exclusions\u003C\u002Fli>\n\u003Cli>Fairness and bias evaluation methods\u003C\u002Fli>\n\u003Cli>Oversight structures and appeal processes\u003Ca href=\"#source-12\" class=\"citation-link\" title=\"View source [12]\">[12]\u003C\u002Fa>\u003Ca href=\"#source-6\" class=\"citation-link\" title=\"View source [6]\">[6]\u003C\u002Fa>\u003Ca href=\"#source-1\" class=\"citation-link\" title=\"View source [1]\">[1]\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Child welfare must not become another opaque AI black box.\u003C\u002Fp>\n\u003Cblockquote>\n\u003Cp>💡 Key takeaway: Safe use of AI in child protection is possible only when models are tightly scoped, technically constrained, and always subordinate to accountable human professionals.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Chr>\n\u003Cp>Treating ChatGPT as an informal social worker erases the boundary between drafting text and determining a child’s future, importing unresolved AI risks into an already overstretched system.\u003C\u002Fp>\n\u003Cp>If you oversee child or family services, urgently audit how staff use AI, freeze unsupported high‑stakes use, and convene practitioners, technologists, and ethicists to design a disciplined, rights‑respecting AI policy—before the technology quietly rewrites how your agency makes life‑changing decisions.\u003C\u002Fp>\n","Child welfare agencies face crushing caseloads and budget pressure. Generative AI looks tempting: draft notes, flag risk, suggest placements.  \n\nBut tools like ChatGPT are probabilistic text engines,...","hallucinations",[],983,5,"2026-02-14T10:02:18.231Z",[17,22,26,30,34,38,42,46,50,54],{"title":18,"url":19,"summary":20,"type":21},"Building Ethical Guardrails for Deploying LLM Agents","https:\u002F\u002Fmedium.com\u002F@saiaditya.g\u002Fethical-considerations-in-deploying-autonomous-llm-agents-a6d10b281847","Building Ethical Guardrails for Deploying LLM Agents\n\nIn an era of ever-growing automation, it’s not surprising that Large Language Model (LLM) agents have captivated industries worldwide. From custom...","kb",{"title":23,"url":24,"summary":25,"type":21},"AI Guardrails in Practice: Preventing Bias, Hallucinations, and Data Leaks","https:\u002F\u002Fwww.geeksforgeeks.org\u002Fartificial-intelligence\u002Fai-for-geeks-week3\u002F","AI Guardrails in Practice: Preventing Bias, Hallucinations, and Data Leaks\n\nLast Updated : 23 Dec, 2025\n\nAfter a decade in data science, I’m still amazed, and occasionally alarmed, by how fast AI evol...",{"title":27,"url":28,"summary":29,"type":21},"LLM business alignment: Detecting AI hallucinations and misaligned agentic behavior in business systems","https:\u002F\u002Fwww.giskard.ai\u002Fknowledge\u002Fllm-business-alignment-detecting-ai-hallucinations-and-misaligned-agentic-behavior-in-business-systems","LLM business alignment: Detecting AI hallucinations and misaligned agentic behavior in business systems\n================================================================================================...",{"title":31,"url":32,"summary":33,"type":21},"How to Build Guardrails for AI Applications | Galileo","https:\u002F\u002Fgalileo.ai\u002Fblog\u002Fai-guardrails-framework","Recently, security researchers exposed a critical vulnerability in Lenovo's AI-powered customer support chatbot. The chatbot, despite being built on OpenAI's GPT-4, lacked fundamental AI guardrails ag...",{"title":35,"url":36,"summary":37,"type":21},"How to Prevent Data Leakage into LLMs in Corporates","https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fnaman-goyal1_how-to-make-sure-your-data-never-leaks-activity-7391113085589229568-9ASR","🔒 How to Make Sure Your Data Never Leaks into LLMs — Even Inside Corporates Generative AI is transforming how enterprises operate — but beneath the excitement lies a hard truth: data leakage into lar...",{"title":39,"url":40,"summary":41,"type":21},"Artificial Power: AI Now 2025 Landscape","https:\u002F\u002Fainowinstitute.org\u002Fwp-content\u002Fuploads\u002F2025\u002F06\u002FFINAL-20250602_AINowLandscapeReport_Full.pdf","Artificial Power: AI Now 2025 Landscape\nJune 3, 2025\n\nAuthored by Kate Brennan, Amba Kak, and Dr. Sarah Myers West. With research support from Mohammed Ali, Yasmine Chokrane, Madeline Kim, Tekendra Pa...",{"title":43,"url":44,"summary":45,"type":21},"Strengthening ChatGPT Against Prompt Injection Attacks","https:\u002F\u002Fwww.gend.co\u002Fblog\u002Fchatgpt-prompt-injection-defence","Strengthening ChatGPT Against Prompt Injection Attacks\n======================================================\n\nOpenAI\n\nChatGPT\n\nDec 10, 2025\n\n**Not sure what to do next with AI?**\n\nAssess readiness, r...",{"title":47,"url":48,"summary":49,"type":21},"Loopex Digital: Survey Finds 87% of Companies Using AI in Core Operations","https:\u002F\u002Finterface.media\u002Fblog\u002Ftopic\u002Fdata-ai\u002F","A 2026 survey of nearly 1,000 C-suite executives found that 87% of companies now use AI in their core operations. However, AI errors and rework continue to cost businesses over $67bn a year. Loopex Di...",{"title":51,"url":52,"summary":53,"type":21},"Red-Teaming Large Language Models","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fred-teaming","Warning: This article is about red-teaming and as such contains examples of model generation that may be offensive or upsetting.\n\nLarge language models (LLMs) trained on an enormous amount of text dat...",{"title":55,"url":56,"summary":57,"type":21},"ChatGPT Security Risks and How to Mitigate Them","https:\u002F\u002Fwww.nightfall.ai\u002Fblog\u002Fchatgpt-security-risks-and-how-to-mitigate-them-a-complete-guide","The Nightfall Team\n\nMarch 8, 2025\n\nChatGPT Security Risks and How to Mitigate Them\n\nChatGPT and similar large language models (LLMs) have transformed how organizations operate, offering unprecedented ...",null,{"generationDuration":60,"kbQueriesCount":61,"confidenceScore":62,"sourcesCount":63},108890,12,100,10,{"metaTitle":6,"metaDescription":10},"en","https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1626117036246-49f0d6949186?ixid=M3w4OTczNDl8MHwxfHNlYXJjaHwxfHxzb2NpYWwlMjB3b3JrZXJzJTIwZ29uZSUyMHdyb25nfGVufDF8MHx8fDE3NzQwMTU1MjR8MA&ixlib=rb-4.1.0&w=1200&h=630&fit=crop&crop=entropy&auto=format,compress",{"photographerName":68,"photographerUrl":69,"unsplashUrl":70},"Patrick Perkins","https:\u002F\u002Funsplash.com\u002F@patrickperkins?utm_source=coreprose&utm_medium=referral","https:\u002F\u002Funsplash.com\u002Fphotos\u002Fa-black-shirt-with-a-yellow-and-white-logo-on-it-NhAEQEkD-FI?utm_source=coreprose&utm_medium=referral",false,{"key":73,"name":74,"nameEn":74},"ai-engineering","AI Engineering & LLM Ops",[76,84,91,98],{"id":77,"title":78,"slug":79,"excerpt":80,"category":81,"featuredImage":82,"publishedAt":83},"6a13dbc6a33b9706f9fe038c","DeepSeek V4‑Pro’s 75% Price Cut: How Ultra‑Cheap Frontier Models Rewrite AI Economics, Risk, and Architecture","deepseek-v4-pro-s-75-price-cut-how-ultra-cheap-frontier-models-rewrite-ai-economics-risk-and-archite","A trillion‑scale Mixture‑of‑Experts (MoE) model with open weights and bargain‑bin pricing is not just another catalog entry—it is a structural shock to stack design, traffic routing, and governance. 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Engineering Lessons from a Hypothetical 16M‑Conversation Leak","anthropic-claude-breach-engineering-lessons-from-a-hypothetical-16m-conversation-leak","1. Framing the alleged Anthropic Claude fraud incident\n\nAssume a worst‑case scenario: 16 million Claude conversations, run by Anthropic, are exfiltrated by a Chinese threat group from a vendor environ...","https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1564551713171-b1a90c34daa5?ixid=M3w4OTczNDl8MHwxfHNlYXJjaHw0Nnx8Y3liZXJzZWN1cml0eSUyMHRlY2hub2xvZ3l8ZW58MXwwfHx8MTc3OTY4MDU3MXww&ixlib=rb-4.1.0&w=1200&h=630&fit=crop&crop=entropy&auto=format,compress&q=60","2026-05-24T22:48:23.005Z",["Island",106],{"key":107,"params":108,"result":110},"ArticleBody_Ruxzja0mRUtOsEzPU9wV7Lf6rzquGfNyfIsbiyt6w",{"props":109},"{\"articleId\":\"699047b1f49ebddd2143dd8b\",\"linkColor\":\"red\"}",{"head":111},{}]