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Woongsik Dr. Su, MBA 공유함🚀 LLMs: Expanding Capability, Expanding Risk Large Language Models (LLMs) continue to evolve at an extraordinary pace—from natural language understanding to advanced reasoning and increasingly autonomous decision-making 🤖📈 But with greater capability comes greater exposure. ⚠️ The Overlooked Risk: Deployment-Stage Privacy Much of today’s research has focused on privacy risks during training But a critical reality is emerging 👇 👉 New privacy threats are arising at the deployment stage As LLMs become deeply embedded in applications—and as autonomy increases—the attack surface expands 🔓 💡 What’s changing? LLMs are no longer passive tools. They are: Integrated across workflows 🔄 Connected to sensitive data sources 📊 Acting with increasing autonomy ⚙️ 👉 This fundamentally shifts the risk landscape 🚨 Key Emerging Threats The paper highlights three major concerns: 🔍 Inadvertent Data Leakage Sensitive information exposed unintentionally through prompts, outputs, or system interactions 🕵️ Malicious Data Exfiltration Attackers exploiting LLM behavior to extract confidential data from connected systems 🌐 Large-Scale Privacy Attacks Sophisticated, automated exploitation enabled by AI at scale 🧠 Why this matters now The risk is no longer theoretical. As LLMs: Interface with enterprise systems 🏢 Access proprietary and personal data 🔐 Operate with limited human oversight 👥 👉 Privacy vulnerabilities scale with adoption 🔄 From Model Risk → System Risk We are shifting from: ➡️ Risks in model training ➡️ To risks in real-world deployment environments This requires a new mindset: 👉 Security must be embedded across the entire AI lifecycle 🛡️ What organizations should prioritize Strong access controls & data boundaries 🔐 Monitoring of prompts, outputs, and agent actions 👀 Red-teaming and adversarial testing 🧪 Governance frameworks for autonomous behavior 📜 👉 Privacy is no longer just compliance—it’s core infrastructure 📌 Bottom Line LLMs are not just powerful—they are exposure multipliers Organizations that fail to address deployment-stage risks risk scaling vulnerabilities alongside innovation Follow and Connect: Woongsik Dr. Su, MBA #AI #LLM #Privacy #CyberSecurity #DataProtection #ResponsibleAI #AIGovernance #GenAI #EnterpriseAI #RiskManagement
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Woongsik Dr. Su, MBA 공유함🚀 The Hidden Cost of AI Assistance As AI becomes embedded in everyday workflows, a critical question emerges: 👉 Are we gaining efficiency at the expense of human capability? 🤖🧠 Recent randomized controlled trials on human–AI interaction (N = 1,222) reveal a more nuanced reality. 📊 Key Findings Across tasks like mathematical reasoning ➗ and reading comprehension 📖: ✅ AI assistance improves short-term performance ❗ But leads to reduced persistence ❗ And weaker performance without AI Even more striking 👇 ⏱️ These effects appear after just ~10 minutes of interaction 🧠 What’s really happening? AI changes behavior—not just outcomes. When people rely on AI: They expect instant answers ⚡ They are less likely to struggle through problems 💭 They become more prone to give up early ❌ 👉 The result: diminished independent problem-solving ability ⚠️ Why this matters Persistence is not a “soft skill”— it is a core driver of learning and mastery Without it: Skill acquisition slows down 📉 Deep understanding weakens 🧩 Long-term capability erodes 🔄 The Trade-off We are shifting from: ➡️ Effort-driven learning ➡️ To answer-driven interaction And that shift has consequences. 💡 What needs to change? AI systems shouldn’t just optimize for task completion They should also support human development 👉 The future of AI design must include: Learning scaffolds 🏗️ Guided problem-solving 🧭 Delayed assistance models ⏳ Mechanisms that encourage persistence, not replace it 📌 Bottom Line AI is a powerful amplifier— but without thoughtful design, it may also become a crutch The goal isn’t just smarter systems. 👉 It’s smarter humans working with them Follow and Connect: Woongsik Dr. Su, MBA #AI #HumanAI #FutureOfWork #Learning #CognitiveSkills #ArtificialIntelligence #EdTech #ResponsibleAI #Innovation #DigitalTransformation
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Woongsik Dr. Su, MBA 공유함🚀 GenAI Concepts: A Practical Foundation for Responsible AI Deployment As organizations accelerate Generative AI (GenAI) adoption, one challenge stands out: 👉 Do we truly understand the fundamentals behind the systems we’re deploying? 🤖📊 This publication addresses that gap. 📘 Developed through a collaboration between ARC Centre of Excellence for Automated Decision-Making and Society and Office of the Victorian Information Commissioner, this guide introduces 42 essential concepts that underpin modern AI software systems. 💡 What makes this resource valuable? 🔍 Comprehensive yet accessible Each concept is explained using: Clear descriptions 🧠 Practical examples 📌 Real-world use cases 🌍 👉 Designed for both technical and non-technical audiences 📊 Built on rigorous foundations The framework is not theoretical alone—it is grounded in: Academic research 📚 Industry reports 🏢 Policy and regulatory guidance 📜 Expert consultation 👥 👉 Bridging the gap between theory, practice, and governance ⚙️ Why it matters for GenAI deployment Understanding these 42 concepts enables organizations to: ✅ Make more informed technology decisions ✅ Strengthen AI governance and risk management ✅ Align with regulatory and ethical expectations ✅ Improve cross-functional collaboration ✅ Scale AI initiatives with confidence 📌 Bottom Line GenAI success is not just about models and tools— it’s about shared understanding across the organization This publication provides exactly that: 👉 A common language for AI that enables responsible, scalable adoption Follow and Connect: Woongsik Dr. Su, MBA #GenAI #ArtificialIntelligence #ResponsibleAI #AIGovernance #DigitalTransformation #AIConcepts #EnterpriseAI #Innovation #TechStrategy #DataDriven
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Woongsik Dr. Su, MBA 공유함🚀 Human Rights and Artificial Intelligence Over the past decade, Council of Europe has taken a leading role in shaping the intersection of AI and human rights 🌍⚖️ From early exploration to recent acceleration, multiple committees and bodies have introduced policies, recommendations, and legal frameworks to guide responsible AI development 📜🤖 As AI capabilities rapidly evolve, one thing is clear: 👉 Governance is no longer optional—it’s foundational 💡 3 Critical Pillars for AI Supervision & Compliance If you’re working with AI in regulated industries, these are not just concepts—they are non-negotiables: 🔍 1. AI Transparency The degree of openness and clarity across the entire AI lifecycle How systems are designed, trained, and deployed 🏗️ Visibility into data sources and decision flows 📊 Clear governance structures and accountability 🧭 👉 Without transparency, trust cannot scale 🧠 2. Explainable AI (XAI) The ability to clearly explain why AI produces certain outcomes Why a prediction or recommendation was made 🤔 What factors influenced the decision 📌 Understanding system capabilities and limitations ⚠️ 👉 If you can’t explain it, you can’t justify it ⚙️ 3. AI Interpretability The ability to understand how AI systems actually work internally Insight into models, logic, and decision pathways 🔍 Making “black box” systems more understandable 📦 Ensuring both experts and affected users can interpret outcomes 👥 👉 If you can’t interpret it, you can’t control it 📌 Why This Matters These three pillars form the backbone of AI Supervision Compliance Organizations that embed them early will: ✅ Strengthen regulatory alignment ✅ Build stakeholder trust ✅ Reduce operational and ethical risk ✅ Enable scalable, responsible AI adoption 🔥 Bottom Line AI is not just a technology shift—it’s a governance challenge The future will be defined by organizations that can balance: ⚡ Innovation ⚖️ Accountability 🔍 Transparency Follow and Connect: Woongsik Dr. Su, MBA #AI #HumanRights #ResponsibleAI #AITransparency #ExplainableAI #AIInterpretability #AIGovernance #EthicalAI #Compliance #TrustInAI
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Woongsik Dr. Su, MBA 공유함🚀 The Agentic Banking Blueprint Over the past two decades, banking transformation has been driven by digitization. Financial institutions invested heavily in systems to capture data, streamline processes, and elevate digital customer experiences 💻📊 More recently, AI has accelerated this journey—helping teams analyze information, generate insights, and make smarter decisions 🤖✨ But here’s the reality 👇 ⚠️ Execution hasn’t evolved at the same pace Work still depends on manual coordination across teams, systems, and channels. Even when insights exist, follow-through is inconsistent. The biggest bottleneck? Turning decisions into action. 💡 Enter AI Agents Unlike traditional AI that recommends, AI agents act. They execute tasks, follow rules, and move workflows forward across systems 🔄⚙️ And now, we are seeing the rise of: 🔥 Autonomous AI Agents These are not just tools—they are digital operators: Learn from data 📚 Make decisions within defined objectives 🎯 Adapt to real-world conditions 🌍 Continuously improve performance 📈 They don’t just complete tasks— 👉 They own outcomes within governed boundaries 🔄 From Decision Support → Decision Execution This “agentic” shift is more than technology. It fundamentally reshapes how work gets done. Banks are evolving from: ➡️ Systems that support decisions ➡️ To systems that execute them 🏦 Strategic Advantage for Early Movers Institutions that embed AI agents as governed digital labor will unlock: ✅ Revenue growth & protection 💰 ✅ Cost-to-serve optimization 📉 ✅ Operational resilience 🛡️ ✅ Faster decision-making ⚡ ✅ Institutional knowledge retention 🧠 📌 Bottom Line This is not incremental innovation— it’s a structural transformation of banking operations. The future belongs to organizations that can deploy autonomous agents 👉 responsibly, measurably, and at scale Follow and Connect: Woongsik Dr. Su, MBA #AI #AgenticAI #AutonomousAgents #BankingTransformation #DigitalTransformation #FutureOfWork #IntelligentAutomation #FinTech #Innovation #EnterpriseAI
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Woongsik Dr. Su, MBA 공유함Project Management for the Built Environment!Woongsik Dr. Su, MBA 공유함🏗️ Book Spotlight: Project Management for the Built Environment – Study Notes By Low Sui Pheng | Springer | Management in the Built Environment Series This book is a structured and practical guide for students, researchers, and professionals in construction and engineering management. It distills the essentials of project management into clear study notes tailored for the built environment, making it both an academic resource and a professional reference. 🔑 Key Highlights from the Book Foundations of Project Management: Introduction to life cycles, stakeholders, and organizational structures. Core Processes: Integration, scope, time, cost, and quality management explained with clarity. Human Factors: Insights into managing teams, communication, and stakeholder engagement. Risk & Procurement: Practical frameworks for anticipating risks and managing procurement processes. Ethics & Professional Conduct: Guidance on ethical practice, industry standards, and regulations. Applied Learning: Discussion questions and case-based reflections to bridge theory with practice. ✨ Why it matters In today’s complex construction and infrastructure projects, effective project management is the difference between success and failure. This book equips professionals with the tools to manage scope, time, cost, and quality while upholding ethics and industry standards. 💡 Engagement Tip for LinkedIn Post End with a thought-provoking question to spark discussion: 👉 “What project management principle do you find most critical in delivering successful built environment projects?” Hashtags #ProjectManagement #BuiltEnvironment #ConstructionManagement #InfrastructureDevelopment #RiskManagement #EthicsInConstruction #EngineeringLeadership #ManagementInPractice #ProfessionalDevelopment #SpringerBooks
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Woongsik Dr. Su, MBA 공유함This paper draws on 18 months of research across 200+ VC and growth equity funds and 80 LPs managing $6T+ in AUM—combining 30+ in-depth interviews with a global survey of practitioners. 📊🌍 The message is clear: Responsible AI is no longer a side conversation. It’s becoming core to investment strategy. Here’s what stands out 👇 1️⃣ Responsible AI = Good business 💼🤖 73% of VCs believe companies with strong responsible AI practices will outperform financially Driven by two realities: ✔ Avoiding costly failures ✔ Earning customer trust for real-world adoption And it goes further: 84% see direct investment opportunities in responsible AI-native companies 91% see AI safety infrastructure as the next cybersecurity 👉 This is shifting from ethics → alpha generation 2️⃣ Conviction is high. So is concern. ⚠️ 84% worry about AI’s impact on human agency 96% see malicious use as a major societal threat 74% are concerned about environmental impact (data centers, energy) 3️⃣ The execution gap is real 📉 Belief is strong—but practice lags: Only 55% include responsible AI in due diligence Just 14% rate their AI risk assessment as “good” Only 27% say they have enough internal expertise And it’s not just investors: 👉 Only 42% believe their portfolio companies truly understand AI risks ✔ Expertise is scarce ✔ Teams are fragmented (ESG / legal / tech / investment) 4️⃣ Regulation = complexity, not clarity ⚖️ GDPR → widely respected baseline EU AI Act → mixed reactions Regional dynamics diverge: 🇪🇺 Europe → compliance as reality 🇺🇸 US → compliance as competitive constraint 🌏 Asia → increasingly progressive 👉 Regulation is shaping strategy—but not simplifying it 5️⃣ LP pressure is accelerating change 🔄 Limited partners are stepping in: US LPs rapidly catching up to European expectations Growing pressure across the investment chain Enterprise customers raising procurement standards 👉 Incentives are shifting top-down and bottom-up 6️⃣ From risk management → investment thesis 🚀 Responsible AI is evolving into a full investment lens: Infrastructure for AI safety & governance High-impact sectors: ✔ Healthcare 🏥 ✔ Education 🎓 ✔ Climate 🌱 ✔ Financial inclusion 💳 👉 Trustworthy AI is not just safer—it’s more scalable and more valuable 💡 Bottom line Responsible AI is no longer just about avoiding downside. It’s becoming: 👉 A source of competitive advantage 👉 A driver of capital allocation 👉 A defining factor in long-term value creation The gap now is not awareness. It’s capability and execution. Follow and Connect: Woongsik Dr. Su, MBA #AI #ResponsibleAI #VentureCapital #PrivateEquity #ESG #AIGovernance #Innovation #InvestmentStrategy #FutureOfWork #DigitalTransformation
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Woongsik Dr. Su, MBA 공유함UNICEF just released a framework on AI in evaluation—and it quietly challenges how most teams are using it today. 🤖📊 We often assume AI will fix evaluation by making it faster. But that’s not where the real value is. Here’s what actually stands out 👇 1️⃣ AI works best where evaluation struggles most Not in dashboards. Not in reporting. But in: ✔ Synthesizing hundreds of reports 📚 ✔ Mapping activities across countries 🌍 ✔ Extracting insights from unstructured text 🧩 👉 This is where machine learning is already saving time and improving consistency. 2️⃣ Simpler methods often win A surprisingly practical takeaway: You don’t always need complex AI. In many cases: ✔ Keyword search outperformed advanced semantic models 🔍 ✔ Basic NLP delivered faster, reliable results ⚡ 👉 The real insight: fit > sophistication 3️⃣ AI solves analysis. Not understanding. The framework is very clear here. AI can: ✔ Process large datasets ✔ Identify patterns ✔ Generate summaries But it cannot answer: ❌ What does this mean for decisions? ❌ What should change? 👉 That gap is still human. 🧠 4️⃣ The real risk is over-trusting AI Because: ⚠ High accuracy ≠ correct interpretation ⚠ Faster outputs ≠ better insight ⚠ Scalable systems ≠ meaningful evaluation 💡 What this means for M&E professionals The shift is not about using more AI. It’s about using it in the right place: → Automate repetitive analysis 🔄 → Focus human effort on interpretation & judgment 🎯 👉 That’s where real value is created. Bottom line: UNICEF’s framework is a strong reminder— AI is not replacing evaluation. It’s reshaping what good evaluation looks like. 🧠 + 🤖 = Better decisions, not just faster ones. Follow and Connect: Woongsik Dr. Su, MBA #AI #MonitoringAndEvaluation #UNICEF #DataForGood #AIEthics #DigitalTransformation #DecisionMaking #PublicSector #Analytics #ResponsibleAI
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Woongsik Dr. Su, MBA 공유함“Philosophy eats AI.” 🧠⚙️ Plenty of people are sharing MIT’s piece—and rightly so. It’s refreshing to see philosophy step more explicitly into the AI conversation, because it’s been missing where it matters most. A core point stands out: prompts, parameters, and deployments are never neutral. They encode assumptions about knowledge, truth, purpose, and value. 🎯 In philosophical terms, AI systems are shaped by: Teleology → what the system is for 🧭 Epistemology → what counts as knowledge 📚 Ontology → how reality is represented 🌐 Which means: AI doesn’t arrive as pure capability. It arrives carrying a worldview. And that worldview quietly defines: ✔ what gets optimized ✔ what gets ignored ✔ what gets traded away This is why AI is not just a technical story—it’s a story about ends, not just means. ⚖️ Once embedded into organizations, these assumptions stop being abstract. They begin shaping how companies: think 💡 decide 📊 prioritize 📌 act 🚀 And ultimately, they shape real-world consequences. So the real takeaway is this: The people building, training, and tuning AI systems are not just defining what AI can do. 👉 They are shaping what organizations come to see as: salient true worth acting on That’s not just engineering. That’s philosophy in production. 🏗️🧠 Follow and Connect: Woongsik Dr. Su, MBA #AI #Philosophy #EthicsInAI #DigitalTransformation #Leadership #DecisionMaking #AIGovernance #Strategy #ResponsibleAI #FutureOfWork
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Woongsik Dr. Su, MBA 좋아요 표시함🚀 LLMs: Expanding Capability, Expanding Risk Large Language Models (LLMs) continue to evolve at an extraordinary pace—from natural language understanding to advanced reasoning and increasingly autonomous decision-making 🤖📈 But with greater capability comes greater exposure. ⚠️ The Overlooked Risk: Deployment-Stage Privacy Much of today’s research has focused on privacy risks during training But a critical reality is emerging 👇 👉 New privacy threats are arising at the deployment stage As LLMs become deeply embedded in applications—and as autonomy increases—the attack surface expands 🔓 💡 What’s changing? LLMs are no longer passive tools. They are: Integrated across workflows 🔄 Connected to sensitive data sources 📊 Acting with increasing autonomy ⚙️ 👉 This fundamentally shifts the risk landscape 🚨 Key Emerging Threats The paper highlights three major concerns: 🔍 Inadvertent Data Leakage Sensitive information exposed unintentionally through prompts, outputs, or system interactions 🕵️ Malicious Data Exfiltration Attackers exploiting LLM behavior to extract confidential data from connected systems 🌐 Large-Scale Privacy Attacks Sophisticated, automated exploitation enabled by AI at scale 🧠 Why this matters now The risk is no longer theoretical. As LLMs: Interface with enterprise systems 🏢 Access proprietary and personal data 🔐 Operate with limited human oversight 👥 👉 Privacy vulnerabilities scale with adoption 🔄 From Model Risk → System Risk We are shifting from: ➡️ Risks in model training ➡️ To risks in real-world deployment environments This requires a new mindset: 👉 Security must be embedded across the entire AI lifecycle 🛡️ What organizations should prioritize Strong access controls & data boundaries 🔐 Monitoring of prompts, outputs, and agent actions 👀 Red-teaming and adversarial testing 🧪 Governance frameworks for autonomous behavior 📜 👉 Privacy is no longer just compliance—it’s core infrastructure 📌 Bottom Line LLMs are not just powerful—they are exposure multipliers Organizations that fail to address deployment-stage risks risk scaling vulnerabilities alongside innovation Follow and Connect: Woongsik Dr. Su, MBA #AI #LLM #Privacy #CyberSecurity #DataProtection #ResponsibleAI #AIGovernance #GenAI #EnterpriseAI #RiskManagement
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Woongsik Dr. Su, MBA 좋아요 표시함🚀 The Hidden Cost of AI Assistance As AI becomes embedded in everyday workflows, a critical question emerges: 👉 Are we gaining efficiency at the expense of human capability? 🤖🧠 Recent randomized controlled trials on human–AI interaction (N = 1,222) reveal a more nuanced reality. 📊 Key Findings Across tasks like mathematical reasoning ➗ and reading comprehension 📖: ✅ AI assistance improves short-term performance ❗ But leads to reduced persistence ❗ And weaker performance without AI Even more striking 👇 ⏱️ These effects appear after just ~10 minutes of interaction 🧠 What’s really happening? AI changes behavior—not just outcomes. When people rely on AI: They expect instant answers ⚡ They are less likely to struggle through problems 💭 They become more prone to give up early ❌ 👉 The result: diminished independent problem-solving ability ⚠️ Why this matters Persistence is not a “soft skill”— it is a core driver of learning and mastery Without it: Skill acquisition slows down 📉 Deep understanding weakens 🧩 Long-term capability erodes 🔄 The Trade-off We are shifting from: ➡️ Effort-driven learning ➡️ To answer-driven interaction And that shift has consequences. 💡 What needs to change? AI systems shouldn’t just optimize for task completion They should also support human development 👉 The future of AI design must include: Learning scaffolds 🏗️ Guided problem-solving 🧭 Delayed assistance models ⏳ Mechanisms that encourage persistence, not replace it 📌 Bottom Line AI is a powerful amplifier— but without thoughtful design, it may also become a crutch The goal isn’t just smarter systems. 👉 It’s smarter humans working with them Follow and Connect: Woongsik Dr. Su, MBA #AI #HumanAI #FutureOfWork #Learning #CognitiveSkills #ArtificialIntelligence #EdTech #ResponsibleAI #Innovation #DigitalTransformation
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Agentic AI: The Next Evolution of Intelligent Systems
Open Access
논문·저서 보기🚀 We are no longer programming machines — we are awakening them.
For the first time in history, intelligence has become autonomous.
Agentic AI doesn’t just process data — it thinks, plans, and acts with purpose.
📘 The Age of Agency explores this transformation — how AI evolves from a reactive tool into a self-directed collaborator.
💡 Inside the book:
🔹 The shift from automation to autonomy
🔹 The anatomy of agentic systems — memory, reasoning, and…🚀 We are no longer programming machines — we are awakening them.
For the first time in history, intelligence has become autonomous.
Agentic AI doesn’t just process data — it thinks, plans, and acts with purpose.
📘 The Age of Agency explores this transformation — how AI evolves from a reactive tool into a self-directed collaborator.
💡 Inside the book:
🔹 The shift from automation to autonomy
🔹 The anatomy of agentic systems — memory, reasoning, and planning
🔹 Real-world applications across science, business, and governance
🔹 Ethics, control, and the design of intelligent organizations
🌍 This isn’t just about smarter software — it’s about a new cognitive revolution.
How we design, deploy, and govern these systems will define the next era of human progress.
✨ The age of reactive machines is over.
🔥 Welcome to the Age of Agency.
📖 Free Access:
This book is available for open reading — no paywalls, no restrictions.
Feel free to share or repost this post so others can explore the ideas shaping our autonomous future. -
Development of a high throughput method for the discovery of selective propene oxidation catalysts
Saarland University
논문·저서 보기The main focus of this thesis lies in the development of a high-throughput method for the
discovery of catalysts for selective oxidation of propene. For this, a new 10-fold parallel
reactor was constructed, validated, and tested for selective oxidation of propene to
acrolein or propene oxide. Catalysts for these conversions were prepared by combinatorial
chemistry approaches and subsequently screened in high-throughput techniques.
Following a sol-gel procedure approximately…The main focus of this thesis lies in the development of a high-throughput method for the
discovery of catalysts for selective oxidation of propene. For this, a new 10-fold parallel
reactor was constructed, validated, and tested for selective oxidation of propene to
acrolein or propene oxide. Catalysts for these conversions were prepared by combinatorial
chemistry approaches and subsequently screened in high-throughput techniques.
Following a sol-gel procedure approximately 2200 catalysts have been prepared, and they
were tested with the help of a stage robot reactor as primary screening and a 10-fold
parallel reactor as secondary screening. Using the stage robot reactor, several catalysts
consisting of Cu-containing mixed oxides have been discovered that were active for
propene-oxide production, but they could not be confirmed by conventional tests. In the
case of acrolein-production, a Pd10Ga45Cu45 catalyst has been found with the best activity.
Among Mo-based catalysts, Mo94Ru4Te2 and Mo88Sn8Te4 showed very high propene
conversion to acrolein with yield of 25% at 375 °C and yield of 28% at 400 °C,
respectively.
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Deutscher Akademischer Austausch Dienst - DAAD
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