https://monago.io/assets/glossary-JInIKidS.js

1const i={"ai-fundamentals":{id:"Fundamental AI",en:"AI Fundamentals"},"ai-governance":{id:"Tata Kelola AI",en:"AI Governance"},"automation-governance":{id:"Tata Kelola Otomasi",en:"Automation Governance"},compliance:{id:"Kepatuhan & Regulasi",en:"Compliance & Regulation"},"llm-ai":{id:"LLM & Generative AI",en:"LLM & Generative AI"},"ai-applied":{id:"AI Terapan",en:"AI Applied"},"data-foundation":{id:"Fondasi Data",en:"Data Foundation"},"data-protection":{id:"Perlindungan Data",en:"Data Protection"},mlops:{id:"MLOps & Operasi",en:"MLOps & Operations"},"audit-logging":{id:"Audit & Logging",en:"Audit & Logging"},deployment:{id:"Deployment & Arsitektur",en:"Deployment & Architecture"},security:{id:"Keamanan & Adversarial",en:"Security & Adversarial"}},t=["ai-fundamentals","ai-governance","automation-governance","compliance","llm-ai","ai-applied","data-foundation","data-protection","mlops","audit-logging","deployment","security"],n=[{slug:"artificial-intelligence",term:"Artificial Intelligence",abbrev:"AI",category:"ai-fundamentals",summary:{id:"Bidang ilmu komputer yang membuat sistem mampu meniru kemampuan kognitif manusia — belajar, memutuskan, memahami bahasa.",en:"A field of computer science that builds systems capable of mimicking human cognitive abilities — learning, deciding, understanding language."},context:{id:"AI bukan lagi teknologi masa depan — sudah ada di hampir setiap aplikasi enterprise. Tantangan bagi pimpinan adalah memilih use case yang menghasilkan ROI nyata, bukan sekadar mengikuti hype.",en:"AI is no longer future tech — it sits inside nearly every enterprise application. The leadership challenge is picking use cases that produce real ROI, not just chasing hype."},related:["machine-learning","deep-learning","generative-ai"]},{slug:"machine-learning",term:"Machine Learning",abbrev:"ML",category:"ai-fundamentals",summary:{id:"Cabang AI di mana sistem belajar dari data — bukan dari aturan eksplisit yang ditulis programmer.",en:"A branch of AI where systems learn from data — rather than from explicit rules written by programmers."},context:{id:"ML mengubah cara perusahaan menganalisis pola, memprediksi tren, dan mengotomasi keputusan. Tanpa tata kelola, model bisa mendiskriminasi atau menjadi obsolete diam-diam.",en:"ML changes how companies analyze patterns, predict trends, and automate decisions. Without governance, models can discriminate or quietly grow obsolete."},related:["deep-learning","supervised-learning","unsupervised-learning"]},{slug:"deep-learning",term:"Deep Learning",category:"ai-fundamentals",summary:{id:"Cabang ML yang menggunakan neural network berlapis-lapis untuk memproses data kompleks seperti teks, gambar, dan suara.",en:"A branch of ML using multi-layered neural networks to process complex data such as text, images, and audio."},context:{id:"Deep learning adalah teknik di balik LLM, computer vision, dan speech recognition modern. Biaya komputasinya tinggi — pengendalian biaya menjadi keputusan strategis.",en:"Deep learning powers modern LLMs, computer vision, and speech recognition. Compute cost is high — cost control becomes a strategic decision."},related:["neural-network","machine-learning","transformer"]},{slug:"neural-network",term:"Neural Network",category:"ai-fundamentals",summary:{id:"Arsitektur model AI yang terinspirasi cara kerja neuron otak — terdiri dari node-node yang terhubung dalam lapisan.",en:"An AI model architecture inspired by how brain neurons work — nodes connected across layers."},context:{id:"Neural network adalah fondasi semua AI generatif modern. Memahami konsep dasarnya membantu eksekutif berkomunikasi dengan tim data science tanpa salah arti.",en:"Neural networks are the foundation of all modern generative AI. Grasping the basics helps executives talk with data science teams without miscommunication."},related:["deep-learning","transformer","algorithm"]},{slug:"algorithm",term:"Algorithm",category:"ai-fundamentals",summary:{id:"Prosedur langkah demi langkah yang menyelesaikan masalah tertentu — dasar dari semua sistem komputer, termasuk AI.",en:"A step-by-step procedure that solves a specific problem — the basis of all computer systems, including AI."},context:{id:"Algorithm AI yang menghasilkan keputusan harus dapat diaudit. Algorithmic accountability menjadi prinsip yang ditetapkan UU PDP dan NIST AI RMF.",en:"Decision-making AI algorithms must be auditable. Algorithmic accountability is a principle codified by Indonesia's PDP Law and the NIST AI RMF."},related:["machine-learning","ai-auditability","ai-explainability"]},{slug:"supervised-learning",term:"Supervised Learning",category:"ai-fundamentals",summary:{id:"Metode pelatihan ML di mana model belajar dari data yang sudah diberi label benar/salah oleh manusia.",en:"An ML training method where the model learns from data already labeled right/wrong by humans."},context:{id:"Sekitar 80% aplikasi ML enterprise saat ini bersifat supervised — klasifikasi dokumen, prediksi churn, deteksi fraud. Kualitas labeling menentukan kualitas model.",en:"Roughly 80% of current enterprise ML use cases are supervised — document classification, churn prediction, fraud detection. Labeling quality determines model quality."},related:["unsupervised-learning","training-data","classification"]},{slug:"unsupervised-learning",term:"Unsupervised Learning",category:"ai-fundamentals",summary:{id:"Metode pelatihan ML di mana model menemukan pola dalam data tanpa label — sistem yang menemukan kelompok sendiri.",en:"An ML training method where the model finds patterns in unlabeled data — the system discovers groupings on its own."},context:{id:"Cocok untuk segmentasi pelanggan, deteksi anomali, dan menemu

1kan pola tersembunyi. Hasilnya butuh interpretasi manusia sebelum bisa dipakai.",en:"Fits customer segmentation, anomaly detection, and hidden-pattern discovery. Results require human interpretation before they can be acted on."},related:["clustering","anomaly-detection","supervised-learning"]},{slug:"reinforcement-learning",term:"Reinforcement Learning",abbrev:"RL",category:"ai-fundamentals",summary:{id:"Metode pelatihan ML di mana model belajar lewat trial-and-error dengan reward dan penalty.",en:"An ML training method where the model learns by trial-and-error using rewards and penalties."},context:{id:"Powerful untuk optimasi proses (routing, dynamic pricing) tapi memerlukan environment simulasi yang akurat. RLHF di LLM adalah varian penting.",en:"Powerful for process optimization (routing, dynamic pricing) but needs accurate simulation environments. RLHF in LLMs is an important variant."},related:["rlhf","machine-learning","agentic-ai"]},{slug:"classification",term:"Classification",category:"ai-fundamentals",summary:{id:"Tugas ML untuk mengategorikan input ke dalam kelas yang sudah ditentukan — spam/bukan, fraud/sah, kategori dokumen.",en:"An ML task that categorizes input into predefined classes — spam/not spam, fraud/legitimate, document categories."},context:{id:"Use case paling umum di enterprise. Confusion matrix dan threshold tuning adalah konsep dasar yang harus dipahami oleh business owner, bukan hanya data scientist.",en:"The most common enterprise use case. Confusion matrix and threshold tuning are basics that business owners — not just data scientists — should understand."},related:["supervised-learning","regression","predictive-analytics"]},{slug:"regression",term:"Regression",category:"ai-fundamentals",summary:{id:"Tugas ML untuk memprediksi nilai numerik kontinu — harga, demand, durasi.",en:"An ML task that predicts continuous numerical values — prices, demand, duration."},context:{id:"Dasar untuk demand forecasting, pricing optimization, dan capacity planning. Output regression butuh interval kepercayaan, bukan sekadar satu angka.",en:"The basis for demand forecasting, pricing optimization, and capacity planning. Regression outputs need confidence intervals, not just point estimates."},related:["classification","predictive-analytics","machine-learning"]},{slug:"clustering",term:"Clustering",category:"ai-fundamentals",summary:{id:"Tugas ML untuk mengelompokkan data tanpa label yang sudah ada — mencari segmen alami dalam data.",en:"An ML task that groups unlabeled data — discovering natural segments inside the data."},context:{id:"Customer segmentation, anomaly detection, dan content discovery. Hasil clustering butuh interpretasi bisnis untuk menjadi aksi yang bernilai.",en:"Customer segmentation, anomaly detection, and content discovery. Clustering results need business interpretation to become valuable action."},related:["unsupervised-learning","anomaly-detection","machine-learning"]},{slug:"training-data",term:"Training Data",category:"ai-fundamentals",summary:{id:"Data yang dipakai untuk melatih model AI — mengajarkan pola yang harus dikenali.",en:"Data used to train an AI model — teaching it the patterns it should recognize."},context:{id:'"Garbage in, garbage out" — kualitas dan representativitas training data menentukan kualitas model. Bias di training data menjadi bias di output.',en:'"Garbage in, garbage out" — the quality and representativeness of training data dictate model quality. Bias in training data becomes bias in output.'},related:["validation-data","data-quality","ai-bias"]},{slug:"validation-data",term:"Validation Data",category:"ai-fundamentals",summary:{id:"Data terpisah yang dipakai mengukur seberapa baik model bekerja di luar data pelatihan.",en:"A separate dataset used to measure how well a model performs outside its training data."},context:{id:'Tanpa validation data yang representatif, perusahaan bisa men-deploy model yang "tampak bagus" di lab tapi gagal di produksi.',en:'Without representative validation data, companies risk deploying models that "look great" in the lab but fail in production.'},related:["training-data","overfitting","model-monitoring"]},{slug:"overfitting",term:"Overfitting",category:"ai-fundamentals",summary:{id:"Kondisi di mana model AI terlalu hafal data pelatihan dan gagal generalisasi ke data baru.",en:"A condition where an AI model memorizes its training data and fails to generalize to new data."},context:{id:"Penyebab utama model gagal di produksi padahal akurasinya tinggi di lab. Tata kelola model wajib mensyaratkan evaluasi pada data dunia nyata.",en:"The primary reason models fail in production despite high lab accuracy. Model governance must require evaluation on real-world data."},related:["validation-data","training-data","model-drift"]},{slug:"hyperparameter",term:"Hyperparameter",category:"ai-fundamentals",summary:{id:"Parameter konfigurasi model yang ditentukan sebelum pelatihan — bukan dipelajari dari data.",en:"A model configuration parameter set before training — not learned from data."},context:{id:"Hyperparameter tuning bisa membuat perbedaan 10–20% akurasi. Untuk enterprise, ini biasanya tugas tim ML — tapi penting kenal istilahnya saat berdiskusi.",en:"Hyperparameter tuning can swing accuracy by 10–20%. Usually an ML team task in enterprises — but worth knowing the term when in discussion."},related:["machine-learning","fine-tuning","training-data"]},{slug:"generative-ai",term:"Generative AI",abbrev:"GenAI",category:"ai-fundamentals",summary:{id:"Cabang AI yang menghasilkan konten baru (teks, gambar, kode) — bukan sekadar menganalisis yang sudah ada.",en:"A branch of AI that generates new content (text, images, code) — not just analyzing what already exists."},context:{id:"GenAI memicu lonjakan adopsi AI sejak 2022. Tantangan utama: tata kelola atas konten yang dihasilkan (halusinasi, hak cipta, kepatuhan).",en:"GenAI triggered the AI adoption surge since 2022. The main challenge: governance over generated content (hallucinations, IP, compliance)."},related:["llm","hallucination","ai-governance"]},{slug:"ai-governance",term:"AI Governance",category:"ai-governance",summary:{id:"Rangkaian kebijakan, proses, dan kontrol yang memastikan penggunaan AI di organisasi tetap aman, dapat dipertanggungjawabkan, dan sesuai regulasi.",en:"The set of policies, processes, and controls that ensure AI usage in an organization stays safe, accountable, and compliant."}

1,context:{id:"Tanpa tata kelola, AI bisa membuka risiko hukum (UU PDP), risiko reputasi (output bias), dan risiko finansial (biaya tidak terkontrol). Tata kelola memungkinkan adopsi cepat tanpa kehilangan kendali.",en:"Without governance, AI exposes legal (PDP Law), reputational (biased output), and financial (uncontrolled spend) risks. Governance enables fast adoption without losing control."},related:["responsible-ai","ai-risk-management","model-governance"]},{slug:"responsible-ai",term:"Responsible AI",category:"ai-governance",summary:{id:"Pendekatan pengembangan dan penggunaan AI yang adil, transparan, dan dapat dipertanggungjawabkan secara etis dan hukum.",en:"An approach to AI development and use that is fair, transparent, and ethically and legally accountable."},context:{id:"Bagi perusahaan terkemuka Indonesia, Responsible AI bukan lagi pilihan — regulator (OJK, Kominfo) dan pelanggan menuntut bukti bahwa AI tidak merugikan kelompok tertentu dan keputusannya dapat dijelaskan.",en:"For leading Indonesian enterprises, Responsible AI is no longer optional — regulators (OJK, Kominfo) and customers demand proof that AI does not harm specific groups and that its decisions can be explained."},related:["ai-bias","ai-explainability","ai-ethics"]},{slug:"ai-ethics",term:"AI Ethics",category:"ai-governance",summary:{id:"Cabang etika yang mempelajari moralitas pengembangan dan penggunaan AI — keadilan, transparansi, dan dampak sosial.",en:"The branch of ethics that studies the morality of AI development and use — fairness, transparency, and social impact."},context:{id:"Bukan kemewahan akademik — perusahaan tanpa AI ethics framework terjebak dalam kontroversi PR (bias, diskriminasi, privasi) yang merugikan brand jangka panjang.",en:"Not academic luxury — companies without an AI ethics framework get trapped in PR controversies (bias, discrimination, privacy) that damage the brand long-term."},related:["responsible-ai","ai-bias","ai-transparency"]},{slug:"ai-risk-management",term:"AI Risk Management",category:"ai-governance",summary:{id:"Proses mengidentifikasi, menilai, dan memitigasi risiko yang muncul dari penggunaan AI — termasuk risiko hukum, operasional, keamanan, dan reputasi.",en:"The process of identifying, assessing, and mitigating risks arising from AI use — including legal, operational, security, and reputational risks."},context:{id:"Framework seperti NIST AI RMF memberi pedoman terstruktur. Risk register AI yang baik membantu CIO dan CISO menjawab pertanyaan board dengan data, bukan intuisi.",en:"Frameworks like NIST AI RMF provide structured guidance. A well-maintained AI risk register helps CIOs and CISOs answer board questions with data, not intuition."},related:["nist-ai-rmf","ai-governance","model-governance"]},{slug:"ai-bias",term:"AI Bias",category:"ai-governance",summary:{id:"Kondisi ketika output AI memberi perlakuan tidak adil pada kelompok tertentu karena data pelatihan atau desain model.",en:"A condition where AI output unfairly treats certain groups due to training data or model design."},context:{id:"Di Indonesia, bias terhadap nama, daerah, atau gender bisa berujung pelanggaran UU PDP atau diskriminasi konsumen. Audit bias rutin adalah bagian wajib dari tata kelola AI.",en:"In Indonesia, bias against names, regions, or gender can lead to PDP Law violations or consumer discrimination. Routine bias audits are a mandatory part of AI governance."},related:["responsible-ai","ai-explainability","training-data"]},{slug:"ai-explainability",term:"AI Explainability",abbrev:"XAI",category:"ai-governance",summary:{id:"Kemampuan untuk menjelaskan mengapa AI menghasilkan output tertentu dalam bahasa yang dapat dipahami manusia.",en:"The ability to explain why an AI produced a particular output in language humans can understand."},context:{id:"Untuk keputusan berdampak tinggi (kredit, klaim asuransi, diagnosis), regulator dan auditor menuntut penjelasan, bukan sekadar skor. Tanpa explainability, AI tidak dapat dipakai di produksi enterprise.",en:"For high-impact decisions (credit, insurance claims, diagnosis), regulators and auditors demand explanations, not just scores. Without explainability, AI cannot be used in enterprise production."},related:["ai-transparency","ai-auditability","chain-of-thought"]},{slug:"ai-transparency",term:"AI Transparency",category:"ai-governance",summary:{id:"Keterbukaan tentang bagaimana AI dibangun, dilatih, dan dipakai — termasuk data sumber dan keterbatasannya.",en:"Openness about how an AI is built, trained, and used — including data sources and limitations."},context:{id:"Transparansi adalah prasyarat untuk audit, manajemen risiko, dan kepercayaan pengguna. Vendor AI yang menolak transparan adalah red flag untuk procurement enterprise.",en:"Transparency is a prerequisite for audit, risk management, and user trust. AI vendors that refuse to be transparent are a red flag for enterprise procurement."},related:["ai-explainability","ai-auditability","model-card"]},{slug:"ai-auditability",term:"AI Auditability",category:"ai-governance",summary:{id:"Sifat sistem AI yang dapat diperiksa secara independen — setiap input, output, dan keputusan tercatat dan dapat ditelusuri.",en:"The property of an AI system that can be independently examined — every input, output, and decision is recorded and traceable."},context:{id:"Auditability bukan janji, tapi struktur. Audit trail tamper-proof (lihat hash chain) adalah fondasi yang membuat AI aman dipakai di sektor regulasi tinggi.",en:"Auditability is not a promise, but a structure. Tamper-proof audit trails (see hash chain) are the foundation that makes AI safe to use in highly regulated sectors."},related:["audit-trail","hash-chain","immutable-log"]},{slug:"model-governance",term:"Model Governance",category:"ai-governance",summary:{id:"Kontrol siklus hidup model AI: dari pelatihan, validasi, deployment, monitoring, sampai pensiun.",en:"Lifecycle c

1ontrol of an AI model: training, validation, deployment, monitoring, and retirement."},context:{id:"Tanpa model governance, organisasi tidak tahu model mana yang sedang dipakai di produksi, oleh siapa, dengan performa apa. Risiko shadow model berkembang menjadi risiko compliance.",en:"Without model governance, organizations do not know which models are running in production, by whom, with what performance. Shadow model risk grows into compliance risk."},related:["model-registry","mlops","ai-governance"]},{slug:"human-in-the-loop",term:"Human-in-the-Loop",abbrev:"HITL",category:"ai-governance",summary:{id:"Desain sistem AI di mana manusia tetap menjadi pengambil keputusan akhir — AI hanya memberi rekomendasi.",en:"An AI system design where humans remain the final decision-maker — the AI only provides recommendations."},context:{id:"Untuk keputusan berdampak tinggi (kredit, diagnosis, sanksi), HITL bukan opsional — wajib secara etis dan sering secara regulasi. Mengurangi risiko sambil tetap efisien.",en:"For high-impact decisions (credit, diagnosis, sanctions), HITL is not optional — it is ethically and often legally required. Reduces risk while preserving efficiency."},related:["ai-governance","responsible-ai","ai-auditability"]},{slug:"ai-sandbox",term:"AI Sandbox",category:"ai-governance",summary:{id:"Lingkungan terbatas dan terkontrol untuk menguji inovasi AI tanpa risiko penuh ke produksi atau pelanggan.",en:"A restricted, controlled environment for testing AI innovation without exposing production or customers to full risk."},context:{id:"OJK, Kominfo, dan beberapa kementerian sudah menjalankan regulatory sandbox. Perusahaan yang ingin menjadi early mover wajib menelusuri program-program ini.",en:"OJK, Kominfo, and several ministries already run regulatory sandboxes. Companies aiming to be early movers must track these programs."},related:["ai-governance","pojk","pse-kominfo"]},{slug:"shadow-ai",term:"Shadow AI",category:"ai-governance",summary:{id:"Penggunaan tools AI oleh karyawan tanpa sepengetahuan atau izin tim IT/compliance.",en:"AI tool usage by employees without IT or compliance team awareness or approval."},context:{id:"Shadow AI adalah risiko terbesar yang tidak terlihat di hampir semua perusahaan. Survey internal sering menemukan 5–10× lebih banyak tools AI dipakai daripada yang resmi disetujui.",en:"Shadow AI is the largest invisible risk in nearly every company. Internal surveys typically find 5–10× more AI tools in use than officially approved."},related:["ai-governance","ai-gateway","audit-trail"]},{slug:"model-card",term:"Model Card",category:"ai-governance",summary:{id:"Dokumentasi terstruktur yang menjelaskan kemampuan, keterbatasan, dan use case yang sesuai untuk sebuah model AI.",en:"Structured documentation explaining an AI model's capabilities, limitations, and appropriate use cases."},context:{id:"Praktik dari NIST dan Google yang kini menjadi standar de facto. Model card adalah artefak yang dibutuhkan auditor dan tim risk untuk menilai apakah AI cocok dipakai.",en:"A NIST/Google practice that has become a de facto standard. The model card is the artifact auditors and risk teams need to assess whether an AI fits a use case."},related:["model-governance","ai-transparency","ai-auditability"]},{slug:"automation-governance",term:"Automation Governance",category:"automation-governance",summary:{id:"Kebijakan dan kontrol yang memastikan otomasi proses bisnis (termasuk AI) berjalan sesuai standar perusahaan, hukum, dan etika operasional.",en:"Policies and controls ensuring business process automation (including AI) operates within company standards, law, and operational ethics."},context:{id:"Otomasi tanpa tata kelola bisa menyebarkan kesalahan dengan kecepatan tinggi. Tata kelola memberi rem dan audit trail di tiap langkah otomasi.",en:"Automation without governance can amplify mistakes at speed. Governance provides brakes and audit trails at every automated step."},related:["rpa","hyperautomation","workflow-automation"]},{slug:"rpa",term:"Robotic Process Automation",abbrev:"RPA",category:"automation-governance",summary:{id:"Teknologi otomasi yang meniru aksi pengguna di sistem-sistem yang ada — login, klik, copy-paste — tanpa perubahan aplikasi.",en:"Automation technology that mimics user actions in existing systems — login, click, copy-paste — without changing the applications."},context:{id:"RPA cepat diimplementasikan tapi rapuh terhadap perubahan UI. Generasi baru menggabungkan RPA dengan AI agar lebih tahan perubahan dan dapat membuat keputusan.",en:"RPA is quick to deploy but brittle to UI changes. The next generation combines RPA with AI to be more resilient and capable of decision-making."},related:["workflow-automation","intelligent-automation","agentic-ai"]},{slug:"intelligent-automation",term:"Intelligent Automation",abbrev:"IA",category:"automation-governance",summary:{id:"Penggabungan RPA dengan AI/LLM agar proses otomatis bisa memahami konteks, mengambil keputusan, dan menangani pengecualian.",en:"A combination of RPA with AI/LLMs so automated processes can understand context, make decisions, and handle exceptions."},context:{id:'IA memindahkan otomasi dari "klik berurutan" ke "menyelesaikan tujuan". Inilah yang membuat otomasi back-office benar-benar mengurangi beban kerja, bukan sekadar memindahkannya.',en:'IA shifts automation from "click in sequence" to "complete an objective". This is what makes back-office automation truly reduce workload, not just shift it.'},related:["rpa","agentic-ai","hyperautomation"]},{slug:"hyperautomation",term:"Hyperautomation",category:"automation-governance",summary:{id:"Strategi mengotomatiskan sebanyak mungkin proses bisnis dengan menggabungkan RPA, AI, process mining, dan integrasi data.",en:"A strategy to automate as many business processes as possible by combining RPA, AI, process mining, and data integration."},context:{id:"Hyperautomation menjadi prioritas board untuk efisiensi. Tanpa tata kelola yang kuat, justru menjadi sumber risiko terdistribusi yang sulit diaudit.",en:"Hyperautomation is a board-level priority for efficiency. Without strong governance, it becomes a source of distributed risk that is hard to audit."},related:["automation-governance","intelligent-automation","process-mining"]},{slug:"process-mining",term:"Process Mining",category:"automation-governance",summary:{id:"Teknik menganalisis log sistem untuk memahami bagaimana proses bisnis sebenarnya berjalan — bukan bagaimana digambar di dokumen.",en:"A technique for analyzing system logs to understand how business processes actually run — not how they are drawn on paper."},context:{id:"Process mining mengungkap kesenjangan antara SOP dan praktik. Ini fondasi untuk memutuskan langkah mana yang layak diotomatiskan dan langkah mana yang harus diperbaiki dulu.",en:"Process mining reveals the gap between SOP and practice. It is the foundation for deciding which steps deserve automation and which should be fixed first."},related:["hyperautomation","workflow-automation","observability"]},{slug:"workflow-automation",term:"Workflow Automation",category:"automation-governance",summary:{id:"Otomasi alur kerja terstruktur — approval, eskalasi, notifikasi — yang berjalan lintas tim dan sistem.",en:"Automation of structured workflows — approvals, escalations, notifications — running across teams and systems."},context:{id:"Workflow automation adalah otomasi level menengah. Lebih sederhana dari intelligent automation, tapi cukup untuk menghilangkan friksi koordinasi harian.",en:"Workflow automation is mid-tier automation. Simpler than intelligent automation, but enough to remove daily co

1ordination friction."},related:["rpa","intelligent-automation","automation-governance"]},{slug:"uu-pdp",term:"UU Pelindungan Data Pribadi",abbrev:"UU PDP",category:"compliance",summary:{id:"UU No. 27/2022 Indonesia — regulasi komprehensif yang mengatur pengumpulan, pemrosesan, dan penyimpanan data pribadi warga.",en:"Indonesia Law No. 27/2022 — a comprehensive regulation governing the collection, processing, and storage of personal data of citizens."},context:{id:"UU PDP berlaku efektif penuh 17 Oktober 2024. Sanksi mencapai 2% dari pendapatan tahunan. Tata kelola AI di Indonesia harus dirancang dengan UU PDP sebagai constraint, bukan checklist.",en:"PDP Law took full effect on October 17, 2024. Penalties reach 2% of annual revenue. AI governance in Indonesia must be designed with PDP Law as a constraint, not a checklist."},related:["pse-kominfo","pii","data-anonymization"]},{slug:"pse-kominfo",term:"PSE Kominfo",category:"compliance",summary:{id:"Penyelenggara Sistem Elektronik yang terdaftar di Kementerian Komunikasi dan Informatika — wajib untuk layanan elektronik yang beroperasi di Indonesia.",en:"Electronic System Provider registered with the Ministry of Communication and Informatics — mandatory for electronic services operating in Indonesia."},context:{id:"Pendaftaran PSE memberi kepastian regulasi kepada pelanggan enterprise dan sering jadi prasyarat tender BUMN dan instansi pemerintah.",en:"PSE registration provides regulatory certainty to enterprise customers and is often a prerequisite for SOE and government tenders."},related:["uu-pdp","bssn","public-sector"]},{slug:"pojk",term:"POJK",category:"compliance",summary:{id:"Peraturan Otoritas Jasa Keuangan — regulasi yang mengikat industri keuangan Indonesia, termasuk POJK 11/2022 untuk manajemen risiko teknologi informasi.",en:"Indonesia Financial Services Authority regulations — binding rules for the financial industry, including POJK 11/2022 on IT risk management."},context:{id:"Untuk bank, asuransi, dan multifinance, POJK menentukan apakah AI boleh dipakai untuk keputusan kredit, KYC, atau customer service. Audit trail yang siap submit OJK adalah keharusan.",en:"For banks, insurers, and multifinance, POJK determines whether AI can be used for credit decisions, KYC, or customer service. Audit trails ready for OJK submission are mandatory."},related:["ojk","uu-pdp","audit-trail"]},{slug:"ojk",term:"Otoritas Jasa Keuangan",abbrev:"OJK",category:"compliance",summary:{id:"Lembaga pengawas independen yang mengatur industri jasa keuangan di Indonesia — perbankan, pasar modal, asuransi, multifinance.",en:"Independent supervisory body regulating Indonesia's financial services industry — banking, capital markets, insurance, multifinance."},context:{id:"OJK aktif mengeluarkan pedoman penggunaan AI di sektor keuangan. Vendor AI yang tidak memahami POJK akan kesulitan menembus klien BFSI.",en:"OJK is actively issuing guidance on AI use in financial services. AI vendors that do not understand POJK will struggle to land BFSI clients."},related:["pojk","uu-pdp","ai-sandbox"]},{slug:"bssn",term:"Badan Siber dan Sandi Negara",abbrev:"BSSN",category:"compliance",summary:{id:"Lembaga negara yang mengatur keamanan siber dan kriptografi di Indonesia — termasuk standar bagi penyelenggara infrastruktur kritis.",en:"State agency regulating cybersecurity and cryptography in Indonesia — including standards for critical infrastructure operators."},context:{id:"Untuk sektor energi, telekomunikasi, dan layanan publik, BSSN menetapkan baseline keamanan. Sistem AI yang menyimpan data sensitif harus selaras dengan pedoman BSSN.",en:"For energy, telecom, and public services, BSSN sets the security baseline. AI systems holding sensitive data must align with BSSN guidance."},related:["pse-kominfo","iso-27001","zero-trust"]},{slug:"nist-ai-rmf",term:"NIST AI Risk Management Framework",abbrev:"NIST AI RMF",category:"compliance",summary:{id:"Kerangka manajemen risiko AI yang dikeluarkan NIST Amerika Serikat — kini menjadi referensi de facto global untuk tata kelola AI.",en:"AI risk management framework issued by NIST USA — now the de facto global reference for AI governance."},context:{id:"NIST AI RMF 1.0 (2023) memberikan vocab dan struktur untuk mengelola risiko AI. Diadopsi oleh banyak perusahaan global termasuk yang beroperasi di Indonesia.",en:"NIST AI RMF 1.0 (2023) provides vocabulary and structure for managing AI risk. Adopted by many global enterprises including those operating in Indonesia."},related:["ai-risk-management","ai-governance","iso-27001"]},{slug:"iso-27001",term:"ISO/IEC 27001",category:"compliance",summary:{id:"Standar internasional untuk Information Security Management System (ISMS) — dasar keamanan informasi yang diakui audit global.",en:"International standard for Information Security Management Systems (ISMS) — a globally-audited baseline for information security."},context:{id:"Sertifikasi ISO 27001 sering jadi syarat tender enterprise dan multinational. Bagi vendor AI, ini adalah credential yang membuka pintu prosedur procurement formal.",en:"ISO 27001 certification is often a requirement in enterprise and multinational tenders. For AI vendors, it is the credential that opens formal procurement procedures."},related:["iso-27799","bssn","audit-trail"]},{slug:"iso-27799",term:"ISO 27799",category:"compliance",summary:{id:"Standar keamanan informasi spesifik sektor kesehatan — turunan ISO 27001 untuk konteks Protected Health Information (PHI).",en:"Health-sector-specific information security standard — a derivative of ISO 27001 for Protected Health Information (PHI) contexts."},context:{id:"Untuk rumah sakit dan health-tech di Indonesia, ISO 27799 plus Permenkes 24/2022 RME adalah baseline minimum tata kelola AI.",en:"For hospitals and health-tech in Indonesia, ISO 27799 plus Permenkes 24/2022 EMR is the minimum AI governance baseline."},related:["iso-27001","phi","healthcare-compliance"]},{slug:"eu-ai-act",term:"EU AI Act",category:"compliance",summary:{id:"Regulasi Uni Eropa pertama di dunia yang mengkategorikan sistem AI berdasarkan tingkat risiko dan menetapkan kew

1ajiban berbeda untuk tiap tingkat.",en:"The world's first comprehensive EU regulation that categorizes AI systems by risk tier and imposes different obligations per tier."},context:{id:"Walau di luar yurisdiksi langsung Indonesia, EU AI Act memengaruhi vendor global dan menjadi acuan ketika UU Indonesia tentang AI nanti disusun.",en:"While outside Indonesia's direct jurisdiction, the EU AI Act influences global vendors and serves as a reference when Indonesia drafts its own AI law."},related:["ai-governance","nist-ai-rmf","responsible-ai"]},{slug:"gdpr",term:"GDPR",category:"compliance",summary:{id:"General Data Protection Regulation — regulasi perlindungan data Uni Eropa yang menjadi inspirasi global, termasuk UU PDP Indonesia.",en:"General Data Protection Regulation — EU data protection regulation that has globally inspired data laws, including Indonesia's PDP Law."},context:{id:"Untuk perusahaan Indonesia yang melayani pelanggan Uni Eropa atau bermitra dengan vendor global, kepatuhan GDPR adalah prasyarat operasional.",en:"For Indonesian companies serving EU customers or partnering with global vendors, GDPR compliance is an operational prerequisite."},related:["uu-pdp","data-anonymization","pii"]},{slug:"llm",term:"Large Language Model",abbrev:"LLM",category:"llm-ai",summary:{id:"Model AI berskala besar yang dilatih untuk memahami dan menghasilkan teks dalam bahasa manusia — contoh: GPT-4, Claude, Gemini.",en:"A large-scale AI model trained to understand and generate human-language text — examples: GPT-4, Claude, Gemini."},context:{id:"LLM adalah engine di balik mayoritas use case AI enterprise saat ini. Tata kelola yang tepat menentukan apakah LLM aman dipakai untuk dokumen sensitif perusahaan.",en:"LLMs power most enterprise AI use cases today. Proper governance determines whether LLMs can safely process your company's sensitive documents."},related:["slm","foundation-model","fine-tuning"]},{slug:"slm",term:"Small Language Model",abbrev:"SLM",category:"llm-ai",summary:{id:"Model bahasa ukuran lebih kecil yang dioptimalkan untuk domain spesifik — lebih cepat, lebih murah, dan bisa dijalankan di infrastruktur internal.",en:"A smaller-sized language model optimized for a specific domain — faster, cheaper, and runnable on internal infrastructure."},context:{id:"SLM cocok untuk use case yang membutuhkan kontrol penuh, biaya rendah, atau latensi rendah — seperti chatbot internal atau pemrosesan dokumen masif.",en:"SLMs fit use cases requiring full control, low cost, or low latency — like internal chatbots or mass document processing."},related:["llm","fine-tuning","on-premise-deployment"]},{slug:"foundation-model",term:"Foundation Model",category:"llm-ai",summary:{id:"Model AI berskala besar yang menjadi dasar (foundation) untuk berbagai aplikasi turunan lewat fine-tuning atau prompting.",en:"A large-scale AI model that serves as the foundation for various downstream applications through fine-tuning or prompting."},context:{id:"Memilih foundation model yang tepat menentukan kualitas dan biaya seluruh portofolio AI perusahaan. Pilihan ini sebaiknya bersifat reversibel.",en:"Choosing the right foundation model determines the quality and cost of your entire AI portfolio. That choice should remain reversible."},related:["llm","slm","fine-tuning"]},{slug:"transformer",term:"Transformer",category:"llm-ai",summary:{id:'Arsitektur neural network yang menjadi fondasi semua LLM modern — dipublikasikan Google 2017 dalam paper "Attention is All You Need".',en:'The neural network architecture that underpins all modern LLMs — published by Google in 2017 in the paper "Attention is All You Need".'},context:{id:"Pemahaman transformer membantu eksekutif menilai klaim vendor. Hampir semua LLM dari OpenAI, Anthropic, dan Google menggunakan arsitektur ini dengan modifikasi.",en:"Understanding transformers helps executives evaluate vendor claims. Nearly all LLMs from OpenAI, Anthropic, and Google use this architecture with modifications."},related:["attention-mechanism","neural-network","llm"]},{slug:"attention-mechanism",term:"Attention Mechanism",category:"llm-ai",summary:{id:"Komponen dalam transformer yang membuat model fokus pada bagian penting dari input — meniru cara manusia memperhatikan kata-kata kunci.",en:"A transformer component that makes the model focus on important parts of the input — mimicking how humans pay attention to key words."},context:{id:"Attention adalah alasan LLM bisa memahami konteks panjang. Memahami konsepnya membantu menjelaskan kenapa context window penting.",en:"Attention is why LLMs can grasp long contexts. Understanding it helps explain why context window matters."},related:["transformer","context-window","embedding"]},{slug:"embedding",term:"Embedding",category:"llm-ai",summary:{id:"Representasi 

1numerik (vektor) dari kata, kalimat, atau dokumen yang menangkap maknanya secara semantik.",en:"A numerical (vector) representation of a word, sentence, or document that captures its semantic meaning."},context:{id:"Embedding adalah fondasi semua semantic search dan RAG. Vector database menyimpan embedding untuk pencarian dengan kecepatan milidetik.",en:"Embeddings are the foundation of all semantic search and RAG. Vector databases store embeddings for millisecond-fast retrieval."},related:["vector-database","rag","tokenization"]},{slug:"tokenization",term:"Tokenization",category:"llm-ai",summary:{id:"Proses memecah teks menjadi unit-unit kecil (token) yang bisa diproses model AI — biasanya bagian kata, bukan kata utuh.",en:"The process of breaking text into small units (tokens) that an AI model can process — usually word fragments, not whole words."},context:{id:"Biaya AI dihitung per token. Memahami tokenization membantu memprediksi dan mengoptimalkan biaya — kalimat Bahasa Indonesia sering lebih boros token dibanding bahasa Inggris.",en:"AI cost is metered per token. Understanding tokenization helps predict and optimize cost — Bahasa Indonesia often consumes more tokens than English."},related:["llm","context-window","embedding"]},{slug:"context-window",term:"Context Window",category:"llm-ai",summary:{id:"Jumlah maksimum token yang bisa diproses LLM dalam satu permintaan — termasuk prompt dan jawaban.",en:"The maximum number of tokens an LLM can process in one request — including prompt and response."},context:{id:"Context window membatasi panjang dokumen yang bisa dianalisis sekaligus. Model dengan context 200K+ token (Claude, Gemini) mengubah cara dokumen panjang diproses.",en:"Context window limits how long a document can be analyzed at once. Models with 200K+ token windows (Claude, Gemini) change how long documents are processed."},related:["tokenization","llm","rag"]},{slug:"fine-tuning",term:"Fine-tuning",category:"llm-ai",summary:{id:"Proses melatih ulang model AI dengan data internal perusahaan agar lebih akurat untuk domain dan terminologi spesifik.",en:"The process of retraining an AI model with your company's internal data so it becomes more accurate for your specific domain and terminology."},context:{id:"Fine-tuning yang baik bisa mengubah model umum menjadi pakar industri Anda. Tata kelola data pelatihan menentukan apakah hasilnya aman dipakai di produksi.",en:"Good fine-tuning can turn a generic model into your industry expert. Training-data governance determines whether the result is safe for production."},related:["slm","rag","training-data"]},{slug:"rag",term:"Retrieval Augmented Generation",abbrev:"RAG",category:"llm-ai",summary:{id:"Teknik menggabungkan LLM dengan basis data internal perusahaan — AI menjawab berdasarkan dokumen Anda, bukan dari pengetahuan umum.",en:"A technique combining LLMs with your internal data — the AI answers based on your documents, not generic knowledge."},context:{id:"RAG mengurangi halusinasi dan membuat AI mengutip sumber. Untuk perusahaan, ini sering lebih praktis dan murah dibanding fine-tuning.",en:"RAG reduces hallucinations and makes the AI cite sources. For enterprises, it is often more practical and cheaper than fine-tuning."},related:["vector-database","embedding","hallucination"]},{slug:"vector-database",term:"Vector Database",category:"llm-ai",summary:{id:"Database yang dioptimalkan untuk menyimpan dan mencari embedding — fondasi infrastruktur RAG modern.",en:"A database optimized for storing and searching embeddings — the foundation of modern RAG infrastructure."},context:{id:"Pilihan vector database (Pinecone, Weaviate, pgvector, Qdrant) berdampak pada biaya, performa, dan kontrol. Untuk data sensitif, vektor di on-prem lebih disarankan.",en:"Vector database choice (Pinecone, Weaviate, pgvector, Qdrant) affects cost, performance, and control. For sensitive data, on-prem vectors are preferred."},related:["embedding","rag","on-premise-deployment"]},{slug:"prompt-engineering",term:"Prompt Engineering",category:"llm-ai",summary:{id:"Disiplin merancang instruksi (prompt) yang membuat AI menghasilkan output sesuai harapan secara konsisten.",en:"The discipline of crafting instructions (prompts) that consistently make an AI produce the expected output."},context:{id:"Prompt yang baik adalah aset perusahaan — bisa menghemat biaya AI dan meningkatkan kualitas output secara dramatis. Harus tercatat dan diversi seperti kode.",en:"Good prompts are a corporate asset — they can dramatically lower AI cost and improve output quality. They should be versioned and managed like code."},related:["few-shot-learning","chain-of-thought","function-calling"]},{slug:"few-shot-learning",term:"Few-shot Learning",category:"llm-ai",summary:{id:"Teknik prompting di mana beberapa contoh diberikan di prompt agar LLM belajar pola yang diinginkan.",en:"A prompting technique where a few examples are given inside the prompt so the LLM learns the desired pattern."},context:{id:"Sering diabaikan tapi powerful — bisa meningkatkan akurasi 10–30% tanpa fine-tuning. Praktik standar untuk extraction, classification, dan style-matching.",en:"Often overlooked but powerful — can lift accuracy 10–30% without fine-tuning. Standard practice for extraction, classification, and style-matching."},related:["prompt-engineering","fine-tuning","chain-of-thought"]},{slug:"chain-of-thought",term:"Chain of Thought",abbrev:"CoT",category:"llm-ai",summary:{id:"Teknik prompting yang meminta LLM berpikir step-by-step sebelum menjawab — sering meningkatkan akurasi pada pertanyaan logis.",en:"A prompting technique that asks the LLM to reason step-by-step before answering — often boosts accuracy on logical questions."},context:{id:"Untuk decision support dan analisis, CoT bukan opsional — outputnya lebih akurat dan lebih mudah diaudit karena reasoning-nya tertulis eksplisit.",en:"For decision support and analysis, CoT is not optional — output is more accurate and easier to audit because reasoning is written out explicitly."},related:["prompt-engineering","ai-explainability","few-shot-learning"]},{slug:"function-calling",term:"Function Calling",category:"llm-ai",summary:{id:"Kemampuan LLM untuk memanggil fungsi atau API eksternal — mengubah AI dari penjawab pertanyaan menjadi agen yang bertindak.",en:"The LLM's ability to call external functions or APIs — turning AI from a question-answerer into an acting agent."},context:{id:"Function calling adalah jembatan antara LLM dan sistem perusahaan. Tata kelola wajib: apa yang boleh dipanggil AI, dengan otoritas siapa.",en:"Function calling is the bridge between LLMs and enterprise systems. Governance is mandatory: what can the AI call, on whose authority."},related:["agentic-ai","ai-gateway","prompt-engineering"]},{slug:"temperature",term:"Temperature",category:"llm-ai",summary:{id:"Parameter LLM yang mengontrol seberapa kreatif/acak output-nya — 0 untu

1k deterministik, 1+ untuk lebih bervariasi.",en:"An LLM parameter controlling output randomness — 0 for deterministic, 1+ for more varied output."},context:{id:"Untuk use case audit-sensitive (laporan keuangan, dokumen hukum), temperature rendah memberi konsistensi. Untuk brainstorming, temperature tinggi memunculkan ide.",en:"For audit-sensitive use cases (financial reports, legal documents), low temperature provides consistency. For brainstorming, higher temperature surfaces ideas."},related:["llm","prompt-engineering","inference"]},{slug:"rlhf",term:"Reinforcement Learning from Human Feedback",abbrev:"RLHF",category:"llm-ai",summary:{id:"Teknik melatih LLM dengan umpan balik manusia agar output-nya lebih sesuai preferensi pengguna.",en:"A technique that trains LLMs with human feedback so output better matches user preferences."},context:{id:'RLHF adalah alasan ChatGPT dan Claude terasa "natural". Untuk SLM internal, fine-tuning dengan feedback manusia bisa meningkatkan akurasi domain-specific.',en:'RLHF is why ChatGPT and Claude feel "natural". For internal SLMs, human-feedback fine-tuning can lift domain-specific accuracy.'},related:["reinforcement-learning","fine-tuning","llm"]},{slug:"prompt-injection",term:"Prompt Injection",category:"llm-ai",summary:{id:"Serangan ketika input pengguna dirancang untuk mengelabui AI agar mengabaikan instruksi sistem atau membocorkan informasi rahasia.",en:"An attack where user input is crafted to trick an AI into ignoring system instructions or leaking confidential information."},context:{id:"Prompt injection adalah ekuivalen SQL injection untuk era AI. Tanpa lapisan keamanan (lihat AI Gateway), AI internal bisa membocorkan data pelanggan ke siapa saja yang tahu trik prompting.",en:"Prompt injection is the SQL-injection equivalent for the AI era. Without a security layer (see AI Gateway), an internal AI can leak customer data to anyone who knows the right prompt tricks."},related:["jailbreaking","ai-gateway","adversarial-attack"]},{slug:"jailbreaking",term:"Jailbreaking",category:"llm-ai",summary:{id:"Teknik melewati pengamanan AI agar menghasilkan output yang seharusnya dilarang — konten berbahaya, instruksi ilegal, atau data sensitif.",en:"A technique for bypassing AI safety controls to produce output that should be prohibited — harmful content, illegal instructions, or sensitive data."},context:{id:"Jailbreaking bukan teori — terjadi setiap hari. Perusahaan yang menyebar AI ke publik wajib memantau pola jailbreak dan punya kontrol berlapis.",en:"Jailbreaking is not theoretical — it happens daily. Companies deploying AI to the public must monitor jailbreak patterns and layer their controls."},related:["prompt-injection","ai-gateway","ai-red-teaming"]},{slug:"hallucination",term:"Hallucination",category:"llm-ai",summary:{id:"Ketika AI menghasilkan informasi yang terdengar meyakinkan tapi sebetulnya salah atau dibuat-buat.",en:"When an AI produces information that sounds convincing but is factually wrong or fabricated."},context:{id:"Halusinasi adalah risiko nomor satu untuk use case berdampak tinggi. Mitigasi: RAG dengan sumber tertelusur, validasi manusia untuk keputusan kritis, dan audit trail.",en:"Hallucination is the #1 risk for high-impact use cases. Mitigation: RAG with traceable sources, human validation for critical decisions, and audit trails."},related:["rag","ai-explainability","human-in-the-loop"]},{slug:"agentic-ai",term:"Agentic AI",category:"llm-ai",summary:{id:"AI yang dapat mengambil tindakan otomatis untuk menyelesaikan tujuan — bukan sekadar menjawab pertanyaan, tapi memanggil API, menjalankan workflow, dan berkoordinasi dengan AI lain.",en:"AI that can take autonomous actions to achieve a goal — not just answer questions, but call APIs, run workflows, and coordinate with other AIs."},context:{id:"Agentic AI menjadi tren 2025–2026. Tanpa tata kelola yang tepat, agen AI yang punya kemampuan eksekusi bisa menyebabkan kerugian operasional sebelum dideteksi.",en:"Agentic AI is the 2025–2026 trend. Without proper governance, AI agents with execution capabilities can cause operational damage before being detected."},related:["multi-agent-system","function-calling","ai-gateway"]},{slug:"multi-agent-system",term:"Multi-Agent System",category:"llm-ai",summary:{id:"Arsitektur di mana beberapa AI bekerja sama, masing-masing dengan peran berbeda, untuk menyelesaikan tugas kompleks.",en:"An architecture where multiple AI agents collaborate, each with different roles, to solve complex tasks."},context:{id:"Powerful untuk otomasi kompleks tapi audit trail-nya jauh lebih sulit — tata kelola harus mencatat interaksi antar-agen, bukan hanya output akhir.",en:"Powerful for complex automation but much harder to audit — governance must record agent-to-agent interactions, not just final outputs."},related:["agentic-ai","audit-trail","ai-governance"]},{slug:"ai-gateway",term:"AI Gateway",category:"llm-ai",summary:{id:"Lapisan perantara antara aplikasi perusahaan dan penyedia AI yang menerapkan keamanan, kebijakan, dan audit di setiap permintaan.",en:"A middle layer between enterprise applications and AI providers that enforces security, policy, and audit on every request."},context:{id:"AI Gateway adalah titik kontrol terpusat — tanpa-nya, organisasi tidak punya leverage menerapkan kebijakan AI yang konsisten lintas tim.",en:"An AI Gateway is the central control point — without it, an organization has no leverage to enforce consistent AI policy across teams."},related:["prompt-injection","pii","shadow-ai"]},{slug:"inference",term:"Inference",category:"llm-ai",summary:{id:"Proses menjalankan model AI yang sudah dilatih untuk menghasilkan prediksi atau output dari input baru.",en:"The process of running an already-trained AI model to produce predictions or output from new input."},context:{id:"Biaya AI di perusahaan didominasi inference, bukan training. Optimasi inference (model selection, caching, batching) adalah kunci pengendalian biaya.",en:"Enterprise AI cost is dominated by inference, not training. Inference optimization (model selection, caching, batching) 

1is the key to cost control."},related:["llm","slm","ai-gateway"]},{slug:"natural-language-processing",term:"Natural Language Processing",abbrev:"NLP",category:"ai-applied",summary:{id:"Bidang AI yang berfokus pada pemahaman dan generasi bahasa manusia.",en:"A field of AI focused on understanding and generating human language."},context:{id:"NLP adalah payung untuk customer service automation, document intelligence, dan analisis konten. LLM membuat NLP jauh lebih praktis dibanding pendekatan klasik.",en:"NLP is the umbrella for customer service automation, document intelligence, and content analysis. LLMs made NLP far more practical than classic approaches."},related:["llm","conversational-ai","sentiment-analysis"]},{slug:"computer-vision",term:"Computer Vision",abbrev:"CV",category:"ai-applied",summary:{id:"Bidang AI yang membuat komputer mampu memahami dan menganalisis gambar dan video.",en:"A field of AI that enables computers to understand and analyze images and video."},context:{id:"Use case enterprise: quality control produksi, surveillance, OCR, asset inspection. Memerlukan governance khusus karena bisa menyangkut data biometrik (UU PDP).",en:"Enterprise use cases: production QC, surveillance, OCR, asset inspection. Requires special governance because it can involve biometric data (PDP Law)."},related:["ocr","deep-learning","pii"]},{slug:"speech-recognition",term:"Speech Recognition",abbrev:"STT",category:"ai-applied",summary:{id:"Teknologi AI yang mengubah suara menjadi teks.",en:"AI technology that converts speech into text."},context:{id:"Untuk call center, transkripsi rapat, dan voice command. Akurasi untuk Bahasa Indonesia dan dialek lokal masih variatif — perlu evaluasi sebelum deployment.",en:"For call centers, meeting transcription, and voice commands. Accuracy for Bahasa Indonesia and local dialects varies — evaluate before deployment."},related:["text-to-speech","conversational-ai","nlp"]},{slug:"text-to-speech",term:"Text-to-Speech",abbrev:"TTS",category:"ai-applied",summary:{id:"Teknologi AI yang mengubah teks menjadi suara natural.",en:"AI technology that converts text into natural-sounding speech."},context:{id:"Untuk IVR, aksesibilitas, dan generasi podcast. Suara natural Bahasa Indonesia sudah memadai dengan model modern.",en:"For IVR, accessibility, and podcast generation. Natural Bahasa Indonesia voices are now serviceable with modern models."},related:["speech-recognition","conversational-ai","nlp"]},{slug:"ocr",term:"Optical Character Recognition",abbrev:"OCR",category:"ai-applied",summary:{id:"Teknologi AI yang mengekstrak teks dari gambar dokumen — KTP, invoice, formulir tulis tangan.",en:"AI technology that extracts text from document images — ID cards, invoices, handwritten forms."},context:{id:"Fondasi document intelligence. OCR akurasi tinggi untuk dokumen Indonesia (KTP, NPWP, faktur pajak) menentukan apakah otomasi back-office layak diadopsi.",en:"The foundation of document intelligence. High-accuracy OCR for Indonesian documents (ID cards, tax IDs, invoices) determines whether back-office automation is worth pursuing."},related:["idp","computer-vision","nlp"]},{slug:"idp",term:"Intelligent Document Processing",abbrev:"IDP",category:"ai-applied",summary:{id:"Kombinasi OCR, NLP, dan ML untuk memproses dokumen secara end-to-end — bukan sekadar mengekstrak teks, tapi memahaminya.",en:"A combination of OCR, NLP, and ML to process documents end-to-end — not just extracting text, but understanding it."},context:{id:"IDP mengubah back-office finance, HR, dan compliance. Tata kelola: dokumen yang berisi PII butuh masking otomatis di pipeline IDP.",en:"IDP transforms back-office finance, HR, and compliance. Governance: documents containing PII need automatic masking in the IDP pipeline."},related:["ocr","data-masking","rag"]},{slug:"recommendation-system",term:"Recommendation System",category:"ai-applied",summary:{id:"Sistem AI yang menyarankan item (produk, konten, aksi) berdasarkan preferensi pengguna dan perilaku.",en:"An AI system that suggests items (products, content, actions) based on user preferences and behavior."},context:{id:"Backbone e-commerce, media streaming, dan personalisasi. Tata kelola: filter bubble, mani

1pulasi, dan transparansi rekomendasi jadi isu reputasi.",en:"The backbone of e-commerce, media streaming, and personalization. Governance: filter bubbles, manipulation, and recommendation transparency are reputation issues."},related:["classification","predictive-analytics","responsible-ai"]},{slug:"anomaly-detection",term:"Anomaly Detection",category:"ai-applied",summary:{id:"Teknik ML untuk menemukan pola yang menyimpang dari normal — fraud, kerusakan mesin, intrusion.",en:"An ML technique for finding patterns that deviate from normal — fraud, machine failure, intrusion."},context:{id:"Salah satu use case ML dengan ROI tinggi dan paling cepat berbuah di sektor BFSI, manufaktur, dan keamanan siber.",en:"One of the highest-ROI, fastest-payback ML use cases in BFSI, manufacturing, and cybersecurity."},related:["clustering","unsupervised-learning","predictive-analytics"]},{slug:"sentiment-analysis",term:"Sentiment Analysis",category:"ai-applied",summary:{id:"Teknik NLP yang menentukan sentimen (positif/negatif/netral) dari teks — review, sosmed, survei.",en:"An NLP technique that classifies sentiment (positive/negative/neutral) from text — reviews, social media, surveys."},context:{id:"Untuk brand monitoring dan VoC. Akurasi untuk sarkasme Indonesia masih lemah — perlu validasi sample manual untuk insight yang serius.",en:"For brand monitoring and VoC. Indonesian sarcasm accuracy remains weak — manual sample validation is needed for serious insight."},related:["nlp","named-entity-recognition","classification"]},{slug:"named-entity-recognition",term:"Named Entity Recognition",abbrev:"NER",category:"ai-applied",summary:{id:"Teknik NLP yang mengekstrak entitas spesifik (nama orang, organisasi, tanggal, lokasi) dari teks.",en:"An NLP technique that extracts specific entities (people, organizations, dates, locations) from text."},context:{id:"Fondasi untuk PII detection, knowledge graph building, dan structured data extraction dari dokumen unstructured.",en:"The foundation for PII detection, knowledge graph building, and structured data extraction from unstructured documents."},related:["pii","nlp","idp"]},{slug:"predictive-analytics",term:"Predictive Analytics",category:"ai-applied",summary:{id:"Praktik menggunakan data historis dan ML untuk memprediksi peristiwa atau hasil masa depan.",en:"The practice of using historical data and ML to predict future events or outcomes."},context:{id:"Sudah matang di banyak enterprise (churn, demand, credit risk). Yang baru: kombinasi predictive analytics dengan LLM untuk narasi yang mudah dipahami business user.",en:"Mature in many enterprises (churn, demand, credit risk). What is new: combining predictive analytics with LLMs for narratives that business users can grasp."},related:["regression","classification","machine-learning"]},{slug:"conversational-ai",term:"Conversational AI",category:"ai-applied",summary:{id:"Sistem AI yang berinteraksi dengan manusia lewat dialog natural — chatbot, voice assistant, customer service co-pilot.",en:"AI systems that interact with humans through natural dialogue — chatbots, voice assistants, customer service co-pilots."},context:{id:"Investasi besar di perusahaan customer-facing. Tata kelola: persona yang konsisten, eskalasi yang jelas, dan transparansi bahwa user bicara dengan AI.",en:"A major investment for customer-facing companies. Governance: consistent persona, clear escalation, and transparency that the user is talking to AI."},related:["llm","nlp","human-in-the-loop"]},{slug:"big-data",term:"Big Data",category:"data-foundation",summary:{id:"Kumpulan data yang sangat besar, beragam, dan cepat berubah — terlalu besar untuk dikelola dengan database tradisional.",en:"A very large, varied, and fast-changing collection of data — too big to manage with traditional databases."},context:{id:'"Big data" sekarang adalah kondisi baseline, bukan tujuan. Tantangan modern: tata kelola, kualitas, dan ekstraksi nilai — bukan sekadar menyimpan.',en:'"Big data" is now a baseline condition, not a goal. The modern challenge: governance, quality, and value extraction — not just storage.'},related:["data-lake","data-warehouse","data-quality"]},{slug:"data-lake",term:"Data Lake",category:"data-foundation",summary:{id:"Penyimpanan terpusat untuk data dalam format mentah — terstruktur, semi-terstruktur, maupun tidak terstruktur.",en:"A centralized store for raw-format data — structured, semi-structured, and unstructured."},context:{id:'Data lake yang tidak ditata menjadi "data swamp". Tata kelola data (catalog, lineage, access control) adalah pembeda lake yang produktif vs yang mati.',en:'An ungoverned data lake becomes a "data swamp". Data governance (catalog, lineage, access control) is the difference between a productive lake and a dead one.'},related:["data-warehouse","data-catalog","data-pipeline"]},{slug:"data-warehouse",term:"Data Warehouse",category:"data-foundation",summary:{id:"Penyimpanan data terstruktur untuk analytics dan business intelligence — biasanya hasil ETL dari sistem operasional.",en:"Structured data storage for analytics and business intelligence — usually the result of ETL from operational systems."},context:{id:"Modern data warehouse (Snowflake, BigQuery, Databricks) mendekati cloud-scale tanpa overhead infra. Data warehouse adalah fondasi untuk decision intelligence.",en:"Modern data warehouses (Snowflake, BigQuery, Databricks) approach cloud-scale without infra overhead. The data warehouse is the foundation of decision intelligence."},related:["data-lake","etl","big-data"]},{slug:"data-pipeline",term:"Data Pipeline",category:"data-foundation",summary:{id:"Rangkaian proses otomatis yang memindahkan dan mentransformasi data dari sumber ke tujuan — operasional ke analytics, atau analytics ke AI.",en:"A series of automated processes that move and transform data from source to destination — operational to analytics, or analytics to AI."},context:{id:'Pipeline yang bocor adalah masalah AI nomor satu di praktik. Observability dan validation di tiap tahap mencegah "garbage in" sampai ke model.',en:`Leaky pipelines are AI's #1 practical problem. Observability and validation at each stage prevent "garbage in" from reaching the model.`},related:["etl","observability","data-quality"]},{slug:"etl",term:"ETL / ELT",category:"data-foundation",summary:{id:"E

1xtract, Transform, Load (atau Extract, Load, Transform) — paradigma pemindahan dan transformasi data antar sistem.",en:"Extract, Transform, Load (or Extract, Load, Transform) — the paradigm for moving and transforming data across systems."},context:{id:"ELT (transform di tujuan) lebih populer di era cloud warehouse karena compute murah. Pilihan ini berdampak pada cost, latensi, dan kompleksitas governance.",en:"ELT (transform at destination) is more popular in the cloud warehouse era because compute is cheap. The choice affects cost, latency, and governance complexity."},related:["data-pipeline","data-warehouse","data-lake"]},{slug:"data-catalog",term:"Data Catalog",category:"data-foundation",summary:{id:"Inventarisasi terorganisir dari semua dataset di perusahaan — dengan metadata, owner, dan klasifikasi sensitivitas.",en:"An organized inventory of every dataset in the company — with metadata, owner, and sensitivity classification."},context:{id:"Tanpa catalog, perusahaan tidak tahu data apa yang dimiliki — apalagi mengaturnya. Catalog jadi fondasi untuk compliance UU PDP dan tata kelola AI.",en:"Without a catalog, companies do not know what data they have — let alone how to govern it. The catalog is the foundation for PDP compliance and AI governance."},related:["data-lineage","data-lake","uu-pdp"]},{slug:"data-lineage",term:"Data Lineage",category:"data-foundation",summary:{id:"Rekaman lengkap dari mana data berasal, transformasi yang dilewatinya, dan ke mana data dipakai.",en:"A complete record of where data comes from, the transformations it goes through, and where it is used."},context:{id:'Lineage adalah dasar untuk impact analysis ("kalau kita ubah ini, apa yang rusak?") dan audit ("dari mana angka ini sebenarnya?"). Wajib untuk decision yang regulasi-sensitif.',en:'Lineage is the foundation of impact analysis ("if we change this, what breaks?") and audit ("where does this number actually come from?"). Mandatory for regulation-sensitive decisions.'},related:["data-catalog","audit-trail","ai-auditability"]},{slug:"synthetic-data",term:"Synthetic Data",category:"data-foundation",summary:{id:"Data buatan yang menyerupai data asli secara statistik tapi tidak berisi informasi pribadi nyata.",en:"Artificially generated data that statistically resembles real data but contains 

1no real personal information."},context:{id:'Solusi untuk dilema "butuh data untuk melatih AI tapi tidak bisa pakai data asli karena PDP". Validitas synthetic data perlu diuji secara matematis, bukan diasumsikan.',en:'A solution to the "need data to train AI but cannot use real data due to PDP" dilemma. Synthetic data validity must be tested mathematically, not assumed.'},related:["training-data","data-anonymization","differential-privacy"]},{slug:"data-quality",term:"Data Quality",category:"data-foundation",summary:{id:"Sejauh mana data akurat, lengkap, konsisten, dan up-to-date untuk tujuan pemakaian tertentu.",en:"The degree to which data is accurate, complete, consistent, and up-to-date for a given purpose."},context:{id:"80% project AI gagal karena data quality, bukan karena model. Investasi di data quality menghasilkan ROI lebih tinggi daripada upgrade ke model termahal.",en:"80% of AI projects fail due to data quality, not the model. Investing in data quality yields higher ROI than upgrading to the most expensive model."},related:["data-pipeline","training-data","data-catalog"]},{slug:"pii",term:"Personally Identifiable Information",abbrev:"PII",category:"data-protection",summary:{id:"Informasi yang dapat digunakan untuk mengidentifikasi seseorang — nama, NIK, NPWP, nomor telepon, alamat, email, biometrik.",en:"Information that can be used to identify an individual — name, national ID, tax ID, phone number, address, email, biometrics."},context:{id:"Deteksi dan masking PII otomatis sebelum data sampai ke AI adalah baseline tata kelola. Tanpa ini, tiap penggunaan AI berpotensi melanggar UU PDP.",en:"Automatic PII detection and masking before data reaches the AI is a governance baseline. Without it, every AI use risks PDP Law violation."},related:["phi","data-masking","uu-pdp"]},{slug:"phi",term:"Protected Health Information",abbrev:"PHI",category:"data-protection",summary:{id:"Informasi kesehatan yang dilindungi secara hukum — riwayat medis, hasil diagnosis, data BPJS, nomor rekam medis.",en:"Legally-protected health information — medical history, diagnoses, BPJS data, medical record numbers."},context:{id:"PHI memiliki sanksi yang lebih berat daripada PII biasa. UU 17/2023 Kesehatan dan ISO 27799 menetapkan kontrol tambahan untuk lingkungan klinis.",en:"PHI carries heavier sanctions than ordinary PII. Law 17/2023 on Health and ISO 27799 set additional controls for clinical environments."},related:["pii","iso-27799","data-masking"]},{slug:"data-masking",term:"Data Masking",category:"data-protection",summary:{id:"Teknik menyamarkan atau mengganti data sensitif dengan placeholder agar tetap berguna untuk pemrosesan tanpa membocorkan identitas asli.",en:"A technique for obscuring or replacing sensitive data with placeholders so it remains useful for processing without revealing the underlying identity."},context:{id:"Masking otomatis sebelum AI memproses dokumen adalah praktik terbaik. Lebih aman daripada masking pasca-proses karena data sensitif tidak pernah sampai ke model.",en:"Automatic masking before AI processes documents is best practice. Safer than post-processing masking because sensitive data never reaches the model."},related:["data-anonymization","pii","phi"]},{slug:"data-anonymization",term:"Data Anonymization",category:"data-protection",summary:{id:"Proses mengubah data sehingga subjek aslinya tidak dapat diidentifikasi lagi — bahkan jika data digabungkan dengan sumber lain.",en:"A process of transforming data so the original subject can no longer be identified — even when combined with other sources."},context:{id:'Anonimisasi yang benar berbeda dari sekadar masking. Re-identification attack sering berhasil pada data yang "terlihat" anonim. Audit oleh ahli wajib untuk dataset sensitif.',en:'Proper anonymization differs from simple masking. Re-identification attacks often succeed on data that "looks" anonymous. Expert audit is required for sensitive datasets.'},related:["data-masking","pseudonymization","differential-privacy"]},{slug:"pseudonymization",term:"Pseudonymization",category:"data-protection",summary:{id:"Mengganti identitas asli dengan kode atau alias, sambil tetap menyimpan mapping yang dapat dibalik dalam sistem terpisah.",en:"Replacing real identities with codes or aliases, while maintaining a reversible mapping in a separate system."},context:{id:"Pseudonymization lebih lemah daripada anonymization (masih dapat dibalik) tapi lebih praktis untuk operasional. UU PDP dan GDPR mengakuinya sebagai mitigation valid.",en:"Pseudonymization is weaker than anonymization (still reversible) but more practical operationally. Both PDP Law and GDPR recognize it as a valid mitigation."},related:["data-anonymization","data-masking","gdpr"]},{slug:"data-sovereignty",term:"Data Sovereignty",category:"data-protection",summary:{id:"Prinsip bahwa data tunduk pada hukum yurisdiksi tempat data tersebut secara fisik berada.",en:"The principle that data is subject to the laws of the jurisdiction where the data physically resides."},context:{id:"Bagi Indonesia, ini berarti data warga Indonesia yang disimpan di server luar negeri tetap diatur UU PDP — tapi penegakannya lebih sulit. Banyak enterprise memilih on-premise atau cloud lokal untuk menghindari ambiguitas.",en:"For Indonesia, this means Indonesian citizen data stored on overseas servers is still governed by PDP Law — but enforcement is harder. Many enterprises choose on-premise or local cloud to avoid ambiguity."},related:["on-premise-deployment","uu-pdp","private-llm"]},{slug:"mlops",term:"MLOps",category:"mlops",summary:{id:"Praktik mengoperasikan model ML di produksi secara reliable — termasuk deployment, monitoring, dan continuous training.",en:"The practice of reliably operating ML models in production — including deployment, monitoring, and continuous training."},context:{id:"Tanpa MLOps, model AI di-deploy lewat email dan PowerPoint. MLOps adalah pembeda perusahaan yang berhasil scale AI vs yang stuck di pilot eternal.",en:'Without MLOps, AI models get "deployed" via email and PowerPoint. MLOps separates companies that scale AI from those stuck in eternal pilot mode.'},related:["model-monitoring","model-registry","model-governance"]},{slug:"model-drift",term:"Model Drift",category:"mlops",summary:{id:"Penurunan performa model karena distribusi data dunia nyata berubah dari data pelatihan.",en:"Model performance degradation because real-world data distribution shifts away from training data."},context:{id:"Model bagus hari ini bisa buruk dalam 6 bulan tanpa monitoring. Drift detection adalah kewajiban operasional, bukan opsional.",en:"A good model today can be bad in six months without monitoring. Drift detection is an operational duty, not optional."},related:["model-monitoring","overfitting","mlops"]},{slug:"model-monitoring",term:"Model Monitoring",category:"mlops",summary:{id:"Pengawasan terus-menerus atas performa model di produksi — akurasi, latensi, drift, dan anomali penggunaan.",en:"Continuous oversight of model performance in production — accuracy, latency, drift, and usage anomalies."},context:{id:"Tanpa monitoring, model gagal diam-diam. Penemuan via keluhan pelanggan adalah skenario terburuk yang paling sering terjadi.",en:"Without monitoring, models fail silently. Discovery via customer complaints is the worst-case scenario — and the most common one."},related:["model-drift","observability","mlops"]},{slug:"feature-store",term:"Feature Store",category:"mlops",summary:{id:"Penyimpanan terpusat untuk fitur yang dipakai melatih dan menjalankan model — memastikan konsistensi antara training dan production.",en:"A centralized store of features used to train and serve models — ensuring consistency between training and production."},context:{id:'Feature store mencegah "training-serving skew" — kasus klasik di mana model bagus di lab tapi buruk di produksi karena fitur dihitung berbeda.',en:'Feature stores prevent "training-serving skew" — the classic case where a model is great in the lab but bad in production because features are computed differently.'}

1,related:["mlops","data-pipeline","model-monitoring"]},{slug:"model-registry",term:"Model Registry",category:"mlops",summary:{id:"Inventarisasi terstruktur dari semua model AI yang ada di perusahaan — versinya, performanya, dan status deployment-nya.",en:"A structured inventory of every AI model in the company — its versions, performance, and deployment status."},context:{id:"Registry adalah dasar untuk model governance. Tanpa registry, perusahaan tidak tahu model mana yang sedang jalan, untuk apa, dengan akurasi berapa.",en:"The registry is the foundation of model governance. Without one, companies do not know which models run, for what, and at what accuracy."},related:["model-governance","mlops","audit-trail"]},{slug:"ab-testing",term:"A/B Testing",category:"mlops",summary:{id:"Eksperimen membandingkan dua versi (model, fitur, konten) di subset user untuk mengukur mana yang lebih baik.",en:"An experiment comparing two versions (model, feature, content) on subsets of users to measure which performs better."},context:{id:"Cara aman roll-out model AI baru — pilot kecil dulu sebelum 100% pelanggan. Juga cara membuktikan ROI AI ke board dengan data, bukan asumsi.",en:"A safe way to roll out a new AI model — small pilot before 100% of customers. Also how to prove AI ROI to the board with data, not assumptions."},related:["mlops","model-monitoring","predictive-analytics"]},{slug:"audit-trail",term:"Audit Trail",category:"audit-logging",summary:{id:"Catatan kronologis lengkap dari setiap aksi sistem — siapa, kapan, melakukan apa, dengan hasil seperti apa.",en:"A complete chronological record of every system action — who, when, did what, and with what result."},context:{id:"Audit trail yang lengkap adalah pertahanan utama saat regulator, BPK, atau internal audit datang. Tanpa-nya, organisasi tidak punya cara membuktikan kepatuhan.",en:"A complete audit trail is the first line of defense when regulators, BPK, or internal audit show up. Without one, the organization cannot prove compliance."},related:["hash-chain","immutable-log","ai-auditability"]},{slug:"hash-chain",term:"Hash Chain",category:"audit-logging",summary:{id:"Teknik kriptografi yang merangkai entri log dengan hash dari entri sebelumnya — perubahan apapun langsung terdeteksi.",en:"A cryptographic technique linking each log entry with the hash of the previous one — any change is detected immediately."},context:{id:'Hash chain membuat log "tidak bisa dimanipulasi" secara struktural, bukan secara janji. Standar emas untuk audit log yang siap regulator.',en:"Hash chains make logs structurally tamper-proof — not just by promise. The gold standard for regulator-ready audit logs."},related:["immutable-log","audit-trail","ai-auditability"]},{slug:"immutable-log",term:"Immutable Log",category:"audit-logging",summary:{id:"Log yang setelah ditulis tidak dapat diubah atau dihapus — hanya ditambah (append-only).",en:"A log that, once written, cannot be modified or deleted — only appended."},context:{id:'Immutable log mencegah skenario "auditor datang, lalu log diedit". Penegakan di level database, bukan kebijakan, adalah pembeda kualitas.',en:'Immutable logs prevent the "auditor comes, log gets edited" scenario. Enforcement at the database level, not policy level, is the quality differentiator.'},related:["hash-chain","audit-trail","ai-auditability"]},{slug:"observability",term:"Observability",category:"audit-logging",summary:{id:"Kemampuan memahami kondisi internal sistem dari output eksternalnya — log, metrik, dan trace.",en:"The ability to understand a system's internal state from its external outputs — logs, metrics, and traces."},context:{id:"Untuk AI, observability mencakup biaya per tim, kualitas output per use case, dan deteksi anomali penggunaan. Tanpa-nya, organisasi terbang buta.",en:"For AI, observability covers per-team cost, per-use-case output quality, and usage anomaly detection. Without it, organizations fly blind."},related:["model-monitoring","siem","audit-trail"]},{slug:"siem",term:"Security Information and Event Management",abbrev:"SIEM",category:"audit-logging",summary:{id:"Sistem yang mengumpulkan, menganalisis, dan memberi peringatan dari log keamanan lintas-sistem secara real-time.",en:"A system that collects, analyzes, and alerts on security logs across systems in real time."},context:{id:"AI Gateway sebaiknya mengirim event ke SIEM perusahaan agar tim security punya visibility penuh atas penggunaan AI, bukan dashboard terpisah.",en:"AI Gateway should forward events to your enterprise SIEM so the security team has full visibility into AI usage, not a separate dashboard."},related:["observability","audit-trail","ai-gateway"]},{slug:"on-premise-deployment",term:"On-Premise Deployment",category:"deployment",summary:{id:"Penempatan sistem AI di data center milik perusahaan — bukan di cloud publik.",en:"Deploying an AI system in y

1our own data center — not in a public cloud."},context:{id:"On-premise menjamin data sovereignty mutlak dan sering jadi syarat sektor regulasi tinggi (BFSI, healthcare, pemerintah). Trade-off-nya: biaya infrastruktur dan operasional.",en:"On-premise guarantees absolute data sovereignty and is often required in highly-regulated sectors (BFSI, healthcare, government). Trade-off: infrastructure and operational cost."},related:["private-llm","data-sovereignty","hybrid-deployment"]},{slug:"private-llm",term:"Private LLM",category:"deployment",summary:{id:"Penempatan LLM khusus di infrastruktur Anda — data tidak pernah meninggalkan perimeter perusahaan.",en:"A dedicated LLM running on your own infrastructure — data never leaves your enterprise perimeter."},context:{id:"Private LLM mengurangi pilihan model dibanding API publik, tapi memberi kontrol penuh atas data, kebijakan, dan biaya. Sweet spot untuk dokumen sensitif.",en:"Private LLMs offer fewer model choices than public APIs, but give full control over data, policy, and cost. Sweet spot for sensitive documents."},related:["on-premise-deployment","slm","data-sovereignty"]},{slug:"hybrid-deployment",term:"Hybrid Deployment",category:"deployment",summary:{id:"Kombinasi cloud dan on-premise — data sensitif diproses internal, beban umum dialihkan ke cloud publik.",en:"A combination of cloud and on-premise — sensitive data processed internally, general workloads sent to public cloud."},context:{id:"Hybrid sering jadi titik tengah pragmatis: kontrol sesuai sensitivitas data, fleksibilitas sesuai beban kerja. Tata kelola routing-nya yang kritis.",en:"Hybrid is often the pragmatic middle ground: control by data sensitivity, flexibility by workload. Routing governance is the critical part."},related:["on-premise-deployment","private-llm","ai-gateway"]},{slug:"air-gapped-deployment",term:"Air-Gapped Deployment",category:"deployment",summary:{id:"Penempatan sistem AI yang sepenuhnya terisolasi dari internet — tidak ada koneksi keluar sama sekali.",en:"AI system deployment fully isolated from the internet — no outbound connections at all."},context:{id:"Standar tertinggi untuk klasifikasi data rahasia (pertahanan, keuangan negara). Pilihan model terbatas (hanya yang bisa dijalankan offline), tapi risiko data leak minimal.",en:"The highest standard for classified data (defense, state finance). Limited model choices (only offline-runnable), but minimal data leak risk."},related:["on-premise-deployment","private-llm","data-sovereignty"]},{slug:"zero-trust",term:"Zero Trust",category:"security",summary:{id:"Model keamanan yang tidak mempercayai siapapun secara default — setiap permintaan, dari mana pun asalnya, harus diverifikasi.",en:"A security model that trusts no one by default — every request, regardless of origin, must be verified."},context:{id:'Zero Trust sangat relevan untuk AI: setiap permintaan ke LLM harus diautentikasi, diotorisasi, dan tercatat — termasuk dari aplikasi internal yang "tepercaya".',en:'Zero Trust is highly relevant for AI: every LLM request must be authenticated, authorized, and logged — including from "trusted" internal applications.'},related:["ai-gateway","rbac","least-privilege"]},{slug:"rbac",term:"Role-Based Access Control",abbrev:"RBAC",category:"security",summary:{id:"Sistem akses di mana hak pengguna ditentukan oleh peran (analyst, admin, auditor) bukan oleh identitas individu.",en:"An access system where user rights are determined by role (analyst, admin, auditor) not by individual identity."},context:{id:"Untuk AI, RBAC menentukan siapa boleh memakai model mana, dengan budget berapa, untuk use case apa. Fondasi tata kelola akses AI di skala enterprise.",en:"For AI, RBAC determines who can use which models, with what budget, for what use cases. Foundation of enterprise-scale AI access governance."},related:["least-privilege","zero-trust","ai-gateway"]},{slug:"least-privilege",term:"Least Privilege",category:"security",summary:{id:"Prinsip bahwa pengguna atau sistem hanya diberi hak minimum yang diperlukan untuk menjalankan tugasnya.",en:"The principle that users or systems are given only the minimum rights necessary to perform their tasks."},context:{id:"Untuk AI, least privilege berarti aplikasi internal tidak diberi akses ke seluruh model atau ke seluruh data. Membatasi blast radius bila terjadi compromise.",en:"For AI, least privilege means internal applications are not given access to all models or all data. Limits blast radius if a compromise occurs."},related:["rbac","zero-trust","ai-gateway"]},{slug:"ai-red-teaming",term:"AI Red Teaming",category:"security",summary:{id:"Praktik mengetes sistem AI de

1ngan simulasi serangan — prompt injection, jailbreaking, ekstraksi data — untuk menemukan kelemahan sebelum penyerang.",en:"The practice of stress-testing an AI system with simulated attacks — prompt injection, jailbreaking, data exfiltration — to find weaknesses before attackers do."},context:{id:"Wajib sebelum men-deploy AI ke publik atau ke fungsi sensitif. Tim red team yang dedicated adalah investasi yang jauh lebih murah dibanding insiden produksi.",en:"Mandatory before deploying AI to the public or to sensitive functions. A dedicated red team is far cheaper than a production incident."},related:["adversarial-attack","prompt-injection","jailbreaking"]},{slug:"adversarial-attack",term:"Adversarial Attack",category:"security",summary:{id:"Serangan terhadap sistem AI dengan input yang dirancang khusus untuk mengelabui — menghasilkan output salah, kebocoran data, atau crash.",en:"An attack on an AI system using specially-crafted input to fool it — yielding wrong output, data leakage, or crashes."},context:{id:"Adversarial attack di production AI sudah terjadi di banyak industri. Vendor AI yang tidak punya defense layer adalah liability untuk enterprise.",en:"Adversarial attacks on production AI are already happening across industries. AI vendors lacking a defense layer are a liability for enterprises."},related:["prompt-injection","jailbreaking","ai-red-teaming"]},{slug:"model-poisoning",term:"Model Poisoning",category:"security",summary:{id:"Serangan dengan menyisipkan data berbahaya ke training set agar model belajar perilaku yang menguntungkan penyerang.",en:"An attack that injects malicious data into the training set so the model learns behavior beneficial to the attacker."},context:{id:"Risiko tinggi untuk model yang sering di-retrain dari data eksternal. Validasi sumber data pelatihan adalah kontrol fundamental.",en:"High risk for models frequently retrained from external data. Validation of training data sources is a fundamental control."},related:["training-data","adversarial-attack","ai-red-teaming"]},{slug:"differential-privacy",term:"Differential Privacy",category:"security",summary:{id:"Teknik matematis yang menambahkan noise terkontrol ke output agar individu dalam dataset tidak dapat di-identifikasi ulang.",en:"A mathematical technique that adds controlled noise to output so individuals in the dataset cannot be re-identified."},context:{id:"Standar emas untuk merilis insight statistik dari data sensitif. Apple, Google, dan beberapa bank sentral sudah memakai differential privacy di produk yang dihadirkan ke publik.",en:"The gold standard for releasing statistical insights from sensitive data. Apple, Google, and several central banks already use differential privacy in public-facing products."},related:["data-anonymization","pseudonymization","synthetic-data"]},{slug:"ai-watermarking",term:"AI Watermarking",category:"security",summary:{id:"Teknik menyisipkan tanda tersembunyi di output AI (teks, gambar, audio) agar bisa dideteksi sumbernya.",en:"A technique that embeds hidden markers in AI output (text, images, audio) so its origin can be detected."},context:{id:"Relevan untuk media perusahaan dan compliance EU AI Act. Watermarking belum sempurna tapi merupakan bagian dari toolkit governance konten generatif.",en:"Relevant for enterprise media and EU AI Act compliance. Watermarking is imperfect but part of the generative content governance toolkit."},related:["generative-ai","eu-ai-act","ai-governance"]}];function r(a){return n.find(e=>e.slug===a)}export{n as a,t as b,i as c,r as g};