TL;DR: Prompt injection isn’t just a string-matching issue. It’s a governance problem: unclear boundaries, over-privileged tools, and missing approvals. Filters help, but durable defense requires policy, permissions, and process—implemented as code. This playbook shows how to prevent, detect, and contain injection across chat, RAG, agents, and screen/RPA flows. 1) What counts as prompt injection (and […]
TL;DR: Red teaming generative AI isn’t just “try to break the bot.” It’s a structured program that tests content risks (hallucination, toxicity, IP leakage), security risks (prompt injection, data exfiltration), and action risks (unsafe tool calls, financial/operational harm). This guide shows you how to plan, execute, score, and report red-team results so product, security, and […]
TL;DR: Traditional Model Risk Management (MRM) still applies—governance, validation, monitoring, change control—but generative AI (GenAI) adds new moving parts (prompts, retrieval, agents, tool-calls, content risks) and faster vendor updates. This playbook adapts core MRM to GenAI so you can ship value while staying audit-ready. 1) What “counts as a model” in GenAI (broaden your inventory […]
TL;DR: “Shadow AI” isn’t a hypothetical. It’s the prompt pasted into a public chatbot, the spreadsheet add-in that autocompletes sales notes, or the intern’s browser extension summarizing HR files. This playbook shows you how to discover unsanctioned AI use fast, triage risk, and enable approved AI with audit-ready controls—without killing productivity. What Counts as “Shadow […]
TL;DR: Vanilla RAG (vector search → stuff context → generate) is great for “find and summarize.” It breaks on multi-hop questions, entity-heavy domains, and anything that needs joins, constraints, or causality. GraphRAG adds an explicit knowledge graph—entities, relationships, events—so the model retrieves structured facts first, then uses text passages as evidence. The result: higher factuality, […]
TL;DR: We’re moving from clicking through apps to delegating outcomes to AI agents—systems that can plan, use tools, take actions, and learn from feedback. The winning stacks combine small, fast models for routine steps with large models for edge cases, wrapped in strong guardrails, observability, and human oversight. If you design for workflows (not chat), […]
Model Risk Management 2.0: Translating MRM Principles to Generative AI
TL;DR: Traditional Model Risk Management (MRM) still applies—governance, validation, monitoring, cha...
Automotive: The Future of Autonomous Vehicles: Challenges and Opportunities in the Automotive Industry
Introduction Autonomous vehicles (AVs) have emerged as a transformative force in the automotive indu...
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