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Enterprise AI adoption is failing at a 95% rate primarily due to governance gaps, not technology limitations. Key challenges include unclear accountability frameworks for LLM outputs, data privacy risks (highlighted by the Samsung/ChatGPT incident), vendor lock-in through API dependencies and egress fees, and complex procurement contracts that shift liability to customers. The EU AI Act and GDPR impose significant compliance requirements, including data sovereignty obligations. Practical mitigations discussed include RACI frameworks for AI accountability, RAG as a safer alternative to fine-tuning for injecting proprietary knowledge, AI model gateways to reduce vendor lock-in, and self-hosting trade-offs. Prompt injection is identified as the top OWASP LLM risk, with indirect injection enabling data exfiltration. The post concludes that organizations succeeding with enterprise AI prioritize governance infrastructure over speed of deployment.

Enterprise AI Without Guardrails

Exploring the intersections of artificial intelligence, decentralised cognition, posthuman ethics, society and culture.