Saram Consulting감사관들은 생명과학 분야의 AI를 어떻게 다루고 있는가: 경고 서한부터 보증 아키텍처까지
FDA는 최초의 AI 중심 cGMP 경고 서한을 발송했습니다. MHRA는 실존하지 않는 규정을 인용한 AI 생성 실사 답변서를 적발했습니다. EMA는 GMP 내 AI를 위해 Annex 22를 제정 중입니다. 감사 체계가 AI를 평가하는 방식을 근본적으로 재편하고 있으며, 대부분의 바이오 제약 기업은 아직 준비되지 않았습니다.
글 읽기 →Saram 인사이트 (Saram Insights)
바이오텍, 인공지능, 규제 컴플라이언스의 융합에 관한 심층 전략 및 실전 아키텍처 분석.
Backup Is a Validation Control: Architectures for Hardened GxP Equipment
Hardened PLCs, chromatographs, and cleanroom controllers can't take backup agents, inbound traffic, or OS patches — yet auditors expect documented, tested recovery of the exact validated state. The architectures that work, the controls that make a restore defensible, and why 'we have backups' is not an answer.
글 읽기 →Disaster Recovery in GxP: The Plan That Passes, The Test That Proves It, and the Agent That Runs It
Tabletop exercises will not save you from a 483. What auditors actually check in a DR plan, why technical restore testing is non-negotiable for Tier 1 GxP systems, and how to architect an AI agent that continuously proves your regulated systems are recoverable.
글 읽기 →Knowledge Graphs Without Hallucinated Edges: The Extraction Architecture Behind Defensible GraphRAG
Every knowledge graph in a regulated company is one hallucinated edge away from being an audit finding. The fix is not a better prompt — it is an extraction architecture where the LLM proposes and deterministic infrastructure disposes: ontology-first design, span-grounded two-pass extraction, NLI verification, entity resolution, and a claim ledger.
글 읽기 →The Five-Minute Thinking Loop: Why Your Coding Agent Never Starts Coding
Your agent isn't stuck — it was told to think. LLM-generated prompts inflate perceived task complexity, reasoning models price thinking off surface signals, and the result is thousands of hidden reasoning tokens and zero tool calls. Here is the mechanism, the model behavior behind it, and the prompting discipline that fixes it.
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