ColumnAI procurement red-team and vendor acceptance testing4 分鐘閱讀

Memo keputusan: jalankan red team pemerolehan AI seminggu sebelum membeli Copilot

更新 2026/06/15Melayu

Layan vendor AI sebagai sistem untuk diuji tekanan, bukan demo untuk dikagumi. Red team seminggu menukar sumber NIST, OWASP, Anthropic dan Google Cloud menjadi keputusan pembelian.

圖片來源： ALTOS LAB editorial visual

Key Points

Run a one-week procurement red team before signing AI suites.
Map NIST lifecycle trust and OWASP GenAI risks into acceptance criteria.
Require shutoff, rollback, audit trail and cost ceiling before production use.

Tuesday morning, two AI suite proposals land on the procurement table. One demo is elegant; the other promises that controls can be “configured by policy.” Tomorrow the board needs a decision. The real question is not which interface looks better. It is which system can be stopped, rolled back and audited when it fails.

> ALTOS LAB judgment: vendor demos show the best day; a procurement red team tests the worst day.

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The one-week test

Day 1 maps permissions: data, tools, external APIs and human approval points. Day 2 tests factuality across languages and repeated prompts. Day 3 runs misuse drills: prompt injection, over-permissioned tools and wrong output entering a record system. Day 4 checks output handling: source fields, sensitive-data masking and review gates. Day 5 sets cost ceilings for tokens, retries and tool calls. Day 6 places the candidate inside a small real workflow. Day 7 gives only three decisions: approve, repair or reject.

What the sources change

NIST frames trustworthy AI across design, development, use and evaluation; this turns procurement from a demo score into lifecycle evidence. OWASP 2025 names the attack surface: prompt injection, sensitive information disclosure, excessive agency, misinformation and unbounded consumption. Anthropic’s 2025 circuit-tracing work shows that transparency is improving, but also that it still covers only part of model computation. Google Cloud’s 2026 list of 1,302 GenAI use cases shows why the issue is urgent: companies are buying agent teams, not just chatbots.

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Three red lines

Permissions must narrow. Every tool call needs task, owner, data scope and timestamp.

Outputs must be governable. Model text needs sources, review, masking and rollback.

Cost must stop by design. If retries or tool calls run away, the system must halt automatically.

ALTOS LAB recommendation: put these tests into the contract. Vendors that accept the red team can enter negotiation; vendors that only offer demos stay on the observation list. An AI system that cannot be shut down, rolled back and audited today should not enter a core workflow tomorrow.

Editorial procurement table showing AI vendor evidence cards, risk lanes and a visible stop path — The strongest procurement meeting starts by testing failure modes, not features.

Mechanism diagram showing permission gate, factuality test, rollback path and cost ceiling for AI procurement — A one-week red team turns AI vendor claims into operating evidence.

Sources

NIST AI Risk Management Framework · NIST · 2026/04/07
AI RMF and GenAI profiles frame trust across design, development, use and evaluation.
OWASP 2025 Top 10 Risks & Mitigations for LLMs and Gen AI Apps · OWASP Gen AI Security Project · 2025/01/01
OWASP lists GenAI risks such as prompt injection, excessive agency, misinformation and unbounded consumption.
Tracing the thoughts of a large language model · Anthropic · 2025/03/27
Anthropic circuit-tracing research shows useful transparency signals and clear method limits.
1,302 real-world gen AI use cases from industry leaders · Google Cloud · 2026/04/22
Google Cloud documents 1,302 GenAI use cases across 11 industries and six agent types.

Tommy

ALTOS LAB 產品與 AI 導入編輯，關注企業流程、生成式搜尋與能真正落地的決策框架。