Good AI governance isn’t about doing everything fast. It’s about understanding what matters before taking action. Most companies rush into AI governance by adding random rules, checklists, and risk assessments. But the smarter approach is to pause first and ask: - What AI systems are we actually using? -... Which laws and regulations apply to us? - What risks do we truly care about? - Who are our clients and stakeholders? Because if you skip this clarity step, you’ll keep rebuilding everything later. Read the blog attached to learn more about AI governance layers.show more

map then move

Haha

hard agree with this take bro

Yes😇

now that harnesses are a thing, some of the gov needs to drift there. out-of-band file validation enables deeper checks --> lower fp/tn while not getting the agent slower. and fix one for a multi-agent.

which industry is getting hit hardest by ai regs rn - finance or healthcare?

Healthcare, I think. So much restrictions there.

AIガバナンスの重要性は、機敏に行動することではなく、行動を起こす前に何が重要かを理解することです。ランダムなルール、チェックリスト、リスク評価を追加することでAIガバナンスに急いで取り組む企業が多いですが、賢いアプローチは最初に一呼吸置き、以下を考えることです： - 私たちが実際に使っているAIシステムは何ですか？ - 私たちに適用される法律や規制は何ですか？ - 我々が本当に気にするリスクは何ですか？ - 私たちのクライアントや利害関係者は誰ですか？ この明確さのステップをスキップすると、後ですべてを再構築し続けなければなりません。AIガバナンスのレイヤーについて詳しく知りたい場合は添付されたブログを読んでください。

The 'add more rules' trap is real. Every AI concern triggers a new policy layer until governance overhead costs more than the risk it was protecting. Better frame: instead of 'what could go wrong', start with 'what does good look like and what's the minimum required to enable it consistently'. Constraints should enable, not just restrict.

Agreed. But “understand first” has to become an executable governance structure. For agentic AI, understanding is not enough unless the system can prove: what the original intent was, what authority was granted, which agent acted, which tool action changed the world, what evidence supported it, and whether the outcome was accepted. That is why I define AI Agent Governance as a lifecycle problem. MPLP turns this into protocol objects, not just advisory language. Full framework: #AICompliance #AIGovernance #MPLP

That's a very well written article. The feedback loop is the most important layer and what keeps it all relevant. Teams adopt AI tools faster than governance can wrap controls around it

大切なのは、行動を起こす前に何が重要かを理解することです。AIガバナンスは急いですべてを行うことではありません。ランダムな規則、チェックリスト、リスク評価を追加する企業が多いですが、賢いアプローチはまず一呼吸置くことです。どのAIシステムを実際に使用しているのか、どの法律や規制が適用されるのか、どのリスクが本当に重要か、私たちのクライアントやステークホルダーは誰か。この明確さのステップをスキップすると後ですべてを再構築しなければなりません。

The companies that pause longest on governance are usually using it as cover to delay AI adoption entirely. There's a real difference between thoughtful risk assessment and institutional inertia dressed up as caution.

重要なことは何を理解することか。急ぎすぎることはダメ。