Sistemas de inteligência artificial já provocam incidentes de segurança na maioria das organizações. Pesquisa da Trellix com 600 CISOs ou profissionais equivalentes nas Américas, Europa, Oriente Médio e Ásia-Pacífico aponta que 95% dos entrevistados registraram, nos últimos 12 meses, ao menos um incidente ligado a açõe…
Sistemas de inteligência artificial já provocam incidentes de segurança na maioria das organizações. Pesquisa da Trellix com 600 CISOs ou profissionais equivalentes nas Américas, Europa, Oriente Médio e Ásia-Pacífico aponta que 95% dos entrevistados registraram, nos últimos 12 meses, ao menos um incidente ligado a açõe…
Em contexto
- Tema: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
- Fonte: CISO Advisor
- Publicado: 17/10/2026
Continuar lendo na fonte original →
Trecho publicado automaticamente pelo radar do site. O texto completo pertence ao veículo e está vinculado acima.
Why it matters
Adopting AI in a company is less like buying software and more like hiring someone. You have to teach it the context, review its work at the start, and define what it can sign off alone and what it cannot. The stories worth reading are the ones showing somebody solving that part, not the announcement part.
The first thing I look at is not model capability but the data feeding it. Almost every project I have seen fail did not fail on the algorithm. It failed because the data lived in three systems with three different definitions and nobody wanted to be the one to fix that.
What usually goes wrong
What I see fail most is the expectation. Somebody saw a flawless demo and asked for the same thing in their area within a month, without considering the demo ran on clean data prepared for the occasion. When the real pilot shows uneven results, the unfair conclusion is that the technology does not work.
What to watch
- Whether the organisation can change provider without rebuilding everything, which is the proof it did not get locked in.
- Which concrete process is touched and how it is measured before and after; with no baseline there is no result, only opinion.
- Who reviews the model outputs and how often — the point at which AI becomes auditable.
How I read this entry
I would use it for a talent conversation rather than a purchasing one. The capability that needs installing is not operating a tool, which changes every six months, but framing the problem well and judging the answer. That one stays in the house.
The original story is published in another language; the excerpt is quoted as the publisher delivers it and the commentary is written in English.
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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
