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Sonar AI Agent Discovers Vulnerabilities Hidden in Business Logic Workflows

Sonar today made available an artificial intelligence (AI) agent designed to discover vulnerabilities and business logic flaws that pose the greatest risk to an organization should they be exploited.

Sonar today made available an artificial intelligence (AI) agent designed to discover vulnerabilities and business logic flaws that pose the greatest risk to an organization should they be exploited.

In context

  • Topic: Inteligencia Artificial — IA aplicada al negocio: casos, límites, costos y gobierno.
  • Source: DevOps.com
  • Published: 27/08/2026

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Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.

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

The point where it usually breaks is the handover to operations. The team that built the pilot knows how to read the results; the team receiving it does not. Without training and written criteria, people start accepting everything the model says or ignoring it entirely, and both are equally expensive.

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

My practical advice is to fix on day one what happens when the model is wrong: who reviews, how often, and at what point it gets switched off. It sounds defensive, but it is exactly what lets you be aggressive later, because the risk stopped being an unknown.

This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
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