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Help Net Security

GitHub Agentic Autofix and the question of who owns a bad AI patch

In this Help Net Security interview, Marcelo Oliveira, VP of Product Management at GitHub, talks about who is accountable when an AI-generated patch fixes one flaw and introduces another. He explains how Agentic Autofix re-runs detection engines and project tests, and what a CISO should change before pointing an agent at thousands of old alerts. Oliveira also discusses which metrics show a codebase is getting safer,…

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Help Net Security

Dagster: Open-source cloud-native data pipeline orchestrator

Dagster is an open source orchestrator for data pipelines. It decides what runs, in what order, and when. It also tracks how the tables, datasets, machine learning models, and reports it manages connect to the data they came from. When a source table goes bad, the tables built from it carry the problem forward. Dagster’s lineage graph shows which ones sit downstream, and that list is the starting point for fixing a b…

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Help Net Security

Who collects the keys when an AI agent retires?

Think about the last time a good engineer left your company. Someone collected the laptop and the badge. IT revoked their logins before the end of the day. Their manager reassigned the open tickets, and HR filed their records somewhere they’ll stay for years, in case anyone ever challenges a decision they made. Now think about the last time an AI agent left. Someone probably deleted it from the dashboard. I’ve starte…

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Help Net Security

CISOs say OT will survive a cyberattack, but their engineers aren’t so sure

Ask industrial security chiefs whether their plants would keep running through a full IT compromise, and every one who answered the SANS 2026 ICS/OT survey says yes. Thirty-eight percent expect to run indefinitely on islanded ICS/OT systems, and 62% expect to get by temporarily on manual controls. None foresee degradation or a halt. The 696 practitioners SANS surveyed, the people who would run those manual controls,…

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Help Net Security

Bots pose as AI agents, AI agents pose as humans, and defenses believe both

Over one week in October, an online marketplace took 414,210 visits from traffic wearing the name of a widely used consumer AI agent. Every visit went for the sign-in page, and every one returned errors. Behind the name was a credential stuffing operation, with attackers running stolen username and password pairs against the site in bulk. The fake agent was busier than the real ones. Its volume came to about one and…

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