This summer, the White House rolled out the Gold Eagle Initiative, billed as an “unprecedented” effort to coordinate cybersecurity vulnerability disclosure across government and industry.
This summer, the White House rolled out the Gold Eagle Initiative, billed as an “unprecedented” effort to coordinate cybersecurity vulnerability disclosure across government and industry.
To understand what a clearinghouse is, and why the number of them is increasing, it’s important to understand what has broken — namely (and hugely), the vulnerability disclosure process. For many years, vulnerability disclosure worked like this: A researcher found a bug, reported it privately, and honored an embargo period that gave vendors time to patch quietly (and hopefully quickly) and ship the fix before the bug was made public. This gave the good guys a chance to patch before the bad guys had a chance to use the vulnerability for bad guy things. The process was tedious, but it worked because the economics worked — historically, only nation-states had the resources to hunt for — or reward teams of experts to hunt for — undisclosed vulnerabilities at scale.
Of course, AI has changed all that — all of that. The average gap between the discovery of a vulnerability and its weaponization has collapsed from something measured in years to negative seven days. The bad guys are finding CVEs and exploiting them long before the good guys have had a chance to patch them. A terrifying prospect, to be sure. And it’s something I embarrassingly admit has happened to me personally several times recently.
Frontier AI models are democratizing the finding, chaining, and weaponization of flaws faster and cheaper than any team of human experts. Indeed, it’s the chaining — and AI’s mastery of it — that may be the most debilitating. In chaining attacks, two or three low-severity and moderate-severity bugs get stitched together by an AI system. On their own, these bugs are pretty unremarkable. However, by joining the “right” ones together (something AI is very, very good at), a model can achieve a full compromise.
In context
- Topic: Ciberseguridad — Riesgo, identidad, respuesta a incidentes y cumplimiento.
- Source: InfoWorld
- Published: 19/10/2026
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Why it matters
In security the story is rarely the attack. It is the time between the intrusion and somebody noticing. That number says more about an organisation than any certificate hanging on the reception wall.
My way of reading these cases is to ask what failed in the process, not what failed in the machine. Behind nearly every incident there is a reasonable decision taken under pressure: granting a permission to unblock somebody, postponing an update because it was month-end. That is where the lesson lives.
What usually goes wrong
What I see fail most is the backup. It is configured, it runs every night, nobody checks it. The day you need to restore, it turns out it had been failing silently for four months, or that everything was backed up except exactly what was needed. A backup never restored is an assumption.
What to watch
- Whether third parties or suppliers were in the chain, because the perimeter now includes partners.
- How long it took to detect, usually the most revealing metric in the whole case.
- Whether initial access came from a legitimate account handled badly, which is the most frequent pattern.
How I read this entry
What I would review this very week is access: accounts belonging to people who left, permissions that grew without anyone trimming them, and shared credentials everybody swears do not exist. It is the least glamorous work and the one that cuts off the most attacks.
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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Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
