The AI Shutdown No One Saw Coming and What Enterprises Must Do Before the Next One

When a government pulls access to an AI model enterprises are already building on, it exposes a vulnerability most security strategies haven't accounted for

Security, Privacy & ComplianceInterview

Published: June 23, 2026

Francesca Roche

Francesca Roche

Francesca Roche sits down with Shimon Tolts, Co-founder and CEO at Copperhelm, to discuss how the US government’s decision to restrict access to Anthropic’s Mythos mode did something that had simply never happened before.

“We never had a government ban technology that was already there,” he says.

“Like closing a SaaS — you can no longer use this functionality.”

For Tolts, that moment crystallized a risk that enterprises have been quietly accumulating: deep dependency on AI infrastructure they have no control over.

Copperhelm, the agentic cloud security platform, was itself affected. Mid-build on solutions leveraging Mythos, and a participant in Anthropic’s cyber validation programme, the company found itself having to pivot fast.

That experience shapes his advice to enterprise security teams: assume any model can disappear, and build accordingly. A multi-model approach isn’t a nice-to-have — it’s operational hygiene.

But the Mythos episode is just one thread in a much larger tension Tolts unpacks. AI-driven offensive security is accelerating attack timelines in ways that are fundamentally reshaping what good security practice looks like.

The window between a vulnerability being exposed and an exploit being developed has compressed to roughly one day.

That shift, he argues, is forcing a dramatic change in posture — from risk management to what boards are now calling “zero risk,” particularly on external-facing assets.

What makes that demand achievable — just barely — is the kind of AI-native, agentic infrastructure Copperhelm has built from the ground up. Tolts is pointed about what that means in practice: bolt-on AI doesn’t cut it.

“It’s like taking a Ferrari engine and putting it in a Fiat,” he says.

The real work is in building context-aware systems that can filter the noise — and his figures on how much of that noise there actually is are striking.

He also touches on a mindset shift he’s witnessing in real time among CISOs, who as recently as February were refusing to consider autonomous remediation — and are now actively asking for it.

The Mythos fallout, he suggests, has accelerated a reckoning that was already coming.

For security leaders trying to get ahead of a threat landscape that is moving faster than their tooling, this is a candid, practitioner-level perspective worth watching.

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