Anthropic models' foreign national ban exposes AI dependency risks

Major issues for CIOs

Fable 5 had only been available for three days before Anthropic shut off access, making the immediate impact on organizations limited. But the actions raise questions for CIOs and IT leaders about the way these increasingly important AI models need to be governed.

Enterprises need to fully understand sovereignty dependencies and design model-agnostic architectures wherever possible, according to industry experts.

There is a need to protect evolving AI capabilities from misuse but broad restrictions that limit access based on nationality create challenges for U.S. tech companies that depend on global talent, said Kishore Khandavalli, CEO at software development and services companies RiseIT Solutions and SevenTablets.

"Many of the engineers, researchers and entrepreneurs building America’s AI leadership are foreign nationals working legally in the United States," Khandavalli said. "Policies like this must be carefully balanced so they protect national security without weakening the innovation ecosystem that gives the U.S. its competitive advantage."

The right path forward is thoughtful governance through stronger security controls, responsible access management and clear compliance standards, he said.

"America’s leadership in AI will depend on both protecting critical technology and continuing to attract the world’s best minds to build it here," Khandavalli added.

The blockage of the Anthropic models to foreign nationals is concerning for enterprises because it's unlike most current export control regimes, according to Hamilton Mann, executive chairman and president at Artificial Integrity Institute.

These are based on a principle of ex-ante restriction, where organizations know the boundaries in advance and architect their systems accordingly, he said.

Examples of ex-ante restrictions include a semiconductor manufacturer that knows a certain chip cannot be sold to a specific country, a defense contractor that knows certain technical data cannot be shared with particular nationals or a cloud provider that never offers a service in a sanctioned jurisdiction.

"What makes the Anthropic case unsettling for corporate executives, not only CIOs, is that it introduces the possibility of ex-post withdrawal," Mann said. "This represents a fundamentally different risk profile -- a shift from access restriction to dependency disruption."

Organizations may already have integrated the model into workflows, trained employees on it, built agents around it, embedded it into software development processes and incorporated it into customer-facing products, he said.

"Once that dependency exists, removing the capability becomes operationally disruptive," Mann said. "This is less akin to denying access to a technology and more akin to a supplier abruptly terminating deliveries, a cloud provider shutting down a service or a critical API being withdrawn."

Models are now critical infrastructure

For many enterprises, the issues go beyond the reliance on foreign nationals and must be regarded as a central part of good infrastructure design and management.

Businesses should always plan for have to plan for single points-of-failure in critical infrastructure, according to Donna Wilczek, chief product and technology officer at Basware, a global provider of invoice lifecycle management software and services.

Frontier models like Anthropic's have now become an integral part of the infrastructure.

"Today with Anthropic, it's proof positive that all of the plans and processes that businesses have taken through the years to identify single points-of-failure also apply now to the models themselves," Wilczek said. "So, the same approaches that they've taken with understanding their technology landscape now must incorporate the model as critical infrastructure."

CIOs need to have a flexible approach and not rely on a single model or have a rigid mindset, she said. This means that they will not become stranded should a model become unavailable regardless of the reason, in this case a government action.

"If a model is not available, it cannot shut down your business, you have to be able to operate leveraging an alternate model in those situations," Wilczek said.

If a model is not available, it cannot shut down your business, you have to be able to operate leveraging an alternate model in those situations.

There are two critical ways for CIOs to protect against being caught if a particular model is suddenly made unavailable, according to Wilczek.

First, they must have a thorough understanding of their critical infrastructure and inventory all the decision points where they are using AI and what models are being consumed by that AI. Second, they must think of agentic AI less as an on-off switch and more about how they can have a governed autonomy model that allows them to operate regardless of what model is available or what regulations are in place in a specific country.

"For CIOs, single points-of-failure have always been a critical element of their responsibility and now we're seeing that the models themselves are part of the critical infrastructure, and they need to act accordingly," Wilczek said.

A capability sovereignty problem

One of the main issues that the situation exposes is that governance of agentic AI systems is shifting from a solely data sovereignty problem to a capability sovereignty problem, according to Chris Willis, chief design officer and futurist at Domo, a cloud-based business intelligence and data analytics platform provider.

Organizations may have stored data with proper sovereignty requirements, but in this case, it wouldn’t have mattered if they didn't have the intelligence to run automations or agentic processes, he said.

"It's a wake-up call to organizations to say what's your approach – are you going to be able to swap things in and out, have you done an assessment of what automated agentic processes you have, and what's your failover strategy," Willis said.

There's a lot of pressure on CIOs and CTOs to lean in on AI, but they're not ready for many of the issues that are emerging, he said. In this case, if they have outsourced a significant part of their business processes to a frontier model, there are serious complications if that model gets turned off.

This is a very different problem than a data sovereignty one where they already have a migration strategy or replicated data in different places.

"The shift from data sovereignty to capability sovereignty is going to be very important for CIOs because they need business continuity, and now, they've just been introduced to a new way that the business can fail," Willis said.

Companies have been building hybrid model architectures, but more as a cost and control problem rather than addressing capability sovereignty problem, he said. But it's one thing to swap models for commodity tasks; it's a hard case when you have unique capability that you don't have a failover plan for.

"For agentic systems, you need visibility, predictability and control, and this situation suggests the importance of those three pillars in your agentic framework," Willis said. "These models aren't designed to be trusted and now we're seeing that the providers themselves may not have full control over the availability of things."

How to design for AI model resilience

Multinational organizations design their cloud, cybersecurity and supply-chain architectures to withstand geopolitical disruptions, and they must similarly design their AI architecture for resilience rather than uniformity, according to Mann.

This means separating business processes from specific models, adopting multi-model strategies, establishing fallback options and defining governance frameworks that remain consistent even when the underlying models differ, he said.

This should manifest in these organizational dimensions:

• Architecture. Enterprises should implement model abstraction layers that decouple applications and business processes from specific AI providers, standardize APIs and integration patterns across multiple models whenever possible and design around capabilities and business tasks rather than individual vendors or models.
• Operational. Organizations should maintain hot-swappable alternative models for critical use cases, conduct periodic AI failover exercises in the same way they test disaster recovery and business continuity plans. They should also continuously assess geopolitical, regulatory and export-control exposure across their AI vendor ecosystem.
• Governance. AI use cases should be classified according to their business criticality and substitutability, enabling organizations to identify which functions require multi-vendor redundancy and which can tolerate temporary disruption. Legal, compliance, procurement and technology teams must also be closely aligned to ensure that geopolitical risks are reflected in architectural and sourcing decisions.
• Workforce. Employees should be trained on workflows, outcomes and problem-solving approaches rather than on specific models. Organizations should avoid embedding vendor-specific tools, prompts or practices too deeply into critical business processes, but instead develop organizational capabilities that can transfer across model providers when access conditions change.

"Enterprises should not assume that access to frontier AI capabilities will remain constant over time," Mann said. "Just as organizations have learned to design for cyber resilience, cloud resilience and supply-chain resilience, they must now design for AI resilience."

Jim O'Donnell is a news director for TechTarget, where he covers IT strategy and enterprise ESG.