Sovereign AI Risk Assessment: 10 Questions CISOs Must Answer 

December 9, 2025

Collaboration

A sovereign AI risk assessment is the foundation of secure AI adoption for defense and intelligence organizations. Unlike generic AI governance frameworks, a sovereign AI security approach evaluates whether your organization maintains complete control over AI infrastructure, data flows, and operational independence—critical factors when handling classified or sensitive information. This CISO risk assessment framework provides 10 essential questions that separate organizations with genuine sovereign AI capabilities from those with vendor-dependent deployments that merely claim security. 

Before conducting any sovereign AI risk assessment, understand this: the top data exfiltration channel in the enterprise isn’t phishing or misconfigured cloud storage buckets. It’s the AI tools that your employees are using right now, often through personal accounts your security stack can’t even see.[1] Worse yet, the data loss prevention controls enterprises rely on to protect themselves from data loss aren’t protecting against this, as 77% of sensitive data pasted into generative AI tools reportedly bypasses enterprise controls entirely.[2] 

Balancing AI Demand with Security Needs

The pressure to adopt AI is relentless, as operations teams need increased automation to keep up with ever-increasing board demands for efficiency gains. Unfortunately, somewhere in the middle, CISOs are caught between enabling innovation and preventing the next catastrophic data breach. According to Team8’s 2025 CISO Village Survey, AI risk has become the top security priority, outpacing vulnerability management, data loss prevention, and third-party risk.[3] That’s a fundamental reordering of priorities. 

So how do you get ahead of this? A sovereign AI risk assessment starts by asking the questions your AI vendors may not want you to ask, but that expose the gaps between their marketing promises and your operational reality — particularly around data sovereignty and infrastructure control.  

Ten Questions That Separate AI Governance Theater from Real Control 

1. Where does your data go when employees use AI tools? 

Beyond just reading the terms of service, this is about understanding data flows. A 2024 Ponemon Institute survey found that 52% of IT leaders didn’t know whether their collaboration vendors used customer data to train AI models.[4] The inability to answer this question definitively for every AI tool in your environment, you have a governance gap. 

2. How do you maintain operations when your AI provider goes dark? 

Sovereign AI architecture prioritizes operational resilience. When your mission-critical workflows depend on external APIs, you’ve created a single point of failure. The question isn’t whether there will be outages, because those are inevitable. The question is whether you have battle-tested plans for keeping operational when they happen. 

3. What visibility do you have into AI usage across your organization? 

Shadow AI is the new shadow IT, except the data exposure is exponential. Recent reporting indicates that 71.6% of generative AI access happens through non-corporate accounts.[5] This isn’t from malicious employees. Rather, this is due to employees feeling forced to use tools you haven’t sanctioned so they can meet increasingly unreasonable productivity goals. But that distinction regarding motive doesn’t matter when your sensitive data ends up in a Large Language Model (LLM) training dataset. 

4. How do you classify AI systems by risk level? 

The EU AI Act is forcing this conversation globally, as AI systems are categorized from minimal to high-risk, with corresponding compliance requirements including pre-market conformity assessments, technical documentation, and ongoing monitoring.[6] Even if you’re not directly subject to EU jurisdiction, these frameworks are becoming the global standard that all organizations need to get familiar with now so they won’t find themselves scrambling later. 

5. Who owns AI risk in your organization? 

The Nation Institute of Standards and Technology (NIST) AI Risk Management Framework makes it clear that AI risk management should integrate into broader enterprise risk strategies.[7] Yet, AI governance often still falls in-between organizations, as IT thinks security owns it….security thinks legal owns it…and legal thinks IT owns it. Organizations need to have documented clarity in this area immediately. 

6. What happens to prompts and outputs after they’re processed? 

AI risk assessment frameworks must address data retention, but data retention policies for AI systems are often vague or nonexistent. Once data enters a model’s training pipeline, retrieval or deletion becomes nearly impossible. This creates regulatory exposure under GDPR, HIPAA, and emerging AI-specific regulations. 

7. How do you assess AI vendors as part of your supply chain? 

Early 2025 research shows that 91% of CISOs report an increase in third-party cybersecurity incidents, yet only 3% have full visibility into their entire supply chain.[8] While not specifically mentioned in that research, AI vendors introduce new dimensions of risk – including model provenance, training data integrity, and inference logging – that almost certainly make this third-party risk challenge even greater than reported because traditional vendor assessments weren’t designed for this technology. 

8. Can you audit AI decisions that affect your operations? 

When AI drives incident response, procurement decisions, or customer interactions, you need audit trails. Not just for compliance, but for understanding when things go wrong. If you can’t explain how an AI-assisted decision was made, you can’t defend it. 

9. What’s your remediation path when AI models behave unexpectedly? 

AI systems can change behavior over time due to shifts in their operating environment or data flows. Cobalt’s research on LLM applications shows they have the highest proportion of serious vulnerabilities at 32%, with the lowest remediation rate at 21%, often because fixes depend on third-party providers.[9] How are you going to combat these risks…or at least challenge your AI providers to prove they are mitigating them? 

10. Does your infrastructure support sovereign AI deployment? 

This is the ultimate sovereign AI risk assessment question. Organizations with private AI infrastructure experience 76% fewer data exposure incidents compared to those relying solely on public services. But sovereign AI deployment isn’t just about compliance—it’s about maintaining control over your most sensitive operations. Organizations with private AI implementations experience 76% fewer data exposure incidents compared to those relying solely on public services.[10] Sovereign AI isn’t just about compliance, it’s about maintaining control over your most sensitive operations. Can you run AI workloads in air-gapped environments?…on infrastructure you control?…with models you can audit? You are going to want to be able to answer “yes” to all of these questions. 

Putting Your AI Risk Assessment in Motion: Moving from Questions to Action 

The common thread across all of these questions is control. Not to block innovation, but to understand exactly where your data goes, who can access it, and what happens when things go wrong. That’s the difference between AI governance theater and actual risk management. 

For organizations handling sensitive operations – including defense, intelligence, and critical infrastructure – this is a matter of national security. The EU AI Act’s August 2025 deadlines for general-purpose AI compliance are already here, with high-risk system requirements following in 2026. Fines can reach €35 million or 7% of global turnover.[11] But frankly, the regulatory penalties are the least of your concerns, particularly for U.S.-based organizations that are not (yet?) bound by similar legislation. But the risk of losing control of sensitive data to training pipelines you’ll never audit or building mission-critical dependencies on infrastructure you don’t own, is very real. 

Organizations that treat AI infrastructure the same way they treat any other critical system – with full visibility, clear ownership, and the ability to operate independently when external dependencies fail – are in a much better position to understand and mitigate these growing risks. They’re asking these sovereign AI risk assessment questions not as a compliance exercise, but as the foundation for AI strategies that actually work under pressure. Because at the end of the day, the question isn’t whether your organization will adopt AI. It’s whether you’ll maintain control when you do. 

Sources

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A.J. Nash is an intelligence strategist and public speaker focused on building intelligence-driven security programs. Applying his 19+ years of experience in the U.S. Intelligence Community, A.J. is often asked to contribute to traditional and social media discussions on intelligence, security and leadership as well as being invited as a keynote speaker at conferences worldwide. AJ is the host of the podcast Unspoken Security.

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