In the early days of critical government networks, critical systems were designed to be air-gapped – physically isolated to prevent any chance of breach. The principle is anything but new: if attackers can’t reach it, they can’t compromise it.
Today, enterprises once again face the same challenge, only this time with AI. Generative models are already at work providing new insights and efficiency. But for organizations to reap their benefits without creating new vulnerabilities, they need to put in place security measures that eliminate the risk of accidentally exposing sensitive data.
For many organizations, a secure Virtual Private Cloud (VPC) provides the perfect balance of robust security and operational flexibility. For others, particularly in highly regulated sectors, the only acceptable solution is a fully offline air-gapped AI deployment. The key is having a platform that meets your specific requirements, whatever they may be.
What Is Air-Gapped AI?
An air gap is a security measure that physically isolates a computer or network from all other networks, in this case, the public internet.
In the context of generative AI security, an air-gapped AI deployment implies that the entire application stack – the on-premise AI platform, the offline LLM, and all the data they process – runs completely inside your organization's own secure perimeter. No prompts, queries, and data ever travel to an external, third-party vendor. For organizations that require it, offline AI, as it is also referred to, is the definitive method for maintaining full physical control over your AI infrastructure.
What are the main drivers for different AI security models?
The main drivers for choosing an AI security model, such as a secure VPC or offline AI, are based on an organization's specific risk and compliance needs. While a secure VPC is the right choice for many, enterprises with the strictest data sovereignty rules, like central banks or defense agencies, often require air gapped AI to meet policies in place to prevent data leakage and ensure total control.
Why Some Enterprises Require Air-Gapped AI
While a secure VPC is a robust and proven solution for most enterprises, the move toward air gapping can be a non-negotiable requirement for a specific set of organizations. The drivers include:
- Preventing All Risk of Sensitive Data Leakage: For some, even the theoretical risk of data exfiltration from a cloud environment is unacceptable. An offline model ensures proprietary data, trade secrets, or national-level intelligence physically cannot leave the building.
- Meeting Strict Compliance Mandates: For industries like central banking, defense, and government intelligence, regulations are absolute. Data sovereignty laws and national security standards often forbid any connection to an external network, making on-premise, air-gapped AI the only compliant path for AI adoption.
- Mitigating Specific Threats: An isolated model has a minimal attack surface for external threats like prompt injection attacks from public actors or API exploits.
- Total Control Over Infrastructure: An on-premise AI platform offers complete governance, granting full control over model versions, updates, and patches, as well as the ability to integrate the AI directly with existing corporate access control systems.
Core Components of a Secure, Air-Gapped AI Stack
For organizations that require this model, deploying a secure, offline AI environment requires a robust architecture:
- Offline LLMs and Platform: This includes private large language models (either open-source models like Llama or private proprietary models) that can run on your own hardware, as well as the on-premise AI platform used to orchestrate them.
- Secure Infrastructure: Data must be protected at all times. This requires strong encrypted storage for data at rest and processing within a secure enclave for data in use.
- Governance and Scanning: The system must integrate with your internal access control systems to manage user permissions. It also requires automated scanning tools that can operate offline to check for dependency vulnerabilities or misconfigurations.
Use Case: When Air-Gap may be Non-Negotiable
Consider a major European central bank that needed to leverage GenAI to analyze vast amounts of sensitive economic reports, policy documents, and confidential internal research.
- The Risk: A cloud-based AI solution – even in a secure VPC – was a non-starter. The risk of data exfiltration involving market-moving financial data or compromising national economic strategy was unthinkable.
- The Solution: The bank deployed a fully air-gapped AI solution with an offline LLM running within their data center.
- The Outcome: Their teams can now use GenAI to query documents and synthesize information in seconds, dramatically boosting productivity. All data is processed on premises, eliminating security risks and ensuring full compliance with national data sovereignty laws.
Choosing Your Secure Deployment: VPC vs. Air-Gapped AI
Ultimately, selecting the right approach requires a careful evaluation of an organization’s specific regulatory, risk, and operational needs. In most cases, a secure Virtual Private Cloud (VPC) will meet even the stringent security requirements. In others, a fully air-gapped deployment may be considered non-negotiable.
|
Deployment Model |
Virtual Private Cloud (VPC) |
Fully Air-Gapped (On-Premise) |
|
What It Is |
A logically isolated, single-tenant section of a public cloud (like AWS, Azure, or GCP). |
A physically isolated environment in your own data center. No internet connection. |
|
Best For |
High security and compliance: most financial services, healthcare, and legal firms. |
Maximum security needs: central banks, defense, intelligence, critical R&D. |
|
Key Benefit |
A balance of security and scalability. Leverages cloud flexibility in a secure "walled garden." |
Absolute data control. Immune to external network-based attacks. |
|
Consideration |
Still relies on a third-party cloud provider, though data is not publicly exposed. |
Higher operational overhead. Updates and patches must be applied manually. |
The Role of RAG in Any Secure Deployment
Securing the platform is only half the battle. For maximum security, you also need to secure the data it accesses. This is where privacy-enabled retrieval augmented generation (RAG) becomes critical, regardless of your deployment model. RAG connects your LLM to your organization's own verified knowledge bases. This setup is fundamental to enterprise AI security for two reasons:
- It ensures trustworthy answers: The AI generates responses based only on your internal, verified documents.
- It enforces access control: A platform that uses RAG can apply your existing access control systems to the AI. The AI will only find and use data that the specific user is already authorized to see, providing secure knowledge management by default.
How Squirro Delivers Flexible and Secure AI Deployments
At Squirro, we understand that enterprise security is not one-size-fits-all. Our platform is infrastructure-agnostic and designed to meet you exactly where your security needs are.
We have a proven track record of deploying our platform in both secure Virtual Private Clouds (VPCs) and fully air-gapped AI environments.
Whether you choose a VPC for its balance of scalability and security, or a fully isolated on-premise AI platform for maximum control, our solution adapts, acting as the secure integration layer. While we do not provide the LLM itself, our platform allows you to:
- Deploy Your Choice of LLM: You can run any models on your own on-premise hardware or within your secure VPC.
- Connect Locally: Squirro connects to this local LLM endpoint via your internal network.
- Ensure Full Isolation: The entire workflow, from the RAG, your document ingestion, your access controls, and the LLM's inference, happens within your secure perimeter.
Either way, no data ever needs to leave your trusted environment. You get the full power of generative AI combined with the absolute security of an architecture you control.
Secure AI is a Strategic Choice
Choosing your AI deployment model is a core business strategy. For many, a secure VPC provides the robust data protection and flexibility needed to innovate. For others, the non-negotiable requirements of their industry demand a fully air-gapped AI solution.
The key is not to force one model, but to have a partner and a platform that can deliver the full power of generative AI, securely, within the architecture you require. This approach enables you to innovate responsibly, knowing your data is protected according to your specific business, regulatory, and security standards.
Take the Next Step
Ready to dive deeper into securing your GenAI initiatives? Download our comprehensive white paper, 'Data Protection for Enterprise GenAI: A Practical Guide,' for an in-depth exploration of strategies to safeguard customer data, ensure compliance, and build trust in the AI era.
