AI Factory

Simply put, AI factory are environments designed specifically to train, deploy and operate AI models at scale. AI factory is different from traditional data centers in that they’re optimized for continuous AI workloads by combining accelerated compute, data pipelines, orchestration and security all in a unified system. 

Unlike general-purpose infrastructure, AI factory are built for AI as the primary output, not just one workload among many others. Once organizations are ready to move beyond experimental AI, this gives them production-grade systems that run reliably and continuously. 

Fortanix Confidential AI provides the security layer for the AI factory by protecting data and models while they’re actively in use. It enables confidential AI workflows backed by hardware isolation and cryptographic controls, allowing organizations to securely run sensitive AI workloads. 

This is especially important in an AI factory where data and model weights must be decrypted during execution. Confidential AI keeps sensitive assets protected even from infrastructure-level access. 

Training and inference pipelines are typically separated to optimize performance and cost. While training tends to focus on large-scale, batch compute, inference should be optimized for low-latency and continuous execution. 

This separation allows you to scale each phase independently, ensuring you're using accelerated (and costly) compute efficiently whiles till meeting real-time application requirements. 

Not all AI factory require confidential computing, but it’s essential when models work with sensitive, regulated or proprietary data. Confidential computing ensures that the data and the models themselves are protected even during processing, not just when the data is at rest or in transit. 

Without confidential computing, sensitive data is exposed in memory during execution. For businesses operating in regulated industries or looking to preserve data sovereignty this level of protection is a must 

AI factory support everything from data preparation and model training and tuning, to inference, model evaluation and monitoring. They can also host other supporting services for things such as MLOps, observability and governance. 

By placing these workloads in a single location, AI factory reduce data movement and the friction that can arise with more distributed architecture. This helps teams move faster from experimentation to production. 

No. Large language models are certainly a common use case, but AI factory can also support things like computer vision, recommendation systems, predictive analytics and domain-specific models for various industries. 

Many organizations are using AI factory to run multiple AI workloads simultaneously. The setup allows them to support different business units on a shared, optimized infrastructure and platform. 

AI factory provide the infrastructure needed to train, fine-tune, and run GenAI models at scale. This helps organizations operate GenAI continuously while maintaining performance, governance and security controls. 

They’re particularly valuable for GenAI use cases that require repeated access to large datasets. AI factory are also ideal for frequent model updates without disrupting production. 

The main industries currently benefiting most include government, financial services, healthcare, telecommunications, manufacturing and research. Organizations in these sectors are often looking for scalable AI performance along with strict data governance and compliance. 

These industries have a few things in common: they tend to manage sensitive data, and they face regulatory oversight. AI factory allow them to adopt AI without compromising their industry’s compliance requirements. 

AI factory protect proprietary models through isolation, encryption and controlled access to model weights. Techniques such as confidential computing help ensure that models can’t be inspected, copied or tampered with, even during execution. 

This protection will only become more critical as models increasingly represent valuable intellectual property. It also helps prevent insider threats and model exfiltration. 

In traditional environments, cloud operators might have privileged access to infrastructure. But the beauty of AI factory using confidential computing is that data and models can be cryptographically isolated so that even infrastructure operators cannot access them. 

This allows organizations to use cloud-based AI factory without fully trusting the underlying platform. It also supports stricter compliance and sovereignty requirements. 

Confidential computing is the technology that enables AI workloads to run within hardware-enforced, trusted execution environments—literally a physical component on modern CPUs and GPUs. This allows data and models to remain encrypted and protected as they are processed within an AI factory. 

Confidential computing moves security closer to the workload itself, which reduces the dependence on network-based or perimeter security controls. 

Encryption keys are typically managed with a centralized key management system and strict policy controls. In secure AI factory, keys are released only for verified workloads, typically via cryptographic attestation. 

All of this is a technical way to say that your keys are never exposed unnecessarily. A sound key management strategy helps enforce separation of duties between infrastructure and AI workloads. 

AI factory can be deployed on-premises, in the cloud or across hybrid environments. The choice depends on the organization's specific needs when it comes to performance, data sensitivity and regulatory requirements. 

Many organizations take on  a phased approach, starting and testing in one environment and expanding as AI usage grows. Deployment flexibility is a key advantage that modern AI factory design provides. 

Yes. Many organizations today deploy AI factory across hybrid environments so they can combine on-prem or sovereign infrastructure with cloud-based resources, all while maintaining consistent security and governance. 

For many organizations, this is the best of both worlds: hybrid deployments allow them to balance performance, cost and compliance while making it easier to integrate AI with existing systems. 

A sovereign AI factory is an environment in which data, models and workloads remain under the control of a specific organization or nation. The idea is to enforce data residency, governance and legal jurisdiction requirements. 

Sovereign AI factory are commonly used where national laws or regulations restrict how data can be processed. They're also helpful in reducing the dependency on foreign infrastructure. 

Governments use AI factory to support national AI initiatives, public services, defense, healthcare and research. These environments are attractive to governments because they allow them to adopt and roll out AI while maintaining control over sensitive national data. 

They also enable secure collaboration across agencies and nations, which can ultimately help governments modernize services without increasing security risk. 

Yes. AI factory can be designed to ensure that data and models never leave specific geographic or legal boundaries, meaning organizations can meet data residency and sovereignty regulations. 

Crucially, this includes controlling where data is processed, not just where it’s stored. AI factory can also support full auditing and compliance reporting. 

As AI production ramps up, enterprises need infrastructure that delivers predictable performance, scalability and top-class governance. AI factory are designed to soften the infrastructure burden associated with adoption and support long-term AI strategies. 

They also help organizations reduce the complexity that can occur as AI usage grows. For many enterprises, AI factory make large-scale AI low-risk and sustainable. 

AI factory aren’t a “must” for every use case, but they’re becoming a key component for organizations that run AI at scale. As AI becomes a core business operation, infrastructure that’s purpose-built to handle it will become increasingly important. 

As it stands today, many enterprises are using AI factory alongside etheir xisting platforms. This hybrid approach supports both innovation and operational stability. 

AI factory enable organizations to iterate on models, lower operational friction and achieve more reliable AI performance. Over time, this translates into better decision-making, automation and competitive advantages. 

They also shorten the path from model development to production that’s impactful, which is another way of saying they help organizations realize ROI from their AI investments faster. 

Not necessarily, but it’s important to understand that AI factory and ML platforms serve different purposes. ML platforms are all about tools and workflows, while AI factory serve as the underlying infrastructure to reliably operate AI at scale. 

In many cases, machine learning (ML) platforms run on top of AI factory. Together, they form a complete AI stack. 

Since AI factory centralize and optimize AI workloads, they reduce inefficiencies, improve the utilization of accelerated compute, and, ultimately, lower the cost of AI output over time compared to stitched-together infrastructure. 

This can make advanced AI use cases economically viable, especially when they start to scale; better utilization reduces wasted compute resources. 

Yes, but only when implemented correctly. AI factory are meant to improve consistency, performance and reliability, which directly impacts model quality and business results. 

Stable infrastructure reduces the noise that can affect model behavior. This leads to more predictable and trustworthy AI systems. 

Not really. While they may physically resemble data centers, AI factory are architected specifically for AI workloads, with different assumptions about compute, data flow and security. 

Their design prioritizes AI throughput and protection rather than general IT flexibility. So, they’re more than just “rebranded”; they’re fully re-architected. 

Organizations running continuous, large-scale or sensitive workloads benefit most from AI factory, which include enterprises, governments and research institutions where AI is mission-critical. 

Smaller teams or early-stage AI projects may not require this level of specialization... yet. But they may eventually, as AI factory are best suited for mature AI programs. 

Risks include the centralization of sensitive data, new and expanded attack surfaces and increased operational complexity. But these risks can be mitigated with strong governance, isolation and security controls. 

Baking security into the architecture from the beginning is essential. Ongoing monitoring and policy enforcement are also critical. 

Building a sovereign AI factory means selecting trusted infrastructure, enforcing data residency, implementing strong encryption and access controls, and using tech like confidential computing to protect data while it’s in use. 

Governance, legal frameworks and operational processes are just as important as the supporting technology. Sovereign AI factory require cross-functional planning. 

Not at all. A supercomputer focuses on raw compute performance, while an AI factory includes orchestration, data pipelines, security, governance and the tooling needed for production-grade AI. 

AI factory are designed to operate AI systems over time, not just run benchmark workloads. Higher computing power may be ideal, but supercomputers and AI factory are completely different things. 

Core components include accelerated compute, high-speed networking, data pipelines, AI platforms, observability tools and security layers such as encryption, key management and confidential computing. 

Together, these components create a structured, repeatable environment for AI at scale. This makes AI factory easier to govern and secure. 

AI data centers are facilities designed specifically to support artificial intelligence workloads at scale. They’re unlike traditional data centers in that they’re optimized for high-performance computing, large datasets, and continuous AI processing.  

They typically include specialized hardware, high-speed networking, and software platforms built for training and running AI models. Many organizations also refer to these environments as AI factories because their primary output is intelligence, not just application hosting. 

AI data centers are used to train, fine-tune, and run AI models in production. They’re designed to handle data ingestion, large-scale computation, and real-time inference for generative AI, computer vision and predictive analytics, among other use cases.  

The environment supports continuous AI workflows rather than occasional batch jobs, with the goal of reliably producing AI-driven insights and decisions as part of everyday operations. 

Securing AI workloads is different than traditional network and perimeter controls. Because AI models and data must be decrypted during processing, your security must also protect the data while it’s in use.  

Technologies like confidential computing help isolate workloads and protect sensitive data and models during execution. Strong access controls, encryption, and runtime verification are also key components of a modern AI data center security strategy. 

The idea is to reduce reliance on trusting infrastructure operators or shared environments. It also helps limit the impact of inevitable breaches by protecting data even during active processing. 

AI data centers rely heavily on accelerated hardware such as GPUs and specialized AI accelerators. They also use high-performance CPUs, fast memory, and high-speed networking to efficiently move large volumes of data, along with storage systems to support massive datasets and frequent access patterns. The stack is purpose-built to support parallel processing and sustained AI workloads. 

Power delivery and cooling systems are also critical, since AI hardware consumes significantly more energy than traditional servers. Many facilities are designed specifically to handle these demands. 

Since AI data centers consolidate highly valuable assets together in one location, including sensitive data and proprietary models, they’re an attractive target for attackers. This concentration also increases the impact of a breach if and when one takes place.  

Common risks include unauthorized access to data, theft of model weights and misuse of compute resources. And because AI workloads run continuously, security gaps can be exploited for long periods of time if they’re not properly detected and controlled. 

There’s also risk from insiders and misconfigurations, not just external attackers. Strong governance and monitoring are needed to reduce these threats. 

In many organizations, AI systems access data and make decisions without being governed in the same way as traditional user identities or applications. This can create a “shadow identity” problem where models and pipelines have broad access to potentially sensitive assets, but with limited oversight. As AI systems grow more autonomous, a lack of clear governance creates security and compliance risks.  

With this case, treating AI workloads as first-class identities helps improve accountability and data control. This means applying identity, access, and audit principles to AI systems, not just people. It also helps organizations understand and limit what AI agents and systems are allowed to access. 

Regulators worldwide are increasingly issuing rules on data privacy, AI use and cross-border data handling, and many enterprises are still adapting their infrastructure and security practices to meet these requirements.  

This ultimately creates gaps when compliance expectations outpace technical readiness. It’s why AI data centers must be designed with governance, auditability, and data protection in mind. 

Organizations that wait to retrofit compliance later often face higher costs and more damaging disruptions. Building compliance into AI infrastructure from the start is becoming the best practice that can save millions of dollars over time. 

Not all traditional data centers are well-suited for AI workloads. AI systems need far more power, cooling and specialized hardware than typical enterprise applications, and retrofitting older facilities can be expensive and may still fall short of performance needs. That’s why many organizations are building or upgrading facilities specifically to support AI, rather than simply reusing existing infrastructure. 

In some cases, only parts of a facility can be adapted, while others must be replaced or redesigned. Capacity planning is much more critical for AI environments.

Data typically travels from a user device or application to the AI data center over secure network connections. Once it’s in the data center, it’s processed by AI models that analyze it and generate results.  

Those results are then sent back to the originating system or user in near-real time. Along the way, security protects the data in transit, at rest and increasingly while it’s being processed. 

Latency, bandwidth and reliability all play important roles in how responsive AI-powered applications feel to end users. As AI adoption and usage grow, optimizing the flow of data is increasingly important.

An AI token factory is a purpose-built system that transforms GPU compute capacity into governed, consumable AI services, delivered and priced at the unit of the token. 

To understand what that means, it helps to fully understand what a token is. When an AI model processes a request, it doesn't read text the way a human does. It breaks input and output into discrete units called tokens, which can represent words, parts of words, punctuation, or symbols. The model reads tokens in and produces tokens out, and the computational cost scales with token volume. 

An AI token factory is the system that makes all this work at scale. It’s designed to produce large volumes of AI output (tokens), control how that output is used, and keep track of who’s using what. 

Behind the scenes, it brings together several key pieces. There are powerful groups of GPUs that handle heavy lifting, software that decides how and when AI models run, and simple interfaces (like APIs) that let people or applications use those models. It also includes tools that measure how much AI work each user consumes and systems that translate that usage into costs, so everything can be tracked, managed and billed accurately. 

A traditional data center is designed around infrastructure availability. You provision compute, storage, and networking, and you make it accessible to applications. The unit of sale is capacity, things like CPU cores, RAM, storage or bandwidth, and customers pay for resources whether they’re actively doing useful work or not. 

In an AI token factory, what matters most is how much useful work the hardware produces. The work is measured in tokens, which are the units behind every AI response. You’re not paying to reserve space on a GPU; you’re paying for the actual AI output. 

To make that possible, these systems need capabilities that traditional data centers weren’t made for. They have to automatically scale up or down based on demand, so resources aren’t sitting idle. They need to safely support multiple users or organizations on the same hardware without mixing their data. They also need accurate tracking, so every bit of AI usage is tied to the right user, along with rules that control who can access which models and when. 

In short, an AI token factory is built from the ground up to manage, measure, and deliver AI work efficiently, which is something old-school data center designs weren’t built to handle.

Cloud AI services from major hyperscalers also deliver AI capabilities on a usage- or token-based basis, but the differences come down to three things: data residency, model control, and infrastructure ownership. 

When an enterprise sends a query to a public cloud AI service, that data leaves the enterprise environment and is processed on infrastructure the organization doesn't control. For organizations operating under strict data sovereignty requirements (think healthcare providers, financial institutions, government agencies, defense contractors, and so on), this often isn't a viable option. An AI token factory, by contrast, is typically operated on-premises or within a sovereign environment, so the data never leaves the controlled perimeter. 

Another big difference comes down to control over the AI models themselves. With most public cloud AI services, companies don’t really get to see or manage how the models are running. But in an AI token factory, it’s the opposite. The operator decides which models are used when they run, and who can access them. That level of control is especially important for organizations that need clear audit trails, strict compliance, or the ability to run their own proprietary models on sensitive data. 

The last piece is ownership of the infrastructure. Public cloud AI runs on someone else’s systems, which means limited control. An AI token factory, on the other hand, can be run directly by the company, a local cloud provider, or a regional partner. That gives the operator full responsibility (and control) over the hardware, which is often necessary to meet regulatory and data protection requirements that public cloud services can’t always handle. 

One problem an AI token factory solves is the trust gap that blocks enterprise AI adoption at scale. 

Most organizations have data they can’t afford to expose. Healthcare providers operate under patient privacy laws, financial institutions under strict regulatory frameworks, and governments under data sovereignty mandates. At the same time, the most capable AI models are proprietary systems developed by AI labs that have every reason to protect their intellectual property. If those models are deployed on infrastructure, they don't control; their model weights, architecture, and fine-tuned knowledge could be extracted by anyone with sufficient system access. 

This creates a paradox where enterprises want AI models to run where their data lives, and model owners need assurance that their IP can't be stolen. An AI token factory with the right security foundations resolves that paradox by giving enterprises the on-premises or sovereign AI deployment they need while giving model owners the cryptographic guarantees of IP protection they require to deploy in the first place. 

The other major problem that AI token factories solve is the inefficiency of traditional compute-consumption models. Operators continuously optimize the token throughput per watt, while enterprises get predictable, auditable AI spend that maps to actual usage rather than reserved capacity. 

AI token factories are built and operated by several distinct categories of organizations. 

Large enterprises are building internal AI factories to serve their own business units, running inference on sensitive internal data under full organizational control and without reliance on public cloud services. These are often organizations in healthcare, finance, or defense, where data sovereignty is a non-negotiable requirement. 

Sovereign cloud providers are also deploying AI factories as a core component of their infrastructure. These operators serve governments, regulated industries, and national enterprises that must process data domestically. 

A newer and fast-growing category is the neocloud, purpose-built AI infrastructure operators who build AI factories as a commercial service, transforming GPU capacity into revenue-generating AI services sold on a token basis to enterprise customers. Neoclouds are the operators most directly aligned with the commercial token factory model.

The AI factory business model centers on selling AI output, specifically, token-metered AI services. Instead of selling GPU hours, operators sell access to AI inference priced at token units. 

In practice, this opens up a range of business models. Companies can offer prepaid token packages, pay-as-you-go usage, limits for different teams or customers, and internal billing systems that track who used what. The operator handles everything behind the scenes, including running the infrastructure, managing the AI models and controlling access, so customers can simply use the AI without worrying about the hardware underneath. 

The big advantage is that costs line up directly with value. Customers only pay for the AI work they actually use instead of paying for unused capacity. At the same time, operators are motivated to get as much useful output as possible from the power they consume, which pushes them to keep improving efficiency. 

As AI services expand into different tiers (from basic to high-end), those offering faster performance and more advanced capabilities can charge higher prices per token. With this being the case, operators that can support this full range of services can generate much more revenue from the same infrastructure by simply delivering more value with it. 

Traditional factories take raw materials and convert them into finished goods through repeatable processes. An AI token factory basically does the same, taking GPU capacity as the raw input and converting it into AI services as the output. 

That said, a warehouse full of GPUs isn’t a factory any more than a warehouse full of wood is a furniture manufacturer. What makes an AI token factory a factory is the operational layer that surrounds the compute: metering, access controls, multi-tenancy, billing, policy enforcement, and service delivery. 

The factory analogy also captures the system's production-grade nature. Like a factory, an AI token factory is optimized for throughput, efficiency and quality. The goal is to convert GPU capacity into the maximum volume of useful AI work, at the lowest cost per unit, under the governance and security controls that enterprise customers require. 

In the context of AI and large language models, tokenization is the process of breaking text into discrete units (tokens) that the model can process. A token might represent a whole word, part of a word, punctuation or a special character. The exact mapping depends on the tokenizer used by a given model, but the general rule of thumb is that one token corresponds to about three to four characters of English text. 

Tokenization matters for AI infrastructure because it's how computational cost is measured. Every query a model processes and every response it generates are token-generation events. The model reads input tokens and produces output tokens, and the cost of that operation scales with token count. Tokens have become the standard unit of pricing for AI services because they represent the actual work performed, making them a more meaningful measure of value than raw compute time. 

In an AI token factory, the entire system is built around token generation as the primary output metric, optimizing for the number of tokens produced per watt of power, per dollar of infrastructure, and per unit of time. 

In an AI token factory, pricing is usually based on how much AI work you use, and it’s often organized in tiers. At the simplest level, you’re charged per million tokens. There’s also a difference between input tokens (what you send to the AI) and output tokens (what the AI generates in response), since producing answers typically requires more computing power and costs more. 

Pricing also varies depending on the type of AI service you’re using. Simpler models that handle basic tasks are cheaper per token, while more advanced models, such as those used for complex reasoning, legal analysis or medical insights, cost more per token. Faster, premium services that deliver quick responses or handle larger amounts of context at once also come at a higher price. 

On top of that, providers often package these services in flexible ways. You might get discounts for buying tokens in bulk, reserve a certain level of capacity for consistent performance, or set usage limits for different teams within your organization. 

If you’re evaluating costs, the most important thing to look at is the price per token for the level of service you actually need, along with how that price changes as usage grows or as you move to more advanced AI models. 

AI token factories are designed to support a broad range of inference workloads. The most common include natural language processing tasks such as text generation, summarization, classification, and question answering; retrieval-augmented generation (RAG), where a model draws on an organization's private knowledge base to produce more accurate responses; and code generation and analysis, which is increasingly running at scale inside enterprise development environments. 

Agentic AI is a whole other animal. AI agents take on multi-step workflows, call out to and utilize external tools, and reason across complex sequences. That’s much different (and more labor-intensive) than handling a single query. Not surprisingly, these agents and their workloads consume significantly more tokens per task than typical inference, and there’s a higher premium on throughput and latency. 

For enterprises in regulated industries, AI token factory architecture is made for high-sensitivity workloads: things like analyzing patient records for clinical decision support, processing financial data for fraud detection, evaluating legal contracts against proprietary policy frameworks, or running competitive intelligence workloads on data that you don’t want leaving your organization's controlled environment. These are the use cases where on-premises or sovereign AI token factories, built on a foundation of Confidential Computing, deliver capabilities that public cloud services can’t match. 

Shared AI infrastructure introduces risks that don't exist when you're running workloads in a fully isolated, single-tenant environment. The most significant is the potential for data exposure across tenant boundaries. If isolation between tenants is enforced only through software controls, an attacker with privileged access to the infrastructure could, in theory, observe another tenant's data during processing. Traditional virtualization and containerization reduce this risk but don't eliminate it, because they still leave data exposed in memory during active computation. 

Agentic AI has amplified these risks because it executes multi-step workflows, accesses external tools and data sources, and operates continuously over extended periods. Each part of that workflow is a potential point of exposure. An agentic system operating in a shared environment with overly permissive access controls could inadvertently (or through manipulation) interact with data or systems belonging to other tenants. 

AI token factories built on Confidential Computing are much more reliable than those that rely solely on network segmentation and access management. When evaluating a shared AI token factory, understanding the isolation guarantees that are in place is the right starting point. 

In most AI deployments today, data is protected at rest (encrypted on disk) and in transit (encrypted as it moves across networks). But during inference, when the model is actively processing input data and generating a response, that data is traditionally decrypted and exposed in system memory. This is known as the "data in use" gap, and it's where AI workloads are most vulnerable. 

Secure AI inference addresses this with Trusted Execution Environments (TEEs), hardware-enforced, isolated regions of memory within a processor where computation takes place with strong confidentiality guarantees. Inside a TEE, data remains encrypted in memory even while it's being actively processed. The host operating system, the hypervisor, and even administrators can’t read what's happening inside the enclave. 

For AI workloads specifically, this extends across the full inference pipeline: the input data the model receives, the intermediate computations it performs, the model weights themselves, and the outputs it generates. All of it stays within the hardware-enforced

In a conventional multi-tenant AI environment, the answer is: not easily, but it’s also not impossible. Standard isolation controls like virtualization, containerization, and network segmentation make casual cross-tenant data access unlikely.  

That said, they don't make it cryptographically impossible. A compromised hypervisor, an insider with system access, or a sophisticated side-channel attack could potentially reach tenant boundaries in environments that rely solely on software-based isolation. 

In a Confidential Computing-based AI token factory, the answer is much different. Hardware-enforced TEEs provide cryptographic isolation between tenant workloads. Each tenant's data, model weights and inference computations are isolated at the silicon level, and the isolation guarantee is backed by the hardware, not by policy. 

This is a gamechanger for organizations handling regulated data, proprietary models, or any information whose exposure would carry legal, financial or competitive consequences. 

This is one of the most important security questions to ask when evaluating any AI infrastructure provider, and the answer depends entirely on the platform's underlying security architecture. 

In a traditional AI infrastructure environment, a compromised operator is a serious breach. An attacker who obtains administrative access to the host systems can potentially read data in memory, extract model weights, intercept inference inputs and outputs, and access everything the operator's systems can access. The operator's administrative privileges give visibility into the workloads running on the infrastructure. 

In a confidential AI environment built on hardware-enforced TEEs, an attacker who gains access to the infrastructure still won’t be able to view the contents of protected enclaves. Data in use remains encrypted, and model weights are inaccessible.  

This is zero-trust infrastructure at its finest. It means you don’t have to trust the operator; the hardware enforces the boundaries regardless of who controls the software layer above it.

Public cloud AI services and AI token factories can both implement strong security controls, but they differ in who controls the infrastructure and what that means for data residency and trust. 

When you use a public cloud AI service, your data is processed on infrastructure owned and operated by the cloud provider. Even with strong encryption and access controls in place, the cloud provider retains administrative access to the underlying hardware. For organizations subject to data sovereignty requirements that require data to remain within specific geographic or organizational boundaries, or for those whose compliance frameworks prohibit data processing on third-party infrastructure, this is often a non-starter, regardless of the security certifications the provider holds. 

For heavily regulated industries or sovereign deployments, on-premises AI token factories with Confidential Computing are a practical choice when cloud AI services don’t cut it. 

The honest answer: it depends on how the token factory is built. 

A token factory that relies on conventional infrastructure security can provide meaningful protections, but asks you to trust the operator. You're accepting that the operator's security practices are sound, that their administrators won't misuse their access, and that their systems won't be compromised in ways that expose your data. For many use cases, it’s a reasonable and affordable position. For sensitive regulated data, proprietary AI models, or anything with serious legal or competitive consequences, it’s not enough. 

A token factory that utilizes Confidential Computing ensures that data remains secure, even during processing. It effectively protects your information from both operators and potential hackers. With remote attestation, you can independently verify that workloads are operating within genuine, tamper-proof secure enclaves.  

Finally, attestation-gated key release ensures that encryption keys are only given to environments that successfully pass the verification check. 

Put it all together, and these features replace blind trust in an organization with hard, checkable evidence. 

When you're sizing up an AI token factory for handling sensitive data, the smart question is: "Can I independently verify the security setup without just taking their word for it?"

Verifying that an AI token factory is running the expected code is exactly what cryptographic proof of execution through attestation is made for. 

When a workload is loaded into a TEE, the hardware computes a measurement of the code before it begins executing. This measurement is a cryptographic hash of the workload's binary and configuration, included in the attestation report signed by the hardware. A relying party that knows what code is supposed to be running compares this measurement against the expected value. If there’s a match, it means there is cryptographic proof that the exact expected code is running, unmodified, inside a genuine hardware-isolated environment. But sometimes they don't match, maybe because the code has been altered, a different version was loaded, or some unexpected component was introduced. In this case, the measurement won't match and attestation fails. 

In a Fortanix CCM-managed environment, this verification happens before any encryption keys are released and before the workload processes any sensitive data. The attestation report and workload measurement are logged and can be retained for later auditing, giving compliance engineers a verifiable, time-stamped record of exactly what code was running when the time-sensitive data was processed.

Comprehensive confidential AI audit logging requires that any event that triggers attestation, including the evidence generated at workload start, during runtime monitoring, and at workload termination, be captured, signed and stored in a way that makes them tamper-evident and verifiable. 

In an environment managed by Fortanix CCM, each attestation event generates a signed log entry that captures the attestation report, the workload measurement, the timestamp, the identity of the requesting environment, and the policy decisions made. Because these logs are signed at the point of generation, they can’t be modified later; any tampering with the log would break the signature and be detectable. 

This gives compliance engineers something much different from conventional audit logs. A standard log records that an action occurred, while an attestation-based audit log contains proof of which hardware was running, which software was loaded on that hardware, which policies were applied, and which keys were released at the time of the event. This is the kind of trail that eases the auditing process, satisfying regulators who want you to prove what actually happened during processing. 

Fortanix CCM also supports forwarding signed attestation logs to a SIEM for integration into existing security operations workflows, contributing to SecOps teams’ established view of attestation posture alongside conventional security telemetry. 

GDPR doesn’t prohibit shared infrastructure, but certain obligations make the choice of infrastructure architecture legally consequential. 

The core GDPR requirements relevant to AI inference are data minimization, purpose limitation, storage limitation, and the obligation to implement appropriate technical and organizational measures to protect personal data. When personal data is processed during AI inference, the controller is responsible for ensuring that the processing is lawful and that the data is protected throughout the operation, including during active computation. 

The specific challenge for GDPR-compliant AI models running on shared infrastructure is the concept of "appropriate technical measures." GDPR's Article 32 requires measures that ensure a level of security appropriate to the risk, including (as appropriate) the pseudonymization and encryption of personal data [source]. In a shared infrastructure environment, encryption at rest and in transit is well understood. Encryption in use during inference is not standard in conventional shared environments, which creates a gap that regulators and data protection authorities are increasingly scrutinizing.

A GDPR AI inference deployment on confidential computing infrastructure addresses Article 32 by ensuring personal data processed during inference never appears in plaintext outside a secure, hardware-enforced enclave. The environment is cryptographically verified before any data is released to it, and an audit trail is maintained at the hardware level, providing much stronger proof that necessary GDPR requirements are being met. 

The EU AI Act's application to AI token factories depends on the role the factory plays in the AI value chain and the risk classification of the AI systems it runs. 

As of August 2025, the Act's obligations for providers of general-purpose AI models are in effect. There are obligations for high-risk AI systems that apply starting in August 2026, with some categories extending to December 2027 [source].  

The Act defines providers as those who develop or place AI systems on the market, and deployers as those who use AI systems in a professional context. An AI token factory operator that runs AI inference as a service is likely acting as a deployer of the underlying models, though operators that also configure, fine-tune, or package the model for delivery may take on provider obligations. 

For high-risk AI systems, the EU AI Act's technical requirements include robust data governance, meticulous technical documentation, automatic event logging, human oversight mechanisms, and measures to ensure accuracy, robustness and cybersecurity throughout the lifecycle. An EU AI Act-compliant platform built on confidential computing directly addresses several of these requirements. Hardware-enforced execution integrity supports the cybersecurity and robustness requirements, while attestation-based audit logging provides the automatic event records the Act requires. In addition, separating security governance from infrastructure operations helps meet human oversight requirements by preventing unauthorized access to or modification of AI systems during deployment. 

Organizations operating token factories in the EU or processing EU data of EU residents should classify AI workloads by risk level, document technical controls to prepare for conformity assessments, and make sure infrastructure providers can supply the audit evidence that authorities are bound to request. 

PCI-DSS is all about the security of cardholder data environments, and running PCI-regulated data through an AI token factory places the token factory infrastructure into scope for PCI compliance assessments. 

PCI-DSS version 4.0, which became mandatory in 2024, includes explicit requirements for encryption, access controls, logging and vulnerability management across all systems in the cardholder data environment [source]. For finance AI on-premises or in any controlled environment, the key question is whether the AI infrastructure handling cardholder data meets the technical requirements that an assessor will evaluate. 

A token factory running PCI-regulated AI workloads needs to demonstrate network segmentation between the cardholder data environment and other infrastructure; access controls that prevent unauthorized access to cardholder data during processing, encrypted transmission and storage; and detailed audit logging of all access to systems and data. The in-use encryption gap is relevant here: if cardholder data passes through an AI model in plaintext memory, it’s exposed to anyone with administrative access to the host system. 

Running PCI workloads on a token factory built on confidential computing infrastructure supports PCI-DSS compliance rather than working against it. The attestation audit trail also supports the logging and monitoring requirements in PCI-DSS 10 [source]. 

The certifications that matter if you’re evaluating an AI token factory depend on the industries and regulatory frameworks relevant to your organization’s workloads. That said, there are a few baseline indicators of security posture across most enterprise use cases. 

SOC 2 Type II is the foundational AI security certification for enterprise cloud and infrastructure services. A Type II report covers the effectiveness of controls over a defined period, meaning there’s evidence that the security controls are actually functioning rather than just documented. For AI token factories, a SOC 2 Type II audit covering the Security, Availability, and Confidentiality trust service criteria is the minimum most enterprise buyers will expect. 

ISO 27001 certification provides a broader information security management framework that maps well to regulated industry requirements in the EU and Asia-Pacific regions. For token factories serving European customers that must comply with the GDPR and the EU AI Act, ISO 27001 is often expected. 

For the cryptographic components of a confidential AI deployment, FIPS 140-2 or FIPS 140-3 certification of the hardware security module (HSM) handling key management is important. The HSM is the cryptographic trust anchor for the entire deployment: keys released by an uncertified HSM provide weaker assurance than those managed by a FIPS-certified device, which matters for regulated industries and government deployments. 

For healthcare-adjacent deployments, HITRUST CSF certification directly maps to HIPAA requirements and is increasingly required by healthcare payers and providers assessing vendor risk.

Attestation-based security is the best way to provide reliable evidence of compliance because it gives auditors verifiable proof. Every time an attestation occurs, it creates a hardware-signed log entry that records the unique hardware identity, firmware state, software stack, and the policies applied to a workload.  

These logs are tamper-proof; once they're created, you can't change them without invalidating the cryptographic signature, which is a significant advantage over traditional audit logs. 

For compliance audits, this means you can provide an auditor with attestation records that show exactly which hardware was running, which software was loaded, which security configuration was in effect, and which keys were released during your workloads.  

This is much stronger than a log that records only that a system was accessed. The attestation record is more like a timestamped, hardware-signed affidavit about the system's state at the time of processing, the type of evidence that satisfies a detailed audit. 

FedRAMP authorization applies to cloud service offerings and the specific infrastructure on which they operate. A FedRAMP AI platform means the cloud service itself, including its underlying infrastructure, has been assessed and authorized by a federal agency or the Joint Authorization Board against NIST 800-53 controls at the appropriate impact level [source]. 

An AI token factory that wants to process FedRAMP workloads needs to either obtain its own FedRAMP authorization or operate within the authorization boundary of an already-authorized infrastructure provider. FedRAMP High is the relevant baseline for workloads handling sensitive unclassified federal data, and defense workloads operating at Impact Level 4 or 5 require FedRAMP High plus DoD-specific controls from DISA's Cloud Computing Security Requirements Guide on top of the FedRAMP baseline. 

The FedRAMP 20x initiative specifically prioritizes AI-based cloud services. For token factory operators targeting federal customers, the 20x pathway can be a faster road to authorization for qualifying architectures, with emphasis on automated, machine-readable continuous monitoring evidence rather than static documentation. Confidential computing’s continuous hardware-signed attestation records align naturally with the kind of automated compliance evidence the 20x framework rewards.

Data residency guarantees from an AI token factory are based on where the infrastructure is physically located, who operates it, and what contractual and technical controls prevent data from leaving a defined geographic or organizational boundary. 

A token factory that runs workloads on infrastructure located in a specific country or region satisfies geographic residency requirements, provided the data doesn’t transit through external systems in a way that brings it outside the boundary during processing. The key question for AI data sovereignty deployment is whether inference-time data, including queries, context and outputs, is ever exposed outside the defined residency boundary. In a conventional cloud AI service, inference data travels to and is processed on infrastructure the customer doesn’t have control over, which creates a residency problem regardless of where the provider's data centers are located. 

An on-premises or sovereign AI token factory built on confidential computing infrastructure provides the strongest data residency guarantee currently available: processing happens entirely within the defined boundary, data is encrypted in memory throughout inference and never exposed to parties outside the verified enclave, and the attestation record provides evidence of where and how processing occurred. For organizations with strict data sovereignty requirements, this combination of physical location control, hardware-enforced processing confidentiality, and cryptographic audit logging satisfies both the technical and documentation requirements expected by regulators and auditors. 

Yes. In fact, for many model builders and enterprises with strict data governance requirements, on-premises deployment is the only configuration that makes sense. 

On-premises AI inference means running the full token factory stack (the GPU compute, the orchestration layer and the key management infrastructure) within infrastructure your organization physically owns and controls.  

Since there’s no need to rent capacity from a public cloud provider, it eliminates the data residency and third-party access concerns that come with sending sensitive data or proprietary models to infrastructure operated by someone else. 

Keep in mind that standard confidential computing architectures assume connectivity to vendor-operated remote attestation services. For genuinely air-gapped or disconnected on-premises environments, you need an architecture that supports local attestation verification, using pre-seeded reference measurements.  

You should confirm with any infrastructure provider whether your on-premises requirement includes the ability to operate without external network connectivity.

It depends on the infrastructure the token factory is built on. 

On conventional infrastructure, the answer is no, at least not in a cryptographically enforced sense. When you submit a prompt to a model running on standard infrastructure, that prompt is decrypted and processed in plaintext memory.  

The infrastructure operator's administrators can technically read that memory, even if their policies prohibit it. For prompts containing any sensitive information you wouldn't want exposed to a third party, this is a real risk. 

Confidential AI inference changes this through the same hardware-enforced isolation that protects model weights. The practical way to test whether prompts are genuinely confidential is to see whether the protection is backed by attestation and hardware isolation.  

A privacy policy is a promise, but attestation-verified hardware isolation is a property of the system that holds regardless of what anyone, including the provider, intends to do.