What Is Private AI? Definition, Benefits, Challenges

While artificial intelligence (AI) can streamline and automate many tasks, using public AI is risky from a corporate standpoint. Relying on public AI models carries the risk that third-party providers may store, analyze, or use input data for model training, which raises serious data security, residency, and privacy concerns.

To eliminate these risks, a growing number of companies are turning towards private AI strategies. This type of AI restricts all interactions with the model to internal databases and APIs, ensuring the company's IP and customer info remain private during use.

This article is an in-depth guide to private AI that provides a detailed look at how these models enable companies to use AI technologies without jeopardizing the safety of their data. Read on to learn what these models offer and see whether your business would benefit from a private artificial intelligence strategy.

What Is Private AI?

Private AI is an artificial intelligence environment designed to process data and generate insights while ensuring strict privacy and security controls. Most private AI systems are created by organizations that need a model to process proprietary data without exposing it to third-party providers.

Unlike public AI models that run on external servers, private AI performs computations on a restricted infrastructure. Models run on local hardware, keeping data entirely in-house. Adopters also often boost security with the following precautions:

• Federated learning that enables decentralized training across multiple devices.
• Differential privacy that introduces intentional noise to data points.
• In-use encryption that allows models to analyze scrambled data without requiring decryption.
• Zero-knowledge proofs that enable models to verify info without revealing underlying data.

To qualify as a private AI, a model must follow these core principles:

• No external data sets. Companies must only use open-source and proprietary data to train private AI models.
• Full data control. The data must never leave the owner's control during its processing lifecycle.
• Exclusive access. The model must be in exclusive control of the adopting organization.
• Private compute. All processing must occur on the company-owned hardware, whether on-prem, on edge devices, within a private cloud, or in colocation facilities.
• Independence. The private AI model must not depend on external APIs or public cloud services.

Recent reports reveal that security concerns are the primary driver of private AI adoption. While businesses believe that the biggest risk of adopting AI is inaccuracy (56%), the next three most common concerns are cyber security risks (53%), IP infringement (46%), and personal privacy risks (39%).

The need to comply with strict data protection regulations like GDPR and HIPAA is another common reason companies turn towards private AI. Private models help organizations stay compliant by processing data within tightly controlled environments.

Private vs. Public AI

The table below provides an overview of the main differences between private and public AI models:

Retrieval-Augmented Generation (RAG) and Private AI

Most AI language models generate responses based only on pre-trained knowledge. This approach limits the model to its training data and often results in outdated or incorrect responses. AI "hallucinations" are also common as models often generate plausible but false insights due to insufficient info.

Retrieval-augmented generation solves this problem by retrieving relevant data from an external source (e.g., database, document repository, knowledge base) before generating a response. Instead of relying solely on the model's preset knowledge, RAG enables the system to dynamically pull the most relevant, up-to-date data and integrate it into the response.

For example, if you ask a RAG-powered model about company policies, it won't just give you a generic answer it learned during training. Instead, it will search the company's current internal documentation, extract relevant sections, and use that info to generate a precise response.

RAG plays a crucial role in making private AI systems more reliable and domain-specific. By using RAG, private models dynamically pull relevant info from internal databases or document repositories, ensuring responses are accurate and context-aware.

The RAG-based approach is particularly valuable when AI must generate precise and compliant outputs while maintaining strict data privacy levels.

Here are a few examples where using RAG for private AI makes sense:

• Pulling real-time product and policy info during customer interactions.
• Retrieving patient records to assist with diagnoses while maintaining HIPAA compliance.
• Accessing internal risk models and compliance guidelines to generate financial reports.

On the negative side, RAG does introduce some new responsibilities. Adopters must ensure internal data sources are constantly updated and deal with potential latency issues when retrieving large volumes of data. Companies must also consider security risks related to indexing and querying sensitive information.

Benefits of Private AI

Private AI offers significant benefits for businesses that require high levels of security, customization, and control in their AI environments. Here are the most notable selling points of this type of AI:

• Data privacy and security. Private AI models enable companies to retain complete control over their data, which reduces the risk of breaches and leaks. With private AI, sensitive business files, customer records, and intellectual property never leave company-owned hardware.
• Domain-specific knowledge. Private AI can be fine-tuned on proprietary data sets to better understand industry-specific terminology, workflows, and company policies.
• Easier compliance. Private AI enables companies to maintain compliance by limiting all data-related processes to privately owned hardware. Ensuring compliance is significantly more challenging when companies rely on external AI providers.
• Reduced third-party dependence. Relying on external AI services often creates risks related to vendor lock-in, pricing changes, and service availability. Private AI eliminates these dependencies by allowing companies to build and manage their AI models internally.
• Less exposure to cyber threats. Private AI minimizes the risks of adversarial attacks, data poisoning, and unauthorized access by operating within a secure infrastructure with tightly controlled and monitored access.
• Seamless integration with internal systems. Private AI can be fully integrated into various business ecosystems (e.g., legacy systems, ERP platforms, internal databases, custom apps).
• Faster processing. When AI models run on internal servers or private cloud infrastructure, response times are significantly faster compared to relying on public AI APIs.
• Greater uptime. Since private AI runs on internal infrastructure, businesses are unaffected by external outages or service disruptions from third-party AI providers.
• Higher interpretability. Private artificial intelligence models can be designed with greater transparency, which allows businesses to audit decision-making processes and ensure compliance with ethical AI standards.

Challenges of Private AI

While private AI is highly beneficial in the right circumstances, these models come with several notable challenges you must know about. Here are the main downsides of deploying and using this type of AI:

• High implementation costs. Setting up a private AI requires significant investments in hardware and software. You'll need to invest heavily in the right infrastructure to run an advanced AI model on-site.
• High ongoing costs. In addition to high CapEx costs, private AI models also come with considerable operational expenses (OpEx). Adopters must account for high energy consumption and the cost of expensive in-house maintenance teams.
• Data management and quality control. Private AI relies on an organization's internal data, which must be clean, structured, and regularly maintained. Poor data quality leads to inaccurate models that provide faulty outputs.
• Longer development timelines. Unlike public AI, which is ready for immediate use, private AI projects require months (or even years) to design, train, test, and deploy.
• Security responsibilities. While private AI enhances data privacy, it also increases the responsibility of managing security precautions, access controls, and compliance with regulations.
• Lack of access to the latest models. Public AI providers like OpenAI, Google, and Microsoft continuously update their models with the latest advancements. Private AI systems often lag behind in innovation unless companies invest heavily in R&D and ongoing model improvements.
• The required in-house talent. You'll need skilled personnel to develop, deploy, and optimize a private artificial intelligence model. Hiring and retaining AI specialists, data scientists, and machine learning (ML) engineers is challenging unless you offer above-market-average salaries.
• Scalability limitations. Unlike public AI, private models are limited by an organization's internal resources. Scaling AI models to handle larger workloads requires expanding on-prem infrastructure, which is expensive and complex.

Infrastructure Requirements of Private AI

Running private AI requires a robust infrastructure capable of handling high computational demands, secure data processing, large-scale model training, and inference workloads. Here's an overview of what you need to run a private AI model:

• Powerful GPUs, TPUs, specialized AI accelerators, and cloud-based FPGA solutions to train and run the model efficiently.
• High-speed SSDs, NAS, or distributed storage systems to manage extensive data sets with low latency.
• Low-latency networking solutions with high bandwidth (e.g., InfiniBand or high-speed Ethernet) to support AI workloads and high-speed data transmissions.
• Dedicated space with sufficient power and cooling capabilities to support continuous AI operations.

You also require a variety of software to run your private AI model. Here's an overview of what you'll need:

• ML frameworks (such as PyTorch or TensorFlow), MLOps tools (Kubeflow, MLflow), and containerization platforms (Docker, Kubernetes) for efficient AI model deployment.
• Role-based access, data encryption, and monitoring tools to ensure compliance with security policies.
• Tools for data preprocessing, version control, and lineage tracking to maintain AI model accuracy.

If you do not have a private data center or a sizable on-site server room, colocation is an excellent alternative for private AI workloads. As long as you ensure secure connectivity between on-prem systems and the facility, deploying hardware at a colocation data center offers the following benefits:

• You avoid the need for an in-house data center while still maintaining complete control over your hardware.
• Colocation providers offer top-tier data center security and hardware redundancy.
• You can easily scale AI workloads by renting additional rack space, power, or cooling without significant upfront investments.
• Colocation infrastructure is optimized for high-performance computing required to run compute-heavy AI and ML workloads.
• Your team can focus on AI development rather than infrastructure maintenance, power distribution, and environmental controls.

How to Launch and Scale Private AI

Launching a private AI begins with defining clear objectives. You need to identify specific use cases for your private AI, such as automating repetitive tasks, improving decision-making with predictive analytics, or optimizing internal processes.

Once you understand your goal, evaluate whether you have the necessary data to train your private AI model. This type of AI does not use third-party data sets unless they are open-source, so there's not much point in planning a model if you do not have sufficient training data.

The next step is to build the necessary infrastructure. Organizations must decide whether to deploy AI on-prem, in a private cloud, or in a colo data center. No matter where you deploy it, high-performance computing resources and rapid-access storage solutions are a must-have.

Businesses can either train their own AI or fine-tune ready-made models like LLaMA, Falcon, or Mistral. Both approaches have their pros and cons:

• Building an AI from scratch allows you to tailor the model to specific security and operational needs. However, this process requires significant investments in AI expertise.
• Fine-tuning a ready-made AI platform accelerates deployment but comes at the price of customization.

Choose a machine learning or deep learning model suited to your use case. Frameworks like TensorFlow, PyTorch, or Scikit-learn provide tools for effectively building and training models.

Next, feed the data set into the model, adjust hyperparameters and use techniques like cross-validation to improve accuracy. Monitor performance metrics and retrain as needed to prevent biases and overfitting (i.e., instances when models memorize training sets so closely that they fail to make correct predictions on new data).

Remember that security and governance are vital in private artificial intelligence deployments. Implement a zero-trust security model, encrypt sensitive data, and ensure compliance with relevant industry regulations from the beginning of model development.

The Future of Enterprise AI

Private AI models are slowly emerging as the go-to strategy for using AI within corporate settings. This type of AI enables businesses to enhance security, take control over compliance adherence, and eliminate third-party dependencies. As AI adoption continues to broaden, expect more organizations to start investing in AI models that lower the risk of exposing sensitive data.