Public AI or private AI? Start with the work and the data.
Public tools such as ChatGPT, Microsoft Copilot, Gemini, and similar services are strong choices for general, non-sensitive work. A private deployment is the better fit when work requires sensitive operational data, proprietary processes, connected internal systems, controlled access, deployment-specific governance, or traceable workflows. Many organizations can use both.
Public and private are deployment choices, not safety ratings. Business versions of hosted tools can offer substantial controls. A private environment still requires careful configuration, maintenance, evaluation, and accountable people.
Public and private AI compared
| Consideration | Public AI | Private AI |
|---|---|---|
| Typical deployment location | Usually hosted in a vendor-managed cloud service. | An agreed private cloud, on-premises, or local environment where supported; dependencies may still use external services. |
| Data boundary and contractual terms | Privacy, retention, residency, and model-training terms vary by product, plan, configuration, and contract. Verify the selected service. | Define permitted data flows, storage, retention, and third-party access for the actual deployment. Private does not automatically mean isolated. |
| Business context and grounding | General knowledge plus uploaded files, retrieval, or enterprise connectors where available. | Approved business sources, operating context, and retrieval rules shaped around specific workflows. |
| Access control | Business offerings may include identity integration, administrative policies, and permission-aware connectors. | Roles, source permissions, service identities, and action permissions are scoped and tested for the implementation. |
| Auditability | Logging and administrative visibility depend on the offering and configuration. | Source receipts, activity records, review history, and retention can be designed into workflows; confirm what is implemented. |
| Internal-system integration | Supported connectors and APIs can connect business systems, subject to licensing and permissions. | Selected integrations can be tailored to internal systems, with explicit boundaries and maintained credentials. |
| Automation capability | Some products support agents, tools, and automated actions, not just chat. | Role-specific agents can perform approved actions within defined permissions, escalation paths, and review rules. |
| Deployment and management | The vendor manages the service; the organization still manages policy, access, configuration, and acceptable use. | Infrastructure, updates, evaluation, recovery, and monitoring responsibilities must be assigned to the organization and implementation partners. |
| Local and edge options | Mainstream hosted assistants generally depend on the provider's service; check product-specific local capabilities. | Local or edge models may be possible, subject to hardware, model capability, licensing, connectivity, and support. |
| Best-fit work | General research, brainstorming, and drafting with non-sensitive or explicitly approved material. | Sensitive operational data, proprietary processes, connected internal systems, controlled access, and traceable workflows. |
Related scope and evidence
Capabilities
- When public AI fits: Use an approved public tool to brainstorm, explain general concepts, or draft from material cleared for that service. Check outputs against reliable sources. Do not assume a personal account has the same terms or administrative controls as a business plan.
- When private AI fits: Choose a scoped private deployment when the workflow must use proprietary operating knowledge, sensitive records, internal-system permissions, or reviewable actions. Specify the data boundary, sources, reviewers, integrations, and acceptance criteria before choosing infrastructure.
- A practical hybrid approach: Classify tasks and data first. Keep general work in approved hosted tools, and route restricted operational work into a governed private environment. Define what may cross the boundary, including prompts, attachments, generated summaries, logs, and connector results. Train staff on the distinction and review the policy as services change.
- How Drivers Seat approaches private AI: Derrick Brain provides governed business context and orchestration within Drivers Seat's agent platform. Role-specific agents work through approved sources and workflows. Derrick Box is the private/local deployment option for sensitive data and tighter data-boundary requirements. The implementation defines permissions, source use, review points, and escalation—not autonomous authority.
Privacy and safety
Educational guidance only. Organizations must verify the selected vendor plan, configuration, contracts, legal duties, and data-classification requirements. Privacy, retention, residency, and training terms vary; public AI does not always train on customer data. Neither a private deployment nor a product label guarantees security, compliance, or zero risk.
Frequently asked questions
What is private AI?
Private AI is an AI implementation with deliberately defined data, access, and operating boundaries for an organization. It can use private-cloud, local, or on-premises infrastructure where supported. Ask which components, logs, models, and external dependencies sit inside or outside the boundary.
Is ChatGPT safe for confidential business data?
There is no single answer for every ChatGPT offering. Verify the selected plan's training, retention, access, residency, and contractual terms against your data policy. Use an approved organizational account and configuration; do not enter confidential information until the intended use has been authorized.
Does Microsoft Copilot keep business data private?
Microsoft offers different Copilot products and plans. Check the specific service's data protections, contractual commitments, connectors, retention, and administrative settings. Review source-system permissions too: an assistant may reveal material a user can access but should not broadly share.
Do we have to choose public or private AI?
No. Many organizations use approved public tools for general work and a private environment for restricted workflows. Define rules for moving information between them, including summaries and generated outputs, rather than relying on staff to guess.
Can private AI run locally or on premises?
Yes, where the model, hardware, licensing, integrations, and support requirements permit it. Confirm which functions require internet access and where telemetry, backups, and logs go. Local hosting alone does not establish an effective security boundary.
What is Derrick Box?
Derrick Box is Drivers Seat's private/local deployment option for sensitive data and tighter data-boundary requirements. Its infrastructure, connections, access controls, and review processes are scoped for the environment; it is not a blanket security or compliance guarantee.
How do private AI agents connect to our systems?
Through approved APIs, connectors, or controlled data pipelines, using scoped identities and source-system permissions. Start with the minimum necessary access, test retrieval and permission boundaries, and assign responsibility for credentials, monitoring, and integration changes.
Can AI agents take action automatically?
Yes, when a specific action is authorized and implemented within defined permissions. Begin with read-only or draft workflows, then define approval requirements, limits, logs, exception handling, and recovery before enabling actions. Consequential decisions remain with accountable people; automation is not unrestricted authority.
What should we do if staff already use public AI?
Inventory the tools and uses without discouraging reporting. Provide approved accounts and practical guidance, identify any sensitive information already shared, and follow your incident process where appropriate. Offer a supported route for restricted work and train staff on examples they actually encounter.
How do we decide which data can go into which AI tool?
Classify the data, identify its owner, and assess contractual restrictions, legal duties, sensitivity, retention, and permitted recipients. Match those requirements to the exact service configuration. Document approved uses and prohibited transfers, require review for uncertain cases, and reassess when products or workflows change.