Trust is a system requirement, not a marketing label
Calling an AI system private does not by itself prove security, confidentiality, control, accuracy, or compliance. Drivers Seat treats trust as a set of requirements to define, implement, test, document, and monitor for the specific workflow and environment.
The trust model considers data boundaries, identity, least privilege, source authority, retrieval behavior, retention, encryption where implemented, logging, versioning, evaluation, human approval, incident response, backup, recovery, vendors, and deployment architecture.
Controls are described precisely. On-premises, private-cloud, and hybrid approaches may be considered where supported, but no certification, residency outcome, or technical safeguard is claimed until it is part of the approved architecture and has been verified.
Capabilities
- Data and source boundaries: Define which information is permitted, authoritative, sensitive, excluded, time-limited, or subject to special handling before it reaches a model or retrieval layer.
- Identity and least privilege: Map users, roles, service accounts, tools, data access, approvals, and administrative privileges to the smallest access needed for the workflow.
- Grounding and source receipts: Design outputs to cite or link to approved sources where appropriate, distinguish evidence from generated language, and expose missing support.
- Evaluation and human approval: Test representative and adversarial cases, define acceptance thresholds, preserve accountable review, and prevent unsupported automation of high-consequence decisions.
- Audit, retention, and lifecycle: Scope logs, versioned knowledge, prompt and configuration changes, record retention, deletion, model updates, monitoring, backup, and recovery.
- Deployment and vendor review: Evaluate on-premises, private-cloud, or hybrid options, network boundaries, subprocessors, contracts, egress, availability, cost, and operational ownership.
Privacy and safety
Drivers Seat scopes controls with each customer. Depending on requirements and supported infrastructure, an implementation may use on-premises, private-cloud, or hybrid options.
Scoping covers data boundaries and retention, encryption where implemented, role access and least privilege, source receipts, auditability, versioned knowledge, human approval, backup and recovery, connectivity, and third-party dependencies. Controls that are not verified or implemented are requirements to scope, not guarantees.
Frequently asked questions
Does private AI mean our data never leaves our network?
Not automatically. Data flow depends on the selected architecture, vendors, integrations, logging, support model, and configuration. Network and data boundaries must be explicitly designed and verified.
Can Drivers Seat guarantee compliance?
No blanket compliance guarantee is made. Applicable legal, contractual, industry, privacy, security, and records requirements must be identified with the customer's qualified advisors and translated into verified controls.
How do you reduce hallucinations?
Risk can be reduced through authoritative retrieval, constrained prompts and tools, source citations, structured outputs, refusal and escalation behavior, evaluation sets, monitoring, and human review. It cannot be eliminated by a slogan.
Who approves AI-generated work?
The customer defines accountable owners and approval paths for each workflow. Higher-risk outputs require stronger review, clearer escalation, and tighter automation boundaries.