An LLM deployed exclusively for one organization with no data sharing with other customers or the model provider for training; provides stronger confidentiality guarantees at higher infrastructure cost.
Last reviewed: 2026/05/19
Hardware-level encryption using Trusted Execution Environments that protects data even during AI processing, so cloud providers cannot access client data while the model runs.
SecurityAI models deployed on infrastructure owned or controlled by the law firm or legal department, keeping all data and computation within the organization's own environment.
Enterprise AI for portfolio-level contract analysis and institutional memory.
AI contract review with transparent per-contract pricing for solo and SMB clients.
Move from this definition to role-based legal AI shortlists and the selection criteria that matter for each type of legal team.
Am Law 200 and global firm workflows: accuracy at scale, security compliance, and matter-level auditability.
Legal department workflows: contract lifecycle, regulatory tracking, outside counsel management, and risk.
Last reviewed: 2026/05/19. Definitions are written by the LawyerAI Editorial team. Commercial relationships are disclosed and do not determine editorial scores or conclusions. See our Sponsorship & Affiliate Disclosure.
A private LLM is a large language model deployed exclusively for a single organization — a law firm, legal department, or government agency — such that no data processed through the model is shared with other customers, with the model provider, or used for training future model versions. Contrasts with public API-based LLMs (such as commercial APIs) where inputs may be retained, reviewed, or used for model improvement depending on the provider's terms. Private LLMs are deployed on the organization's own infrastructure, on dedicated cloud instances, or through vendor-managed single-tenant environments. They provide stronger confidentiality guarantees at substantially higher infrastructure cost.
Lawyers have confidentiality obligations to clients under professional conduct rules. The Bar has issued varied guidance on whether using a cloud LLM — where client data may be transmitted to and processed by third-party infrastructure — is consistent with those obligations. A private LLM addresses the concern by ensuring that client data does not leave organizational control or enter shared infrastructure.
The practical question is whether the confidentiality concern with shared LLMs is adequately addressed by zero-retention policies and enterprise terms — the approach taken by many firms using commercial LLM APIs — or whether physical data isolation in a private LLM is required. Regulated industries (government, healthcare, financial services) and matters with heightened sensitivity requirements often drive private LLM adoption.
Cost is the practical constraint. A private LLM requires dedicated infrastructure — whether on-premise GPU servers or single-tenant cloud instances — that is significantly more expensive than API access to shared LLMs. Smaller firms typically cannot justify private LLM infrastructure costs; large firms and enterprise legal departments can.
Harvey offers enterprise deployment options that include dedicated infrastructure configurations for clients with heightened confidentiality requirements — legal departments at financial institutions and large law firms with sensitive matter types are the primary market.
Luminance provides on-premise and private cloud deployment options for firms that require physical data isolation for regulatory or confidentiality reasons. LegalSifter supports private deployment configurations for contract analysis within controlled environments.