Security and Privacy

Security and Trust Model

NativelyAI is built so that security is not an add-on or a later concern. It is part of how execution itself works. Every action, model call, and data movement follows strict rules by default. Nothing is trusted just because it exists inside the system.

Encrypted Data Flow

All data processed through NativelyAI remains within infrastructure owned or designated by the user.

Execution, storage, and model inference occur inside environments chosen by the organization, such as private servers, VPCs, on-prem systems, or air-gapped deployments. Encryption is applied at rest, in transit, and during execution where supported by the runtime.

The platform functions as a coordination and orchestration layer, while data location, custody, and lifecycle remain anchored in user-controlled environments.

Private Inference and Model Isolation

A core security guarantee of NativelyAI is that model inference happens inside user-controlled environments.

Models — whether open-source, proprietary, or fine-tuned — are deployed within private infrastructure. Prompts, intermediate representations, and outputs are never transmitted to external APIs by default.

This eliminates common leakage vectors such as:

• prompt logging by third-party providers,

• reuse of inference data for model training,

• cross-tenant exposure in shared environments.

Model isolation also ensures that different workloads, teams, or tenants cannot influence each other’s execution or outputs.

Auditing, Logging, and Verifiability

Every action in NativelyAI produces a verifiable execution trail.

The platform records:

• which models were used,

• where execution occurred,

• what policies were applied,

• and how decisions were made at runtime.

These logs are immutable and can be retained, exported, or integrated into existing security information and event management (SIEM) systems. This enables both internal audits and external compliance reviews without reconstructing system behavior after the fact.

Because execution is deterministic and versioned, historical behavior can be reproduced and inspected precisely.

Policy-Driven Governance

Security and compliance rules in NativelyAI are expressed as executable policies, not documentation.

Policies define:

• where data may be processed,

• which models may be used,

• cost and resource limits,

• access controls and approval boundaries.

These rules are enforced continuously by the orchestration layer. If an action violates policy, execution is blocked automatically.

This shifts governance from manual review cycles to real-time enforcement, enabling faster delivery without compromising control.

Threat Surface Reduction by Design

By consolidating models, orchestration, data access, and deployment into a single system, NativelyAI significantly reduces the attack surface compared to fragmented AI stacks.

There are fewer external dependencies, fewer exposed APIs, and fewer credentials to manage. This architectural consolidation removes entire classes of vulnerabilities associated with tool sprawl and shadow AI deployments.

Security is improved not by adding layers, but by eliminating unnecessary complexity.