AI Security
Data-Centric Zero Trust for AI Systems
AI systems are only as trustworthy as the data they operate on. Lattix extends zero trust enforcement to every AI artifact — prompts, retrieved context, agent messages, tool payloads, model outputs, and training datasets. Not bolted on. Built in.
The integration model is not "bolt security onto AI." It is to make zero trust the native security envelope for every AI data object and make policy enforcement the runtime control path for every meaningful AI action.
/01Protected AI Objects
Every AI Artifact Is a Protected Object
Lattix does not define rigid artifact types. Every artifact — regardless of whether it is a prompt, a retrieved document chunk, a tool response, an agent message, or a training sample — receives the same protections: cryptographic identity, content-addressed lineage, policy-bound encryption, and attribute-based access control.
Classification, purpose, and lifecycle stage are expressed as policy attributes — not hardcoded categories. This means the same enforcement model scales across any AI workload without architectural changes.
// Every AI artifact receives:
/02Enforcement Boundaries
Five Boundaries. Zero Implicit Trust.
Lattix enforces policy at five distinct boundaries in the AI data flow. Every boundary is a policy enforcement point — data does not pass without explicit authorization.
Ingest
Every artifact entering the AI system is classified, identity-stamped, and wrapped in a zero trust envelope before it reaches any model or pipeline. Rejected data never enters the system.
Retrieval
Before any retrieved context, memory entry, or document chunk reaches a model context window, policy is evaluated and only authorized content is decrypted. The model never sees unauthorized data.
Tool & Agent
Every tool invocation and agent-to-agent message carrying business data is policy-protected. Cross-agent payloads carry identity, tenant scope, and purpose — enforcement follows the data across trust boundaries.
Output
Model responses pass through deterministic controls before display, persistence, or forwarding. This is not one LLM reviewing another — these are formal, auditable, policy-driven controls.
Training
The training data supply chain is governed end-to-end. Only authorized samples with verified lineage are admitted to training corpora. Model checkpoints inherit provenance from their input data.
/03AI Policy Attributes
Attribute-Based Access Control for AI Workloads
Lattix extends ABAC to AI-specific attributes — controlling not just whether data can be accessed, but what AI workloads can do with it, where outputs can be delivered, and which models can process which data classes.
/04Output Controls
Deterministic Output Governance
Output controls are not "one LLM reviewing another LLM." They are deterministic and statistical controls — formal, auditable, and policy-driven. Model output passes through analysis, policy evaluation, and routing before it reaches any destination.
Formal Policy Controls
Pattern-based leakage detection for identifiers, credentials, controlled markings, and structured secrets. Citation provenance requirements. Destination-specific deny rules.
Statistical Controls
Entropy thresholds for probable secret leakage. Similarity checks against protected corpora. Distribution shift detection on output structure.
Cryptographic Controls
Only allow assertions backed by verified source artifacts. Require signed responses for high-trust actions. Fail closed when provenance chain is incomplete.
Deterministic Transforms
Redaction, masking, field deletion, confidence-based truncation, and forced templating for regulated workflows. Auditable and repeatable.
// Output gate pipeline
Model Output -> Deterministic Analyzers -> Policy Evaluation -> ALLOW | TRANSFORM | QUARANTINE | DENY
/05Integration Targets
Model-Agnostic Enforcement
The integration target is the orchestration layer, not the model vendor. Enforcement is model-agnostic and works across any LLM, framework, or agent protocol.
Enforce on tool discovery, invocation, and output. Protected payloads for business data crossing tool boundaries.
Protected message envelopes with agent identity, tenant scope, purpose, and artifact references across agent boundaries.
Secure retriever, memory, tool, and callback primitives. Policy enforcement at graph edges, not just endpoints.
Session-scoped policy context. Every upload, retrieved chunk, model response, and memory item is a protected, identity-stamped object.
Governed data admission, purpose-bound datasets, lineage-aware checkpoint management, and compliant model release.
AI-specific audit events with tenant, artifact identity, policy context, session, agent, model, and workflow identifiers.
/06Training Governance
Governed Training Data Supply Chain
Most AI security platforms fail at training governance because controls vanish once data enters the training corpus. Lattix maintains policy enforcement from source artifact through training to model output.
Only authorized samples with verified lineage are admitted. Model checkpoints and adapters inherit provenance from their input data. Model release requires policy verification against restricted content classes.
Every enforcement decision is signed, auditable, and tied to tenant, policy, and purpose. Compliance is continuous — not point-in-time.
Secure Your AI Data Pipeline
See how Lattix enforces zero trust at the data layer for AI systems — from retrieval to training to agent orchestration.