AI Security
Cyber-Resilient Governance for AI Systems
Protect AI data, tools, and outputs with deterministic controls enforced outside the model.
Make the data object the policy boundary. Every meaningful AI action resolves outside the model.
Architecture direction · qualified neurosymbolic security
Model-assisted interpretation. Deterministic enforcement.
Models may suggest intent or relationships. Governed context, explicit rules, and bounded verification retain authority.
Model-assisted signal
Interpret intent or propose relationships; suggestions remain non-authoritative.
Governed semantic context
Versioned classifications, relationships, purpose, and lineage provide bounded policy context.
Bounded verification
Translate supported actions into explicit constraints and repeatable security checks.
Policy enforcement
The existing authorization, key-release, and data-layer controls retain final authority.
/01Autonomous Resilience Risk
AI is an operational actor.
Govern retrieval, tool calls, outputs, and decisions at machine speed. Every action stays bounded, auditable, and recoverable.
- 01Agent actions must remain bounded under adversarial input.
- 02Retrieved context must be authorized before model exposure.
- 03Outputs must be traceable to source artifacts and policy.
- 04AI sessions must support containment, audit, and recovery.
/02Protected AI Objects
Every AI artifact is a protected object.
Prompts, retrieved context, tool responses, agent messages, and training samples receive the same protection: identity, lineage, policy-bound encryption, and attribute-based access. Classification and purpose stay tied to the data-model version that governed the artifact.
// Every AI artifact receives:
/03Out-of-Band Validation
Validation the Model Cannot Talk Its Way Past
Keep security decisions outside the model. The control path remains deterministic, testable, and auditable.
Control sets are checked against the governed data model, signed, and approved before they take effect.
01Out-of-BandThe control path is not the inference path.
Decisions about what data is released and what action is permitted are made by a component the model never touches. A prompt can shape what a model says. It cannot reach the thing that decides. Injection and jailbreak techniques work by persuading a model, and persuasion is not an input here.
02DeterministicSame request, same answer, every time.
No sampling, no temperature, no model judgment anywhere in the control path. A given request against a given policy version and attribute state resolves the same way on every evaluation, which is what makes the behavior testable before deployment and explainable after an incident.
03GroundedClaims require a source, not confidence.
Controls resolve against your governed data model (real classifications, real relationships, real rules) rather than against what a model believes to be true. Assertions that cannot be traced to an authorized source artifact do not pass. An output with a broken provenance chain is not a low-confidence output; it is a denied one.
04Validated Before LiveControls are checked, then approved, then enforced.
Before a control set can take effect it is machine-checked for contradictions, gaps, and unreachable rules, and the result is recorded as a signed validation record bound to the exact model version and control set it covers. A failed, stale, or unsigned result is not a warning, it is a blocked change.
05ScalableControls are compiled, not hand-written.
Because controls derive from the governed data model rather than from per-workflow rulesets, a new agent, data source, retriever, or tool inherits the existing control surface instead of requiring its own. Governance grows with the model, not with the number of AI surfaces you run.
// Control lifecycle, nothing skips a state
Governed Data Model -> Compiled Controls -> Machine-Checked -> Signed Record -> Explicit Approval -> Enforcement
A validation record is evidence, not authority. It proves a control set was checked against a specific data-model version it never grants access to anything. Every live request is still evaluated against current policy, current attributes, and current revocation state, and every key release is still authorized independently. Verified governance and runtime enforcement are separate boundaries by design, because collapsing them is how provable systems quietly stop being provable.
/04Enforcement Boundaries
Five Boundaries. Zero Implicit Trust.
Five enforcement points govern the AI data flow. Data does not pass without explicit authorization.
01Ingest
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.
02Retrieval
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.
03Tool & 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.
04Output
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.
05Training
Datasets are bound to the purpose they were authorized for. A sample admitted for evaluation is not thereby admitted for fine-tuning, and every admission decision is recorded against the source artifact's lineage, so the composition of a training corpus is an auditable fact rather than a reconstruction.
What Must Be Provable Before Data Moves
Any one of these unproven, and the operation is denied. Not degraded, denied.
Availability is never an authorization. If the decision plane, the audit path, or the cryptographic boundary cannot be reached, protected operations stop. There is no degraded mode that quietly becomes an allow.
/05AI 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.
/06Output Controls
Deterministic Output Governance
Output controls are not "one LLM reviewing another LLM." That approach inherits every weakness it was meant to fix. These are deterministic controls, the same output, against the same policy and destination, resolves the same way every time. 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.
Provenance Controls
Only release assertions backed by a verified source artifact. Require signed responses for high-trust actions. Fail closed when the provenance chain is incomplete rather than passing the output through with a caveat.
Destination Controls
Where an output is allowed to land is a policy decision, evaluated separately from whether it could be generated. A response permitted to a human reviewer is not automatically permitted to an agent, an API caller, or persistent storage.
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
/07Integration 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.
Protected artifacts use the standard interoperable package profile by default, so consumers outside your environment can still process them. Lattix extensions are additive and explicitly versioned, enabling one never silently changes the default format, and disabling one never orphans protected data.
/08Training Governance
Governed Training Data Supply Chain
Most platforms lose the thread at the corpus boundary. Data is governed carefully right up to the point it becomes training input, and then the controls stop, which means nobody can answer, six months later, what was actually in the corpus.
Lattix keeps enforcement at that boundary. Every sample is authorized individually, against the purpose it is being admitted for, with the decision recorded against the source artifact's lineage. Corpus composition becomes a record you can query rather than an artifact you have to reconstruct.
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. For teams running their own agents, Lattix delivers this governance as a local-first, sandboxed runtime.