AI Security
Cyber-Resilient Governance for AI Systems
Govern AI artifacts, agents, retrieval, tools, and outputs with data-centric controls that preserve trust, containment, and auditability under autonomous operation. The controls are deterministic, validated before they go live, and enforced outside the model, so there is nothing for a prompt to talk its way past.
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.
/01Autonomous Resilience Risk
AI Systems Are Becoming Operational Actors
They retrieve data, invoke tools, generate outputs, and influence decisions at machine speed. Cyber resilience requires more than filtering prompts, it requires governing the data, context, and actions that autonomous systems depend on.
When AI is treated as a first-class operational actor, every action it takes is a potential decision point that must remain 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
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.
Classification is not a label an object carries by convention. Every protected artifact is bound to the published data-model version that defined its classification and to the control set compiled from it. When the model changes, existing artifacts keep the controls they were protected under, a new model version never rewrites history.
// Every AI artifact receives:
/03Out-of-Band Validation
Validation the Model Cannot Talk Its Way Past
Most AI security products put a model in charge of policing a model. That inherits every weakness it was meant to fix: it can be persuaded, it is non-deterministic, it cannot explain itself under audit, and it fails in exactly the conditions you deployed it for.
Lattix takes the opposite approach. Validation and approval happen outside the model, against your governed data model, with deterministic results, and every control set is checked and signed before it is ever allowed to take effect.
Out-of-Band
The 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.
Deterministic
Same 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.
Grounded
Claims 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.
Validated Before Live
Controls 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.
Scalable
Controls 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.
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
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.