Agentic AI Data Security

Secure autonomous AI agents with continuous Zero Trust governance, from identity and behavior to data control, segmentation, and resilient response.

* Patent Pending.

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/ Problem

AI Is Moving Faster Than Access Control

/ Autonomy Without Boundaries

Today’s AI agents operate with autonomy, making real-world changes at machine speed, creating new threat surfaces that traditional model filters can’t secure.

/ Zero Trust for Autonomous Agents

ATF’s Zero Trust governance treats each agent as a non-trusted, continuously verified identity that must earn trust, be continuously observed, and be bounded in access and data interaction.

/ Solution

Continuous Agentic Data Governance

Before an AI agent executes a prompt, calls a tool, or retrieves context, the Lattix Policy Enforcement Point (PEP) evaluates:

Who is acting (agent identity & attestation)
What data is being accessed (classification & sensitivity)
Why the action is occurring (declared purpose)
Where execution occurs (environment, tenant, region)
How the request is processed (approved model, toolchain, or workflow)

Every response is validated, redacted, watermarked, or denied in real time under attribute-based zero trust policy.
AI agents operate as governed identities — not autonomous wildcards.

Data sovereignty

Policy travels with data into the model context. Access remains enforceable — even after ingestion. Data controls persist inside RAG pipelines, embeddings, and downstream workflows

Purpose binding

Access is cryptographically bound to declared purpose. Agents can only act within approved task scopes.

Immutable Lineage

Every inference, retrieval, and tool invocation is recorded and verifiable. Trust is auditable — not assumed.

Defense-grade Isolation

Optional second line if room: Fail-closed enforcement prevents privilege escalation or lateral movement.

/ Capabilities

/
Policy Aware Retrieval
Filters, transforms, and scopes RAG sources at query time. Builds least-privilege context per request — no implicit data exposure. Retrieval is governed by ABAC policies that evaluate identity, purpose, sensitivity, and environment before context enters the model.
/
Prompt/Reponse Controls
Validates, redacts, and sanitizes inputs before execution. Classifies, watermarks, or blocks outputs before delivery. All inputs and outputs are policy-gated in real time — preventing data leakage, prompt injection, and unintended disclosure.
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Model/Tool Allow List
Define which models, agents, and tools can access which datasets. Support per-tenant overrides and segmented execution boundaries. Execution is restricted to approved chains — preventing unauthorized tool invocation or model drift.
/
Session lineage & attestation
Logs which identities, policies, chunks, and tools shaped each answer. Every decision is cryptographically provable. Each inference event produces a verifiable audit trail — identity, context, decision, and result.
/
Purpose binding
Bind decrypt or data egress to declared task scope. Agents can act only within approved intent. Declared purpose becomes an enforceable attribute — preventing misuse, escalation, or lateral movement.
/
Offline/Airgapped Mode
Run enforcement locally with cached, signed policy snapshots. Fail-closed in disconnected or classified environments. Continuous governance without cloud dependency — built for defense-grade isolation.

/ Industries

Specialized Solutions for Every Industry

Finance

Healthcare

Retail

Education

Manufacturing

Government

Securing Financial Data with Blockchain Technology

In finance, safeguarding sensitive data is essential for maintaining trust and compliance. Lattix delivers cutting-edge data security solutions to protect your institution.

Lattix's platforms enable financial institutions to protect sensitive data with advanced encryption and blockchain-backed data lineage. From fraud prevention to regulatory compliance, we provide the tools to ensure security and trust in every transaction.

/ Let’s Boost Security

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