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About Contact Us
AI-first engineering

Enterprise AI Security & Compliance for Hospitality.

Enterprise AI security for hospitality systems. Protect AI apps, RAG, agents, data, model APIs, integrations, and compliance evidence across production AI.

Get started

See Logiciel in action.

Tell us what you're building and we'll take it from there.

15+
Years building production software
120+
Engineers across delivery pods
75+
Clients served in North America
3K+
Successful product releases
Why Logiciel

Why Enterprise AI Security Needs More Than Traditional AppSec for Hospitality.

Why Logiciel · 01

AI systems create new attack paths across guest data, operational systems, connected tools, model endpoints, and generated outputs.

Why Logiciel · 02

Prompt injection can turn untrusted content into instructions that manipulate responses, expose information, or trigger unsafe actions.

Why Logiciel · 03

RAG and vector systems can bypass intended access boundaries if source permissions are not enforced during retrieval.

Why Logiciel · 04

Agents increase risk because model outputs can lead to API calls, system changes, messages, approvals, or other real actions.

Why Logiciel · 05

Guest-data exposure, unsafe actions, poisoned content, and excessive access require workflow-specific controls.

Why Logiciel · 06

Third-party models and AI services create additional questions around data handling, logging, service identity, retention, and dependency risk.

Why Logiciel · 07

Security and compliance evidence must remain current as models, prompts, integrations, data sources, permissions, and application behavior change.

What you get

What You Get From Logiciel Enterprise AI Security & Compliance for Hospitality.

We combine application security, AI engineering, identity, data protection, and adversarial testing to protect guest service, reservations, loyalty, property operations, workforce support, and hospitality knowledge workflows without treating AI as an isolated security problem.

01

AI-specific threat visibility

mapping attack paths across prompts, models, retrieval, data, tools, agents, APIs, users, and connected business systems

02

Stronger data boundaries

protecting sensitive information across retrieval, model traffic, logs, traces, embeddings, caches, and generated outputs

03

Controlled agent permissions

restricting credentials, tool access, actions, transaction scope, approvals, and execution boundaries for agentic systems

04

Adversarial AI testing

testing prompt injection, data exfiltration, unsafe tool use, poisoned content, policy bypass, and realistic abuse paths

05

Secure AI integrations

applying identity, authorization, secret management, validation, and least-privilege controls around model and business-system connections

06

Security evidence for review

capturing architecture decisions, test results, control mappings, exceptions, remediation, and monitoring for internal assurance teams

07

Ongoing AI security validation

retesting critical controls as models, prompts, retrieval sources, vendors, integrations, and application behavior evolve

Trust & security

Enterprise AI Security Across Hospitality Systems.

01

Hospitality AI Threat Modeling

What it meansMap threats across guest data, reservations, prompts, retrieval, integrations, user roles, and property workflows.
02

Guest Service AI Security

What it meansProtect assistants that access reservations, loyalty, service history, property information, and support systems.
03

Hospitality RAG Security

What it meansSecure retrieval across policies, property content, SOPs, guest information, and internal knowledge sources.
04

Reservation Copilot Security

What it meansTest AI-assisted booking and support workflows for data leakage, prompt injection, role failures, and unsafe actions.
05

Property Operations AI Security

What it meansProtect copilots used for maintenance, housekeeping, service requests, workforce coordination, and operational knowledge.
06

Hospitality Agent Action Controls

What it meansConstrain reservation changes, guest messages, service requests, account updates, and connected-system actions.
07

Adversarial Hospitality AI Testing

What it meansExercise malicious inputs, cross-property leakage, tool misuse, poisoned content, and policy bypass across realistic workflows.
Trust & security

Enterprise AI Security Models Built Around Your Team for Hospitality.

01

Dedicated AI Security Engineering Squad

AI, application-security, cloud, and software engineers work across architecture, control implementation, testing, remediation, and production hardening.

02

AI Security Consulting and Team Extension

Security and AI specialists strengthen internal teams on threat modeling, architecture review, adversarial testing, identity, data protection, and secure implementation.

03

Outcome-Based AI Security Engagement

A focused engagement secures or tests one AI application, RAG system, copilot, agent, or high-risk integration against defined security objectives.

Under the hood

Enterprise AI Security & Compliance Services We Deliver for Hospitality.

01

AI Threat Modeling and Architecture Review

We map assets, trust boundaries, data flows, model dependencies, retrieval paths, tools, users, and abuse cases across the AI system.

Included
02

Prompt Injection and LLM Application Security

We test instruction conflicts, untrusted content, unsafe output handling, sensitive-data exposure, and control bypass across AI application workflows.

Included
03

RAG and AI Data Security

We review indexing, document permissions, retrieval authorization, source integrity, embeddings, caches, logging, and sensitive-data handling.

Included
04

AI Agent and Tool Security

We constrain service identities, credentials, tools, actions, approval points, transaction limits, and exception handling for systems that can take actions.

Included
05

Model API and Integration Security

We secure model endpoints, secrets, gateways, rate limits, service authentication, request validation, logging, and connections to business systems.

Included
06

Adversarial AI Testing and Remediation

We exercise realistic attack scenarios, document exploitable behavior, prioritize findings, and work with engineering teams on practical remediation.

Included
07

AI Security Monitoring and Compliance Evidence

We define security signals, incident paths, change triggers, test evidence, control records, and recurring validation to support internal assurance activities.

Included
Trust & security

Enterprise AI Security Insights & Frameworks for Hospitality.

01

AI Attack Surface Model

A framework for mapping threats across input, model, retrieval, memory, data, tool, identity, integration, output, and human interaction layers.

Trust & security
02

Agent Permission and Action Matrix

A method for defining which identities, tools, data, actions, limits, approvals, and rollback paths are allowed for each agent workflow.

Trust & security
03

AI Security Regression Model

A framework for deciding which adversarial, access-control, data-leakage, and tool-use tests should rerun when AI components change.

Trust & security
Trust & security

Our Enterprise AI Security Framework for Hospitality.

01

Map the AI Attack Surface

We identify models, prompts, retrieval, data, identities, tools, APIs, users, trust boundaries, and high-impact abuse scenarios.

02

Prioritize Security Requirements

We rank threats by exploitability and impact, then define data, identity, authorization, tool, monitoring, and testing requirements.

03

Implement and Harden Controls

We strengthen access, validation, isolation, secrets, data handling, agent permissions, and safe failure behavior around the application architecture.

04

Adversarially Test the System

We test realistic prompt attacks, data exposure, retrieval abuse, authorization gaps, unsafe actions, and integration failures against defined criteria.

05

Monitor, Retest, and Maintain Evidence

We track security signals and rerun critical tests as models, prompts, vendors, data, tools, permissions, and application behavior change.

Selected work

Tailored engineering for your industry.

Zeme · Real EstateCut development costs 50% and launched 3× faster with dedicated dev teams.
Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Leap · ConstructionScaled to 7-figure ARR with AI-augmented software teams.
Construction

Scaled to 7-figure ARR with AI-augmented software teams.

KW · Real Estate56M+ workflows automated, saving agents 30% time with AI-powered tasks.
Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

In their words

What our clients say.

Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.

Patrick Fingles

I would highly recommend them to anyone looking to scale quickly or needing support in engineering, product, or QA.

Patrick Fingles
Patrick Fingles
CEO, Leap
Elior Alayev

We don't just call them Logiciel; they're part of the Zeme team. Within the first week they were contributing meaningfully to our codebase.

Elior Alayev
Elior Alayev
Founder & CEO, Zeme
David Buzzelli

The Logiciel team worked tirelessly and built everything we needed, with security and best practices across our entire platform. It allowed us to become #1 in our industry, and we couldn't have done it without them.

David Buzzelli
David Buzzelli
Co-Founder, JobProgress
Questions

Frequently asked questions.

What is enterprise AI security for hospitality?

Enterprise AI security protects AI-enabled hospitality applications across models, prompts, retrieval, data, identities, tools, APIs, agents, and user workflows. It extends traditional application and cloud security with controls for AI-specific failure and abuse patterns.

What are the main security risks in LLM applications?

Common risks include prompt injection, sensitive-data exposure, insecure output handling, weak retrieval permissions, excessive agent privileges, insecure integrations, dependency risk, and insufficient monitoring.

How do you secure RAG systems?

RAG security includes controlling which documents are indexed, enforcing authorization during retrieval, protecting vector and metadata stores, validating source integrity, limiting sensitive data, and testing for cross-user or cross-tenant leakage.

How do you secure AI agents that can take actions?

Agents should use scoped identities, least-privilege tools, explicit action boundaries, validation, transaction limits, approval for consequential actions, auditable execution, and safe handling when tools or context fail.

What is prompt injection testing?

Prompt injection testing checks whether malicious or untrusted content can override intended instructions, reveal protected information, bypass policies, or manipulate connected tools and workflows.

Does AI security engineering make a system compliant?

No. Security controls and evidence can support compliance activities, but compliance depends on the organization, jurisdiction, data, use case, and applicable requirements. Legal and regulatory interpretation should remain with qualified professionals.

How often should hospitality AI systems be security tested?

Critical tests should be repeated when models, prompts, retrieval sources, permissions, tools, integrations, vendors, or application behavior change. Higher-risk systems may also need recurring adversarial testing and monitoring between releases.

Let's build

Secure Your Hospitality AI Before It Reaches Production.

Share the AI application, RAG system, copilot, or agent you need to protect. We’ll help map the attack surface, prioritize controls, and define the security testing needed before release.