Enterprise AI security and compliance engineering for LLM apps, RAG, agents, model APIs, data, and integrations. Reduce AI security risk across production systems.

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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.

Why Logiciel · 01

AI applications create attack surfaces that span prompts, models, retrieval pipelines, data stores, tools, APIs, and user workflows.

Why Logiciel · 02

Prompt injection can cause systems to ignore intended instructions, expose sensitive information, or misuse connected tools.

Why Logiciel · 03

RAG systems can leak restricted content when retrieval permissions do not match the user and workflow context.

Why Logiciel · 04

AI agents increase impact because generated decisions can lead to real actions across business systems.

Why Logiciel · 05

Sensitive data can appear in prompts, logs, traces, caches, embeddings, model-provider traffic, and generated outputs.

Why Logiciel · 06

Traditional application security remains necessary, but it does not test model behavior, instruction hierarchy, or adversarial AI interactions by itself.

Why Logiciel · 07

AI security controls need continuous testing as models, prompts, tools, data sources, integrations, and vendor behavior change.

What you get

What You Get From Logiciel Enterprise AI Security & Compliance.

We combine application security, AI engineering, identity, data protection, and adversarial testing to reduce risk across the full AI application stack.

AI-specific threat visibility

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

Stronger data boundaries

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

Controlled agent permissions

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

Adversarial AI testing

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

Secure AI integrations

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

Security evidence for review

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

Ongoing AI security validation

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

Trust & security

Enterprise AI Security Across Production AI Systems.

AI Application Threat Modeling

What it meansIdentify threats across prompts, models, retrieval, data pipelines, tools, agents, APIs, and user workflows.

Prompt Injection Defense

What it meansReduce the risk that malicious or untrusted inputs override instructions, expose data, or trigger unsafe actions.

AI Data Security

What it meansProtect sensitive information across prompts, retrieval indexes, logs, model providers, caches, and generated outputs.

RAG and Vector Security

What it meansControl document access, indexing, retrieval permissions, source integrity, and cross-user or cross-tenant leakage.

AI Agent Security

What it meansRestrict tool access, credentials, actions, approval boundaries, and execution scope for systems that can take actions.

Model and API Security

What it meansSecure model endpoints, secrets, service identities, rate limits, integrations, and observability across the AI stack.

Adversarial AI Testing

What it meansTest prompt attacks, data exfiltration, unsafe tool use, policy bypass, poisoned content, and other realistic failure paths.

Trust & security

Enterprise AI Security Models Built Around Your Team.

Dedicated AI Security Engineering Squad

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

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.

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.

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

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

RAG and AI Data Security

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

Included

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

Model API and Integration Security

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

Included

Adversarial AI Testing and Remediation

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

Included

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.

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

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

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

Our Enterprise AI Security Framework.

Map the AI Attack Surface

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

Prioritize Security Requirements

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

Implement and Harden Controls

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

Adversarially Test the System

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

Monitor, Retest, and Maintain Evidence

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

Selected work

Tailored engineering for your industry.

Zeme · Real Estate Cut 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 · Construction Scaled to 7-figure ARR with AI-augmented software teams.

Construction

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

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

Real Estate

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

Resources

Insights for tech leaders.

Framework · PDF

AI Security Vendor Scorecard

Framework · PDF

AI Security Operations Runbook

Framework · PDF

AI Security Decision Framework

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.

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

Patrick Fingles

CEO, Leap

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

Founder & CEO, Zeme

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

Co-Founder, JobProgress

Questions

Frequently asked questions.

What is enterprise AI security?

Enterprise AI security protects AI-enabled 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, model or 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 enterprise 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 the AI System, Not Just the Model.

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 testing needed before production.

Book an Intro Call

Enterprise AI Security & Compliance.

See Logiciel in action.

Why Enterprise AI Security Needs More Than Traditional AppSec.

AI applications create attack surfaces that span prompts, models, retrieval pipelines, data stores, tools, APIs, and user workflows.

Prompt injection can cause systems to ignore intended instructions, expose sensitive information, or misuse connected tools.

RAG systems can leak restricted content when retrieval permissions do not match the user and workflow context.

AI agents increase impact because generated decisions can lead to real actions across business systems.

Sensitive data can appear in prompts, logs, traces, caches, embeddings, model-provider traffic, and generated outputs.

Traditional application security remains necessary, but it does not test model behavior, instruction hierarchy, or adversarial AI interactions by itself.

AI security controls need continuous testing as models, prompts, tools, data sources, integrations, and vendor behavior change.

What You Get From Logiciel Enterprise AI Security & Compliance.

AI-specific threat visibility

Stronger data boundaries

Controlled agent permissions

Adversarial AI testing

Secure AI integrations

Security evidence for review

Ongoing AI security validation

Enterprise AI Security Across Production AI Systems.

AI Application Threat Modeling

Prompt Injection Defense

AI Data Security

RAG and Vector Security

AI Agent Security

Model and API Security

Adversarial AI Testing

Enterprise AI Security Models Built Around Your Team.

Dedicated AI Security Engineering Squad

AI Security Consulting and Team Extension

Outcome-Based AI Security Engagement

Enterprise AI Security & Compliance Services We Deliver.

AI Threat Modeling and Architecture Review

Prompt Injection and LLM Application Security

RAG and AI Data Security

AI Agent and Tool Security

Model API and Integration Security

Adversarial AI Testing and Remediation

AI Security Monitoring and Compliance Evidence

Enterprise AI Security Insights & Frameworks.

AI Attack Surface Model

Agent Permission and Action Matrix

AI Security Regression Model

Our Enterprise AI Security Framework.

Map the AI Attack Surface

Prioritize Security Requirements

Implement and Harden Controls

Adversarially Test the System

Monitor, Retest, and Maintain Evidence

Tailored engineering for your industry.

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

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

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

Insights for tech leaders.

AI Security Vendor Scorecard

AI Security Operations Runbook

AI Security Decision Framework

What our clients say.

Frequently asked questions.

Secure the AI System, Not Just the Model.