SANDS Lab builds cybersecurity-specialized LLMs, RAG, and AI agents in an on-premise environment, so you can use generative AI without sending sensitive security data outside your organization. By combining security domain knowledge with AI development capability, we connect your internal data, threat intelligence, and security systems to AI.
Security data can't simply be sent to external AI services.
Threat analysis results, malware, source code, internal incident information, security policies, and operating manuals are all sensitive data that's hard to move outside your organization. But most general-purpose generative AI assumes calls to an external service, making it difficult to connect directly to the core data of your security work.
The risk of moving sensitive data outside
Sending security analysis data and internal documents to an external AI service can raise data protection and control issues.
Limited use in closed or internal networks
In environments with restricted internet access, applying external generative AI directly to your work is difficult.
General-purpose LLMs don't fully understand security context.
Without enough grounding in cybersecurity terminology, threat data, and internal procedures, an LLM's usefulness for real security work drops.
Control aligned to corporate policy
You need to control what data the AI can access and what answers it can provide, according to your organization's internal standards.
A security company that builds its own AI.
SANDS Lab's strength isn't simply taking an off-the-shelf LLM and using it. Built on years of accumulated cybersecurity data and analysis experience, we design the AI model, RAG, search, agent, and security-system integration as a single, unified structure.
LLM / sLLM development & optimization
Builds and optimizes small language models and security-specialized AI models fit for your purpose and operating environment.
RAG built on your internal data
Designs a structure that searches and references internal knowledge — security manuals, threat intelligence, reports, incident data — to generate answers.
AI agents for security work
Can extend beyond simple Q&A into an agent structure that performs security workflows — search, analysis, summarization, lookup, and more.
Connecting AI to your existing security systems
Connects internal systems and security data via API, search, RAG, and other methods to build an AI environment usable in real workflows.
Bringing security context to general-purpose AI.
General-purpose LLMs are excellent at natural language processing, but they don't automatically understand an organization's security data and threat context. Drawing on our experience in cyber threat analysis and security operations, SANDS Lab connects domain knowledge and data so the LLM can search and interpret the information actual security work requires. (Threat intelligence · malware analysis data · IoCs · vulnerability information · source code · incident response reports · SOC operations data · corporate security policy · response manuals · asset information)
SECURITY KNOWLEDGE
Security expertise and threat data
AI ENGINEERING
LLM / sLLM / RAG / Agent development
DATA PIPELINE
Search, refinement, and connection of internal enterprise data
SECURITY INTEGRATION
Integration with existing security systems
Connecting internal enterprise data with AI in a single secure environment.
Internal security data
Uses internal security data — threat intelligence, reports, source code, security manuals, incident response documents, SIEM/SOC data, and more.
Search & build context
Searches internal information relevant to the question and builds the context the AI references in its answer.
Analyze & generate
Generates summaries, analysis, explanations, and answers based on the retrieved information and the question.
Access-scope control
Restricts the data and systems the AI can access according to your organization's policy.
Repetitive security work, now assisted by AI.
Explaining source code & assembly analysis
Explains, in natural language, how hard-to-analyze code behaves and what it means from a security perspective, supporting the analyst's understanding.
Threat intelligence search
Searches threat information — IoCs, attack groups, malware, campaigns — in natural language, and looks up related internal data at the same time.
Summarizing & reviewing security reports
Summarizes long threat reports and incident analysis materials, quickly organizing the key risks and response details.
Internal response manual search
When a hacking incident occurs, searches internal manuals and response procedures to provide response information suited to the situation.
SOC analysis support
Searches and summarizes security events and related information so analysts can quickly grasp the context they need to make a judgment.
An AI architecture that isn't locked into a single model.
New commercial and open LLMs are appearing rapidly in the generative AI market. Rather than locking in a single model as a fixed product, SANDS Lab aims for an integrated AI structure that can selectively connect on-premise sLLMs with external, commercial LLMs based on your data sensitivity and operating environment. We can design integration structures for commercial and open LLMs; when connecting to an external LLM, this is applied according to your organization's data-export policy and security requirements.
Sensitive data, kept local
Data that's hard to move externally is processed by the model and RAG in your on-premise environment.
Connect to AI when you need it
Designs an integration structure that lets you use a range of LLMs based on your business purpose and policy.
What matters is data control, not the model
The key is designing which data goes to which model in line with your organization's policy.
For environments where security data needs to stay in-house.
SOC & incident response teams
Connects threat intelligence, events, and incident response documents to AI to support analysis, search, and summarization.
Public sector & closed networks
Builds an AI structure based on internal data for environments that can't use external generative AI directly.
Security research & analysis teams
Combines malware, source code, vulnerabilities, and threat data with AI to support analysts' research and investigation work.
Enterprise security knowledge search
Connects internal manuals, policies, reports, and incident response procedures through RAG so you can find the information you need quickly.
Deploying AI within your security perimeter.
Operates on internal infrastructure
AI environments can be built on your internal infrastructure so sensitive data never leaves your organization.
Works in closed-network environments
Designs a structure that connects internal data and AI even in secure environments with restricted internet access.
Data access-scope control
Designs what data and systems RAG and the agent can access based on your work permissions and security policy.
Integrates with existing security systems
Designs an integration structure suited to your system types — SIEM, SOAR, ticketing, threat intelligence, internal databases, REST APIs, and more.
From cybersecurity data to the AI model.
SANDS Lab doesn't stop at simply connecting to an external LLM API. We research cybersecurity data building, AI model training, RAG, agents, and threat analysis technology together, developing an AI structure specialized for security work.
CYBERSECURITY DATA
Builds cybersecurity data accumulated through threat analysis and security operations.
MODEL TRAINING
Trains AI models tailored to the security domain.
sLLM / LLM
Configures sLLMs/LLMs suited to your purpose and operating environment.
RAG
Designs a structure that searches and references internal data for use in answers.
AI AGENT
Extends into an agent that performs security workflows — search, analysis, summarization, lookup, and more.
SECURITY WORKFLOW
Integrated into and used within real security workflows.
Frequently asked questions about Cybersecurity sLLM.
How is Cybersecurity sLLM different from general-purpose AI services like ChatGPT?
General-purpose generative AI offers broad knowledge and strong language capabilities, but it does not inherently understand your organization’s internal security data or operational context. SANDS Lab combines internal data, RAG, and security-domain knowledge to build an AI environment designed for real-world security operations.
Why use an on-premises approach?
Data such as malware analysis results, internal incident information, source code, and security manuals may be difficult to send to external AI services. An on-premises approach allows this data to be processed within your organization, giving you greater control over how it is handled.
Are we limited to SANDS Lab’s sLLM?
No. Depending on your organization’s security policies, data sensitivity, and business requirements, we can design an architecture that combines an internal sLLM with a range of commercial and open LLMs. The specific models and integration scope are determined on a project-by-project basis.
Can it search internal documents and security data?
Using a RAG architecture, the system can search internal security documents, manuals, threat intelligence, reports, and other sources, then use the information relevant to the query to generate its answer.
Can it integrate with our existing SIEM and other security systems?
We can design an architecture that connects your existing security systems to the AI through APIs, search interfaces, databases, and other available integration methods. The integration scope depends on your environment and the interfaces provided by each system.
Can it be deployed in a closed network?
It can be deployed in environments with restricted external connectivity using an on-premises configuration. For fully closed networks, the model deployment and update process, as well as the required infrastructure, are designed separately based on your environment.
Start using AI while keeping your security data protected.
Drawing on SANDS Lab's security data and AI development capability, we'll design an on-premise sLLM, RAG, and AI agent structure fit for your organization together.
How does this product behave in your environment?
Whether you're exploring, evaluating, or rolling out, you connect directly with a SANDS Lab solutions engineer. Clear every question before contract — that's the point.
Product evaluation & PoC
Real-data PoCs, technical deep-dive sessions, and custom integration scoping. Everything you'd need to validate technical fit before the paperwork starts.
Technical collaboration & licensing
If you want to use the product in an academic benchmark or co-authored paper, we support research licenses and the underlying datasets. Co-authorship is on the table.