Built for the controls regulators expect.
Private LLMs run inside your existing security perimeter — so the model fits inside the audit, residency, and access controls your compliance team already operates.
- Data residency + retention you control
- Audit log on every prompt, retrieval, and response
- Role-based + document-level access enforcement
LLM.co delivers private, domain-specific language models designed for cybersecurity teams, MSSPs, and government defense units.
Domain-Specific Artificial Intelligence (AI) Solutions
LLM.co provides privacy-first language models tailored for cybersecurity professionals, security vendors, and government defense teams, enabling alert triage, log analysis, threat investigation, and report drafting entirely within organizational infrastructure.
Why Security Teams Choose LLM.co
Air-Gapped or VPC-Based Deployments: Models deploy in fully isolated environments or secure VPCs, ensuring sensitive telemetry and threat intelligence remain under organizational control.
Trained on Cybersecurity Language and Logs: Models are pretrained on CVEs, MITRE ATT&CK, threat feeds, incident reports, SIEM logs, and fine-tuned using organizational detection rules and protocols.
Support for Compliance, Forensics, and Reporting: Automates risk assessments, incident summarization, and audit-ready documentation aligned with SOC 2, NIST, and ISO 27001.
Bring Your Own Data (BYOD): Ingests playbooks, runbooks, detection rules, and policy documents through RAG-based architecture for immediate analyst insight.
Reduce Alert Fatigue and Accelerate Triage: Interprets alerts, correlates logs, and generates analysis for junior analyst review and escalation.
No Hallucinations, No Guesswork: Grounded outputs traced to known sources via retrieval-augmented generation ensure operational accountability.
Key Use Cases
Alert Triage and Incident Summarization
Threat Intel Analysis and Contextualization
SOC Playbook & Runbook Automation
Compliance and Audit Documentation
Red Team/Blue Team Support
Executive Risk Reporting
Total Control Over Security Data
Sensitive logs, telemetry, and policies remain on-premises; deployments are containerized, auditable, and support granular access controls integrated with existing SIEM, SOAR, and GRC tools.
Never transmit data externally or phone home
Fully containerized and auditable
Granular access controls by role, team, or user
Compatible with existing security infrastructure
Security-First Infrastructure for Security-First Teams
End-to-end encryption (TLS 1.3, AES-256)
Zero-trust access controls with SSO, MFA, and role-based segmentation
Air-gapped, offline, or VPC deployment options
Compatibility with hardened Linux distros, SCAP, and STIGs
Model Context Protocol (MCP) for explainable, traceable AI
SOC 2 Type II-ready infrastructure and audit trails
Who Uses LLM.co for Cybersecurity
Internal SOC teams at Fortune 1000 companies
Managed Security Service Providers (MSSPs) and MDR vendors
Government and defense cybersecurity units
Cloud security and identity platforms embedding secure AI
GRC and compliance teams managing frameworks at scale
SOC Alert Triage and Threat Intelligence at Machine Speed
Modern SOC teams contend with thousands of SIEM alerts per shift, the majority of which require manual analyst review before escalation or closure. A private LLM deployed on-premises ingests raw alert payloads, correlates them against MITRE ATT&CK TTPs, enriches indicators of compromise from internal threat feeds, and surfaces a prioritized disposition — all without routing telemetry through a public API. Analysts receive a structured triage summary, not a raw log dump, enabling L1 and L2 engineers to focus on genuine threats rather than noise.
Threat intelligence summarization follows the same pattern. The model processes CTI reports, vendor advisories, and dark-web monitoring feeds, then maps findings to your specific asset inventory and detection rules. Because the model is grounded in your RAG-indexed playbooks and runbooks, outputs are traceable to internal sources rather than generic internet data — eliminating the hallucination risk that makes public LLMs unsuitable for operational security decisions.
Incident Response Acceleration and Phishing Analysis
During active incidents, response time is the primary variable security teams can control. A private LLM assists responders by generating step-by-step containment guidance cross-referenced against your SOAR playbooks, drafting executive summaries as the incident evolves, and producing audit-ready timelines for post-incident review. Because the model operates inside your environment, it can read live SIEM context and correlate endpoint telemetry without any data leaving the perimeter — critical for regulated industries and government networks operating under strict data-handling requirements.
Phishing analysis is a high-volume, repeatable task well-suited to agentic automation. A private LLM extracts header artifacts, embedded URLs, and payload indicators from submitted samples, cross-references them against your internal blocklists and external threat feeds, and produces an analyst-ready verdict with confidence scoring. The same pipeline handles log analysis — parsing raw syslog, Windows Event, or cloud-trail streams and surfacing anomalous sequences that static detection rules routinely miss. See offline AI agents for fully disconnected deployment patterns.
Air-Gapped and On-Premises Deployment for Sensitive Security Environments
Public LLM services require outbound API calls, making them incompatible with classified networks, FedRAMP-authorized environments, and any SOC governed by strict data-residency requirements. LLM.co models deploy as containerized workloads on hardened Linux infrastructure, inside air-gapped networks, private VPCs, or on-premises hardware — with no egress and no phone-home behavior. Compatibility with SCAP benchmarks, STIGs, and zero-trust access models means the deployment fits existing security architecture rather than forcing exceptions to it. Full data privacy controls and on-prem options are available across all deployment tiers.
For organizations operating at the edge — forward-deployed defense units, remote network operations centers, or infrastructure without reliable uptime — LLM.co supports offline inference with periodic model updates delivered via secure, signed packages. The model retains full capability without continuous connectivity, ensuring analysts have AI-assisted triage even in degraded network conditions.
Common questions
01Can a private LLM actually reduce alert fatigue in a high-volume SOC?
Yes. A private LLM trained on your detection rules, SIEM schema, and historical alert dispositions can classify and prioritize incoming alerts, correlate related events by shared IOCs, and generate analyst-ready summaries. This moves L1 triage from manual log review to exception-based review of the model's output, materially reducing the volume of alerts that require human attention before escalation decisions are made.
02How does a private LLM integrate with existing SIEM and SOAR platforms?
LLM.co models expose standard REST and gRPC APIs that connect to SIEM platforms via webhook or syslog forwarding and to SOAR orchestrators via native integrations or custom action scripts. The model can receive alert context from the SIEM, query internal knowledge bases via RAG, and return structured outputs — severity, classification, recommended actions — that the SOAR uses to trigger automated playbook steps or route to the appropriate analyst queue.
03Is it safe to run LLM-assisted threat intelligence on sensitive or classified data?
When the model is deployed on-premises or in an air-gapped environment, sensitive telemetry, classified indicators, and proprietary threat intelligence never leave the organizational boundary. Unlike public AI services, there are no outbound API calls and no model-training feedback loops that could expose sensitive data. Deployment within FedRAMP, FISMA, or NIST 800-53 controlled environments follows the same hardening baseline applied to other on-premises security tooling.
04What security-specific knowledge does the model bring out of the box?
LLM.co models for cybersecurity are grounded in CVE databases, the MITRE ATT&CK and D3FEND frameworks, STIX/TAXII threat formats, common SIEM log schemas, and incident response methodology. Organizations then fine-tune on their own detection rules, runbooks, and historical incident data through a supervised fine-tuning or RAG layer, so the model reflects both general security knowledge and institutional context specific to your environment.
05Can the model support both red team and blue team workflows?
Yes. Blue team workflows — alert triage, incident summarization, log parsing, compliance reporting — are the primary production use case. Red team and purple team applications include generating adversarial test cases based on MITRE ATT&CK techniques, drafting realistic phishing scenarios for awareness training, and reviewing detection coverage gaps against known TTPs. Both operate entirely within the private deployment, so offensive simulation data never touches external infrastructure.
Private AI On Your Terms
Tell us your use case and constraints — on-prem, cloud, or edge — and we'll map a compliant deployment within one business day.
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