Security-First AI Agents
Agents built for high-stakes, regulated workflows.
Grounded answers, with citations.
Retrieval looks across your documents, the model composes the answer, and every claim is anchored to a source your reviewers can verify.
- Cites the exact source for every assertion
- Access-checked against the asking user
- Logged end-to-end for audit + improvement
LLM.co delivers private, secure AI agents designed to operate entirely within your infrastructure—on-premise or in a VPC—without exposing sensitive data to public APIs. Each agent is domain-tuned, role-restricted, and fully auditable, enabling safe automation of high-trust tasks in finance, healthcare, law, government, and enterprise IT.
Enterprise AI Features
LLM.co delivers private, task-specific AI agents with built-in security, observability, and compliance at their core. Designed for high-stakes environments in finance, healthcare, law, government, and enterprise IT, our security-first agents work within your infrastructure to automate complex tasks—without ever compromising sensitive data, compliance obligations, or control.
Why Security-Conscious Teams Use LLM.co for AI Agents
Our AI agents run inside your secure perimeter—on-premise or in a VPC you control. There's no callout to public APIs, no cross-tenant risk, and no exposure of sensitive workflows. You define what data agents can access, how they behave, and what guardrails they operate under.
Each agent is sandboxed, scoped, and governed with clear permissions. Whether you're deploying an agent for compliance monitoring, customer service, or internal automation, you can ensure that every action is traceable, auditable, and compliant with your organization's policies.
Our AI agents aren't general-purpose bots—they're trained on your proprietary knowledge, operational rules, and structured data. We connect them to internal systems (CRM, file repositories, support platforms, databases) via secure, agentic interfaces—so they can take action intelligently, not just respond with text.
Every agent workflow is grounded in retrieval-augmented generation (RAG), task-specific constraints, and verifiable data sources. That means no hallucinations, no guesswork, and no rogue behavior—just reliable, contextual execution you can trust in production.
All agent activity is logged, versioned, and auditable. Built-in governance tools allow security and compliance teams to review output, monitor usage patterns, and enforce behavioral policies. LLM.co supports Model Context Protocol (MCP) for full explainability of every AI decision.
Key Use Cases
Deploy AI agents to monitor logs, summarize threat alerts, and flag anomalies in real time—without exposing telemetry to third-party tools. Connect to SIEMs, EDRs, or ticketing platforms for secure triage automation.
Scan internal documentation, extract violations, generate reports, and monitor policy changes across departments—all handled by AI agents that understand regulatory frameworks and your internal standards.
Build agents that manage helpdesk queries, provision access, generate internal reports, or route issues to the right teams—securely integrated with your ITSM stack.
Deploy AI agents for customer support, onboarding, or quoting that stay strictly within pre-approved language, workflows, and data boundaries—ensuring consistency, compliance, and safety.
Create agents to query internal knowledge bases, generate documents, assist HR, or summarize meetings—scoped to departments, teams, or roles with tight access control and full observability.
Designed for Private Deployment, Secure Execution, and Policy Enforcement
LLM.co is built from the ground up to meet the security and control requirements of regulated and high-trust environments. Every AI agent runs inside a hardened, auditable runtime designed for enterprise-grade governance.
Secure API and tool integrations (with scoped access)
Role-based identity enforcement via SSO, OAuth, and RBAC
Encrypted data pipelines and vector stores
Model Context Protocol (MCP) for traceability and explainability
Logging, monitoring, and alerting on agent behavior
Guardrails for prompt injection, output filtering, and task limits
Who Uses Security-First AI Agents from LLM.co
Healthcare teams automating patient communication and internal triage
Law firms deploying paralegal-level agents for doc review and summarization
Financial institutions using AI for compliance and customer insights
Government agencies building tightly scoped AI functions for internal ops
Enterprise IT & security teams deploying agents to reduce alert fatigue and automate incident response
Build Agents That Act with Intelligence—and Integrity
Most AI agents are built for convenience. Ours are built for trust. With LLM.co, your AI agents run where you need them, operate how you define them, and access only what you allow.
Hardened Against OWASP LLM Risks
The OWASP Top 10 for LLM Applications identifies prompt injection as the leading vulnerability in production agentic systems—followed by excessive agency, sensitive data disclosure, and insecure tool-calling. LLM.co addresses each risk at the infrastructure layer, not just in the prompt. Every agent runs inside an isolated sandbox environment with strict tool scoping, so a compromised instruction cannot escalate privileges or reach systems outside the agent's defined boundary. Input and output guardrails screen every exchange before the model acts, and agentic workflows are constrained by pre-approved intent graphs that prevent runaway execution.
Enterprises in regulated verticals need more than vendor assurances. LLM.co supports data privacy obligations under HIPAA, GDPR, and SOC 2 by ensuring all inference, retrieval, and tool calls remain within your network perimeter. No telemetry leaves your environment, no third-party model provider receives your prompts, and encryption is enforced at rest and in transit across all vector stores and data pipelines.
Compliance-Grade Audit Logging and Governance Controls
Regulated teams require a complete, tamper-evident record of every agent action—not just logs, but structured traces that capture the input, the retrieved context, the tool calls made, and the final output. LLM.co's audit layer writes immutable, timestamped records for every agent turn, giving your security and compliance teams the evidence chain needed for internal reviews, external audits, and incident response. Role-based access control (RBAC) governs which identities can trigger which agent capabilities, enforced via your existing SSO and OAuth provider so access grants stay consistent with your broader IAM posture.
Policy enforcement extends beyond logging. Governance controls allow you to define behavioral guardrails at the agent level—restricting output categories, enforcing response templates, and requiring human-in-the-loop approval for high-risk actions. Whether you operate under FedRAMP constraints, financial services data-handling rules, or hospital-grade PHI protections, the control plane adapts to your policy framework. Pair with on-prem deployment for air-gapped or hybrid environments that prohibit any cloud egress.
Common questions
01How do your secure AI agents defend against prompt injection attacks?
LLM.co applies multi-layer prompt injection defenses aligned with OWASP LLM01 guidance. Input screening classifies user prompts and retrieved context before they reach the model, structured delimiters prevent instruction smuggling from external data sources, and tool-call action screening validates each proposed action against the original authorized intent. Agents cannot be redirected by adversarial content embedded in documents, emails, or API responses they process.
02What does "sandboxed" mean for an AI agent in a regulated environment?
Each LLM.co agent runs in an isolated execution environment with no lateral access to other agents, services, or data stores outside its explicitly scoped permissions. Tool integrations are credentialed individually with least-privilege API keys, and the agent runtime cannot initiate outbound connections beyond its defined allow-list. This isolation contains the blast radius of any misuse or misconfiguration to a single, bounded context.
03Can your agents meet HIPAA, GDPR, and SOC 2 requirements simultaneously?
Yes. LLM.co's on-prem and VPC deployment model means protected health information (PHI) and personal data never transit a third-party model provider's infrastructure—a baseline requirement for HIPAA Business Associate Agreement coverage and GDPR data-residency compliance. Immutable audit logs, encrypted data pipelines, and access controls provide the evidence artifacts that SOC 2 Type II audits require. Your legal and compliance team defines the policy; LLM.co enforces it at the infrastructure layer.
04How is RBAC enforced across agent capabilities?
Access to agent tools, knowledge bases, and action endpoints is governed by role assignments synced from your existing identity provider via SSO and OAuth. A finance analyst role might read from a specific data source and generate reports but cannot trigger payment workflows—that boundary is enforced by the agent runtime, not just by the prompt. Privilege escalation through conversation is architecturally prevented because tool access is validated against the caller's identity on every request.
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.
Book a Call