Everyone advertises AI. LimaCharlie built an Agentic SecOps Workspace.

Transparency is a core value for LimaCharlie. It’s reflected in our high-visibility platform, unopinionated integrations, and publicly available pricing structure. So rather than vaguely claiming AI capabilities, as many vendors do, we’ll explain how LimaCharlie facilitates agentic SecOps and why it matters to you.

The Agentic SecOps Workspace is a security platform where AI doesn’t just assist operators, but operates alongside them. This differentiates LimaCharlie from AI-SOCs, which largely parse logs and advise analysts on next steps. 

An AI-SOC assists and advises. The Agentic SecOps Workspace operates.

From AI features to agentic operations

Most security platforms treat AI as a bolt-on feature. It’s another proprietary tool, wedged into their closed system and trained on opaque data.

The Agentic SecOps Workspace (ASW) takes a different approach.

The ASW allows AI agents to access the same integrated APIs as operators. They interact with the platform using the same tools, telemetry, detections, and response mechanisms as humans. There is no separate “AI mode,” no hidden execution layer, and no privileged black box.

The only distinction between a human operator and an agentic one is policy. Administrators set access controls and guardrails to define the limit of AI interaction in their security operations.

This is the key architectural shift: AI operates as part of the security fabric, not layered on top of it.

MCP Server: connective tissue between AI and security data

LimaCharlie’s Model Context Protocol (MCP) server allows AI agents to operate inside real security workflows.

Through MCP, AI agents can:

• Query historical telemetry from any sensor

• Investigate detections in real time

• Execute response actions

• Correlate signals across detections, logs, and organizations

Because MCP is built on LimaCharlie’s existing architecture, AI agents inherit the same visibility and controls as human operators. This enables AI automation that is not only powerful, but auditable, observable, and reversible.

Organizations can set the boundaries of autonomous AI operations while achieving 24/7 coverage. Human operators handle sensitive operations, all others run at wire speed.

API-first AI: agentic operators with real capabilities

The Agentic SecOps Workspace is a powerful and natural evolution of LimaCharlie’s API-first approach to security. By building our SecOps Cloud Platform to be unopinionated, transparent, and highly integrable, we laid the foundation for agentic AI to scale with security operations.

Through our AI Agent Engine and MCP integrations, agentic operators can invoke:

• Detection & response workflows

• Sensor and org management

• Incident investigation

• Multi-tenant operations across customers or business units

For MSSPs and large enterprises, this is a game changer. Managing AI across multiple organizations is no longer theoretical, it’s an operational reality. Agentic operators can be scoped per tenant, per capability, and per workflow.

Natural language, structured execution

Agentic operators are capable of executing complex workflows, but they don’t require complex inputs.

Integrations with platforms like Claude Code allow teams to govern how AI investigates, responds, and reports through prompts in plain language or markdown.

A request like:

Retrieve recent detections that require further investigation.

Can trigger a structured sequence such as:

• Determine the appropriate timeframe

• Validate sensor health

• Analyze detection confidence

• Search for related IoCs

• Investigate persistence or beaconing

• Isolate accounts or files with an unacceptable risk profile

• Generate a summarized report with recommendations

What once required scripts, queries, and manual correlation can now happen in seconds. Everything AI performs is visible to the operator, giving security teams critical visibility into why decisions are made and what triggered them.

Native AI capabilities 

The Agentic SecOps Workspace includes native AI capabilities that operate independently from LLMs imported via the MCP server.

One example is Community Rules, where AI translates third-party and open-source detection rules into LimaCharlie Query Language (LCQL). This feature lets analysts review, modify, and deploy rules instantly without sacrificing transparency or control.

This same philosophy applies across the platform:

• AI speeds operations, but does not obscure

• Automation accelerates processes, but does not override governance

• Operators (human or agentic) remain accountable

• Faster response and greater control, not empty AI hype

The Agentic SecOps Workspace is not about AI taking over all security operations. It’s about removing friction, increasing efficiency, and expanding analysts’ capabilities. 

By giving AI agents the same operational footing as humans, security teams gain speed, scale, and consistency without surrendering visibility or trust.

The Agentic SecOps Workspace represents a practical, transparent approach to AI in security, one designed for real operators, real environments, and real outcomes.

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