The SpartanX Difference: How We Use Agentic AI to Automate Security
Beyond the Buzzword: This is Agentic AI.
While other tools add AI as a feature, we built our entire platform on it. SpartanX doesn't just use AI to find problems—we use an autonomous workforce of AI agents to solve them. Discover the engine that powers the future of security operations.
Our Philosophy: AI as the Operating System, Not an App
From day one, SpartanX was designed as a native Agentic AI platform. Our goal wasn't to build a better scanner with a chatbot bolted on; it was to create a new category of security platform that could autonomously execute complex tasks, just like a human expert.
This required a fundamentally different architecture built on three core pillars: a deep contextual brain, a sophisticated method for reasoning, and a versatile workforce of specialized agents.
1. The Brain: Our Ontology-Driven Knowledge Graph
An AI is only as smart as the data it can access and understand. Generic Large Language Models (LLMs) lack the specific context of your business, your code, and your security posture. This is why they often fail at complex security tasks.
Our solution is a proprietary, ontology-driven knowledge graph that acts as the central brain for our entire platform.
What it is:
Think of it as a dynamic, real-time map of your entire security ecosystem. It doesn't just store data; it understands the relationships between everything: code repositories, cloud assets, vulnerabilities, developer identities, business-critical applications, and historical findings.
How it works:
Through our MCP-powered integrations, the knowledge graph continuously ingests and correlates data from all your tools. It knows which code commit introduced a vulnerability, which developer wrote it, what business application it affects, and what its potential attack path looks like.
Why it matters:
This deep, contextual understanding is the fuel for our AI. It allows our agents to reason with the same nuanced perspective as a senior security architect, ensuring their actions are precise, relevant, and effective.
We don't simply ask an LLM, "Is this code vulnerable?" That approach leads to generic, unreliable results. Instead, we've developed a sophisticated inference engine that uses the knowledge graph to engineer multi-layered, advanced prompts for our agents.
When a task is initiated, our engine queries the knowledge graph to build a rich, contextual payload:
The specific task (e.g., "Validate this potential SQL injection flaw")
The relevant data (e.g., the code snippet, the application it belongs to)
The deep context (e.g., "This application processes PCI data, and the developer who wrote this code has a history of similar mistakes")
The required tools (e.g., access to a SAST scanner, a code interpreter, and the Defend remediation model)
This process transforms a simple request into a detailed mission briefing, giving our specialized AI agents everything they need to perform at an expert level and produce standardized, secure outcomes.
2. The Method: Advanced Inference & Contextual Prompting
3. The Workforce: A Team of Specialized AI Agents
SpartanX doesn't rely on a single, monolithic AI. We've built a collaborative workforce of specialized agents, each trained for a specific function in the security lifecycle. An agent that excels at penetration testing (Offense) has a different skillset than one that writes code patches (Defend).
When a plan is created, the right agents are selected for the job, each equipped with the specific tools they need.
Agent Tools & Capabilities:
Code Scanners & Analyzers
Exploit Frameworks
Data Correlation Engines
Remediation & Code Generation Models
These agents work together, passing information between each other to execute complex, multi-step plans autonomously.
From Intent to Outcome: The Autonomous Workflow in Action
Whether you type a command in our chat interface or a scheduled task kicks off, the process is the same. This is the core operational loop of the SpartanX platform.
Intent Recognition
The platform understands your goal (e.g., "Find and fix all critical cross-site scripting vulnerabilities in the 'checkout-api' repository").
Autonomous Planning
Our AI Planner consults the knowledge graph and creates a multi-step execution plan. It breaks the goal down into logical steps, like:
Agent Deployment
The Planner assigns the right specialized agents and tools for each step of the plan.
Autonomous Execution
The agents execute the plan from start to finish, collaborating and adapting as they work, requiring no human intervention.
This agentic workflow is how we operationalize your entire security program—from DevSecOps and Automated Remediation to Continuous Red Teaming, Compliance Reporting, and Security Investigations. You provide the intent; our AI workforce delivers the outcome.