AI Code Security Analysis Enterprise Codebases
Agentic AI Platform That Transforms Source Code Into Verified Security Intelligence
Client
Confidential cybersecurity vendor
Headquarters
United States
Timeline
Active development
Technology
AI AGENTS
Fewer edge-case failures in client testing
Reduced false positives via runtime validation
Scales across multi-service enterprise codebases
The challenge
Diverse enterprise architectures made traditional security analysis brittle, inconsistent, and unreliable at scale
The solution
Agent-driven workflows with graph-based system modeling, runtime validation, and adaptive failure handling
The results
- Fewer edge-case failures in client testing
- Reduced false positives via runtime validation
- Scales across multi-service enterprise codebases
AI Code Security Analysis With Fewer Edge-Case Failures
This agentic platform produced fewer edge-case failures in client testing. Modern enterprises operate large, complex codebases spanning multiple services, frameworks, and authentication models. Traditional security analysis, combining static analysis tools, manual review, and isolated LLM calls, struggles to deliver validated and scalable results across diverse enterprise environments.
This platform enables organizations to ingest their repository (source code and documentation) and automatically transform it into validated security insights with actionable remediation guidance. It moves beyond simple vulnerability detection: identifying system architecture, analysing APIs, validating security findings at runtime, and providing code-level fixes, all orchestrated through intelligent agent workflows built on the Claude Agent SDK.
6 capabilities carried the build
Design and deploy an agent-driven security analysis platform capable of ingesting enterprise repositories and producing validated, actionable security insights. The core challenge was that enterprise codebases vary widely in architecture, authentication patterns, and framework choices. Any hardcoded workflow breaks quickly. The solution required adaptive, agentic execution that could reason about each codebase dynamically.
01
Repository ingestion pipeline with graph-based architecture modelling (Neo4j)
02
Agentic structural analysis engine with MCP-integrated tool access
03
Vulnerability identification covering authentication, privilege escalation, data exposure, and API misuse
04
Runtime validation via Playwright-based API tests and OWASP ZAP integration
05
Adaptive failure-handling loop for automatic test refinement
06
Actionable remediation reports with code-level fixes and configuration guidance
The stack behind it
Claude Agent SDK, TypeScript, NestJS, Neo4j, Playwright
ClaudeAI Orchestration
Claude Agent SDK (Anthropic) with MCP-based tool integrations
Graph Database
Neo4j for graph-based system modelling and contextual architectural reasoning
Workflow Orchestration
Temporal for distributed, durable workflow execution across analysis phases
Runtime Validation
Playwright for automated API test execution; OWASP ZAP for runtime security verification
Backend
NestJS (TypeScript) for backend services and API layer
Frontend
React dashboard for workflow interface and vulnerability reporting
Infrastructure
Dockerized execution environments for safe, isolated analysis runs
Our development process
Repository Ingestion and System Modeling
The platform identifies services, modules, and architectural components, extracts API endpoints across frameworks, and correlates documentation context with actual code behaviour. A graph-based architecture powered by Neo4j models the entire application surface, linking services, APIs, authentication mechanisms, and vulnerabilities, giving the agent a full picture of the system before analysis begins.
Agent-Driven Structural Analysis
Rather than relying on hardcoded workflow branches, the platform uses agentic execution to dynamically identify application structure, infer authentication and authorization mechanisms, and understand API workflows and dependency chains. The agent uses controlled tool access including MCP-integrated tools to read, reason, edit, and validate code context safely within execution boundaries, which reduced edge-case failures across different enterprise patterns in client testing.
“EdgeFirm fundamentally changed how we think about security analysis. Their agentic platform found vulnerabilities, modeled our system architecture, validated findings at runtime, and delivered code-level fixes. The reduction in false positives alone justified the investment.”
What changed after launch
Fewer Edge-Case Failures
Adaptive agentic workflows replaced brittle sequential LLM pipelines. The client's own tests showed fewer failures caused by architectural variation across enterprise codebases.
Fewer False Positives
Runtime validation via Playwright and ZAP ensures only exploitable vulnerabilities surface. Engineering teams act on real threats, not theoretical findings.
Consistent Cross-Architecture Analysis
The platform handles diverse authentication models, framework choices, and service architectures without requiring manual workflow customization per client.
Evidence-Backed Remediation
Every validated vulnerability is paired with code-level fixes, configuration guidance, and authorization hardening recommendations, shortening the remediation cycle.
By combining system-level modelling, adaptive agent-driven workflows, and runtime validation, the platform transforms raw source code into verified, actionable security intelligence. The result is a repeatable security process that gives engineering teams clear findings and helps them fix real vulnerabilities, whatever the underlying enterprise architecture.
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