Shift AI Security Left: Introducing Lasso’s GitHub Integration

Key Takeaways

• Security teams lack visibility into internal AI development. Many organizations cannot confidently answer where AI is being used, which agents are active, what permissions they hold, or what risks they introduce.
• Shifting AI security left starts in the repository. Lasso’s GitHub integration analyzes codebases early to discover AI components, relationships, and risks before deployment.
• Graph-based visibility changes security operations. Instead of static alerts, teams gain a dynamic map of models, agents, and tools. This gives teams clearer insight into security posture, helps surface threats and misconfigurations faster, reveals potential blast radius across the environment, and enables more accurate, risk-based prioritization
• Continuous AI security is the next phase. As AI systems evolve rapidly, security must become embedded in CI/CD pipelines and modern development workflows. 

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AI Development is Outpacing Legacy AppSec

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As engineering teams race to integrate LLMs, autonomous agents, and MCP servers, the traditional AppSec playbook is falling behind. Developers are embedding AI logic directly into core services, often bypassing security review processes designed for static dependencies and monolithic architectures.

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AI introduces non-deterministic behavior, tool-calling capabilities, and persistent access to sensitive data. Legacy SAST and SCA tools can identify vulnerable libraries, but they were not designed to detect AI-native risks such as:

• Prompt Injection Sinks - Where untrusted external content reaches the model or agent and can manipulate its behavior, decisions, or tool actions.
• Unexpected Code Execution (RCE) - AI agents or code-generation workflows that can execute model-produced commands, scripts, or tool calls without sufficient validation or sandboxing.
• Shadow AI - Unsanctioned models or "orphan" agents living in experimental branches.
• MCP Proliferation - The unmanaged growth of Model Context Protocol servers connecting internal data to external models.
• Additional risks from “OWASP Top 10 for Agentic Applications”

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This leaves organizations exposed before code ever reaches production. As a result, many teams still cannot answer fundamental questions:

• Where is AI being used internally?
• Which agents or AI applications are in production?
• What permissions do they have?
• Which systems or MCP servers are connected to them?
• Where are the real vulnerabilities?
• What should be fixed first?
• How should these systems be protected at runtime?

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Introducing Lasso’s GitHub Integration: Security at the Source

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Lasso’s GitHub integration shifts AI security to the earliest possible stage: the repository. This helps organizations discover and assess AI applications at the source, where teams are building AI copilots, internal assistants, autonomous workflows, and agentic applications.

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By connecting directly to GitHub repositories, Lasso analyzes codebases to identify AI primitives and agentic components such as models, frameworks, MCP servers, tools, and other agentic resources logic. It then builds a Security Graph that maps how these components interact across the application.

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This graph-based approach gives security teams important contextual understanding. Instead of isolated findings, teams can see how models connect to tools, where sensitive data may flow, which agents have excessive autonomy, and where risky trust relationships exist.

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Lasso also performs security assessment against emerging AI risk frameworks, helping identify weaknesses aligned to OWASP AI guidance and MITRE ATLAS techniques. The result is earlier visibility into AI risk: before deployment, before exposure, and before incidents occur.

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Together with strong shift-right and runtime controls, this approach enables organizations to monitor live behavior, enforce guardrails, and respond when models, agents, or integrations act unexpectedly. The most effective programs combine both approaches.

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How It Works

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Getting started is designed to be straightforward. First, organizations install the Lasso GitHub App and authorize access to selected repositories. Teams maintain control over which repositories are included in each scan.

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Once connected, Lasso analyzes repository code to detect AI assets, including:

• AI applications and workflows
• LLM models and SDKs
• AI agents and agentic resources
• MCP servers and integrations

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Lasso then evaluates the codebase for AI-related security gaps and misconfigurations aligned with recognized frameworks such as OWASP and MITRE.

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At the same time, it generates a Security Graph that visually maps relationships between agents, models, tools, and connected services, giving teams a clear picture of how each AI application is assembled and where risk may exist.

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The output is immediate business value:

• Clear visibility into AI applications being built across the organization
• Prioritized security findings for faster remediation
• Better governance over shadow AI and unmanaged agentic projects
• Stronger confidence before AI systems move into production

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What You Get: From Code to AI Observability

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Full visibility into AI applications across repositories

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Lasso gives you a unified, continuously updated view of every AI-powered application being developed across your codebase. Whether it’s production-grade services, internal tools, or experimental projects, you can see where AI is being introduced, how it evolves over time, and who owns it. No more guessing where LLMs are hiding or relying on manual reporting.

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Comprehensive inventory of your AI stack

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Automatically discover and catalog every AI component in use, including:

• Models
• Agents and autonomous workflows
• AI frameworks and MCPs

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This creates a living inventory of your AI ecosystem, making it easy to track adoption, enforce standards, and identify risk concentrations.

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Graph-based mapping of AI interactions

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Go beyond static lists and see how everything connects. Lasso maps each AI application as a dynamic graph, showing relationships between models, agents, tools, and data flows. You can clearly visualize how prompts move through the system, what data is accessed, and where outputs are sent.

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This includes connections to:

• Databases and data warehouses
• External APIs and SaaS tools
• Other agents and services
• Internal systems and pipelines

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Actionable security insights

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Move beyond generic alerts to insights grounded in real execution logic. Lasso analyzes how code actually runs, prioritizing vulnerabilities based on true exploitability rather than static patterns or assumptions. It highlights risky data flows, unsafe prompt handling, and insecure tool integrations, then ranks them by impact and likelihood. This allows security and engineering teams to focus on the issues that pose immediate risk, reduce noise, and accelerate remediation without slowing down development.

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Data privacy & security

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We understand that source code is your most valuable IP. Our scanning process is ephemeral and in-memory; your code is never stored or used for model training. For high-compliance environments, we are moving toward an on-premise analysis model where only the metadata (the security graph) leaves your perimeter, while the raw code never leaves your infrastructure.

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Why This Matters for CISOs and Security Teams

• Finally answer: “How is AI being used in my organization?” Lasso gives CISOs a clear, continuously updated view of where AI exists, how it’s being used, and what it touches, turning a blind spot into something measurable, governable, and actionable.
• Reduce shadow AI risk - Lasso surfaces hidden implementations early, so security teams can enforce policies, prevent data exposure, and regain control without slowing innovation.
• Catch vulnerabilities earlier in the lifecycle (shift left) - Lasso identifies risks directly in the code, so security teams can flag unsafe data flows, prompt injection risks, and insecure integrations before they’re deployed.
• Reduce the cost and complexity of fixing issues later - By catching problems early and providing context-aware insights, Lasso helps teams resolve issues when they’re still simple, contained, and significantly cheaper to fix.
• Align security with modern development practices - Lasso integrates directly into existing DevSecOps workflows, giving security teams the visibility and control they need without introducing friction. The result is a security model that evolves alongside how software is actually being built today.

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Shifting Toward Continuous AI Security

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AI systems don’t behave like traditional software. They continuously evolve with new prompts, models, and integrations, often without a formal release cycle. Security needs to move at the same pace as development, continuously analyzing how AI is built, modified, and deployed in real time.

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Lasso is evolving to integrate directly into CI/CD pipelines, embedding AI security checks into every build and deployment. This ensures that new code, model updates, or agent workflows are automatically analyzed before they reach production. Instead of security being a separate step, it becomes a native part of the delivery process, catching issues at the exact moment they’re introduced.

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Secure Your AI Footprint

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You cannot secure what you cannot see. By shifting AI security left, Lasso provides the visibility required to embrace agentic workflows without sacrificing your security posture.

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Ready to map your AI attack surface? Try the Lasso GitHub Integration today.