AI coding at scale, without losing control.

AI-generated code often contains hidden issues—such as security vulnerabilities, licensing conflicts, and architectural inconsistencies. AI tools lack context about your internal codebase, architecture, and business rules, which can result in hard-to-maintain systems and increased technical debt. Without oversight, productivity gains can quickly be outweighed by rework and risk.

AI-generated code introduces serious security risks because it’s often trained on flawed or outdated public code. It may include insecure coding patterns or even hallucinate components—like made-up package names—which attackers can exploit.

In addition, because AI lacks awareness of your architecture and policies, it can accidentally bypass security protocols or introduce unsafe dependencies.

LLMs can generate and even help review code—but they’re fundamentally limited. They rely on associative, pattern-based reasoning, which makes them fast but not always accurate. They can miss critical flaws because they don’t have full system context or understanding of your architecture. That’s why you need deterministic analysis tools and expert reviews to properly govern AI-generated code.

AI coding assistants hold great promise for accelerating software development and modernizing legacy systems. However, they also introduce significant challenges related to code quality, security, legal issues, and efficiency. Enterprises adopting these tools need comprehensive solutions to manage these risks effectively.

Whether formally allowed or not, most developers are already using AI coding assistants like GitHub Copilot or ChatGPT to some extent. Even in organizations that discourage it, developers often find ways to use these tools because they significantly speed up tasks like writing test code, generating boilerplate, or exploring unfamiliar code.

AI-generated code is difficult to distinguish from human-written code, making it challenging to track its usage across the software portfolio.

Vibecoding refers to a style of AI-assisted development where the user builds software by writing natural language prompts instead of writing code manually.

This approach sits at the far end of the AI coding spectrum. It’s often used for quick prototyping but is not suited for production environments, especially in complex systems. That’s because vibecoding typically lacks essential software engineering principles like architecture, reuse, testability, and encapsulation.

We see that enterprise and government clients rarely use vibecoding beyond experimentation, precisely because of these limitations. It can be fun and enabling, but without guardrails, it introduces significant risks.

An MCP (Model Context Protocol) server extends the capabilities of a large language model (LLM) by allowing it to execute external code or connect to external tools. This means an LLM can accomplish tasks that go beyond what it can do on its own—such as analyzing real code, running computations, or integrating with other systems.

Sigrid’s MCP server connects large language models (LLMs) directly to Sigrid’s software analysis engine, allowing models to retrieve deterministic, fact-based insights about software quality, risks, and vulnerabilities.
Instead of relying on probabilistic pattern matching alone, the LLM can query Sigrid through the MCP server to obtain real, data-driven results about the code being analyzed.

The MCP is integrated with AI Coding Assistants. Some IDEs are AI Coding Assistants (e.g. Windsurf, Cursor), some IDEs have plugins for AI Coding Assistants (e.g. VSCode with the Github Copilot plugin, JetBrains). Most IDEs have options today for integrating with some AI Coding Assistant and most AI Coding Assistants support MCP.

Can it connect to any IDE? this depends on the AI Coding Assistant.