AI Coding Risks and How to Mitigate Them

The New Risk Landscape

AI coding tools like Cursor, Claude Code, and GitHub Copilot are transforming software development. Teams report significant productivity gains. But these tools also introduce risks that traditional development workflows weren't designed to handle.

Key Risks

1. Unintended Code Deletion

AI agents can delete files they believe are unused. This happens quickly and often without explicit confirmation. In enterprise codebases with complex dependencies, "unused" code may actually be critical.

2. Cascading Changes

A single prompt can trigger modifications across dozens of files. The AI makes decisions about how to implement changes, and those decisions may conflict with existing architecture or coding standards.

3. Subtle Bug Introduction

AI-generated code often compiles and appears correct but contains subtle bugs. These can be harder to detect than obvious errors because they pass initial review and testing.

4. Loss of Context

During rapid AI-assisted iteration, developers may lose track of what changed and when. Without proper tracking, reverting to a known-good state becomes difficult.

5. Compliance and Audit Gaps

Many organizations require audit trails for code changes. AI-assisted development can move faster than traditional commit-based tracking, creating gaps in the audit trail.

Mitigation Strategies

Continuous State Capture

The most effective mitigation is automatic, continuous capture of code state. This provides a safety net that doesn't depend on manual intervention.

mrq provides this capability:

npm install -g mrq-cli@latest
mrq login
mrq watch --daemon

Every meaningful change is captured automatically. Developers can restore to any point without losing work.

Pre-Session Checkpoints

Before starting AI-assisted work sessions, establish a clean checkpoint. This ensures you always have a known-good state to return to if the session goes wrong.

Review Before Commit

Use tools like the mrq dashboard to review what changed during an AI session before committing to git. This maintains audit trail integrity.

Scope Constraints

Consider constraining AI agents to specific directories or file types for sensitive codebases. This limits the blast radius of unexpected changes.

The Business Case for Protection

The cost of AI coding mistakes scales with codebase complexity:

• Developer time spent debugging and recovering
• Delayed releases and missed deadlines
• Potential production incidents from undetected bugs
• Compliance violations from audit gaps

Automatic state capture is cheap insurance against these outcomes.

Implementation

mrq can be deployed across development teams with minimal friction:

1. Install the CLI: npm install -g mrq-cli@latest
2. Authenticate: mrq login
3. Start watching: mrq watch --daemon

The tool runs silently in the background. Developers continue working normally. Recovery is available when needed.

Learn More

• Security overview
• Enterprise features
• For teams
• Documentation