AI Usage & Safety

## AI Usage & Safety

SyntaxValid uses AI to assist, not replace, engineering judgment.

This page explains how AI is used safely, how risks are controlled, and how teams retain full ownership and control over their code.

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## What AI is used for

AI is used selectively to:

- Assist with issue explanation

- Generate fix suggestions (Fix with AI)

- Analyze AI-generated code risk signals

- Support prioritization and clarity

AI does not make autonomous decisions.

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## What AI is not used for

AI is not used to:

- Automatically modify code

- Bypass policies or reviews

- Execute or deploy code

- Make merge decisions

- Train models on customer code by default

Human oversight is always required.

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## Human-in-the-loop by design

All AI-assisted features are human-in-the-loop.

This means:

- Suggestions are reviewable

- Changes require explicit approval

- Results are explainable

- Responsibility remains with the team

SyntaxValid treats AI as a tool, not an authority.

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## Data handling for AI features

When AI features are invoked:

- Only the minimum required context is used

- Requests are scoped to the active task

- No background or silent processing occurs

- Code is not reused outside the request

AI usage is explicit and intentional.

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## Model training and data ownership

By default:

- Customer code is not used to train AI models

- No long-term model memory is created from user data

- Code ownership remains with the customer

SyntaxValid does not claim rights over analyzed code.

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## AI-generated code risk awareness

SyntaxValid explicitly analyzes AI-generated code as a risk signal.

This helps teams:

- Identify fragile or overconfident patterns

- Review AI-assisted changes more carefully

- Balance speed with reliability

AI contribution is measured, not penalized blindly.

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## Safety guarantees

AI features are designed to be:

- Deterministic in scope

- Policy-aware

- Reviewable and reversible

- Aligned with existing workflows

AI never bypasses security controls.

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## Transparency and explainability

Every AI-assisted output includes:

- Context of why it was generated

- Scope of affected code

- Clear explanations

There are no opaque or hidden AI actions.

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## Responsible AI principles

SyntaxValid follows responsible AI principles:

- Least privilege

- Explicit invocation

- Human oversight

- No silent data reuse

- Clear accountability

Trust is a design requirement, not an afterthought.

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## What this means for organizations

Organizations can:

- Adopt AI safely in engineering workflows

- Maintain compliance and auditability

- Reduce risk without slowing teams down

AI accelerates work only when it is safe to do so.

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## Next steps

- Deterministic analysis guarantees

- Security architecture overview

- Trust & compliance reference