01 · Supporting context
The example uses a request-supplied name in a database lookup. Concatenation places that input directly inside SQL syntax.
Secuarden Code Intelligence · GitHub app
Find security risks, understand why they matter and review suggested fixes in your GitHub workflow—with context from your codebase.
A free plan is available through GitHub Marketplace.
One finding, fully explained
A useful review connects the code, the risk and the next action. Here is what that conversation can look like.
# Before: SQL built from input query = "SELECT id FROM users WHERE name = '" + name + "'" cursor.execute(query) # Suggested change: bind the value cursor.execute( "SELECT id FROM users WHERE name = ?", (name,) )
Illustrative code, not a customer scan result. Parameter syntax depends on the database driver; review and test any suggested change.
The example uses a request-supplied name in a database lookup. Concatenation places that input directly inside SQL syntax.
An attacker could alter the query rather than simply supply a search value. The impact depends on the query and the database account’s permissions.
Use the driver’s parameter binding. Add tests for names containing quotes, unexpected input and the intended search behaviour.
How it works
Choose the GitHub account or organisation where you want to use Secuarden.
Complete onboarding and select the repositories you want analysed.
Inspect risks alongside explanations and the repository context supporting them.
Review suggested remediation, test the change and make the merge decision.
Context behind the finding
Repository context gives reviewers a better starting point for understanding a finding.
Understand the conventions and components around the code being reviewed.
See the libraries and repository organisation that help explain the implementation.
Bring complementary analysis techniques into the review, with explanations a developer can inspect.
application/
├── routes/
│ └── search.py
│ Request input
├── services/
│ └── users.py
│ Search logic
├── data/
│ └── database.py
│ Query execution
└── tests/
└── test_search.py
Behaviour checksA simplified explanation of code context, not an assertion of a particular dataflow-analysis capability.
From findings to action
Work through inline feedback, understand the explanation and evaluate a suggested fix in the context of the change.
Use repository insights and downloadable reports to organise review and discuss findings with the teams responsible for the code.
Start with GitHub
Explore the free plan and current scan allowances on GitHub Marketplace.
Review the requested repository permissions during installation and confirm your organisation’s data-handling requirements before enabling analysis.
Languages listed in the Marketplace’s supported-language section:
Extend to Change Assurance
When you need to understand how AI participated in a change, explore the Change Ledger, policy controls and Context BOM. Available evidence depends on the integrations and agent activity captured in your workflow.
Before you connect
Code Intelligence evaluates code risks. That question is separate from identifying whether an AI agent contributed to the change; lineage is part of Change Assurance.
No. The public activity scanner examines commit metadata and AI attribution signals. Code Intelligence is the GitHub app for contextual code analysis and PR feedback.
This product provides remediation guidance for review. Treat suggested changes as code that needs validation, tests and the appropriate human approval.
Confirm repository access, retention, model-provider handling and organisational requirements before enabling private code analysis. Contact the team for your deployment’s details and review the app’s requested GitHub permissions.
A scan result is one input to review. Compliance and change assurance also depend on applicable requirements, evidence, approvals and other controls.
Secuarden Code Intelligence
Start with a repository. Bring code context, security findings and remediation guidance into the conversation.