AI-assisted development and AI security

The governing principle

BairesDev's AI coding best practices treats generated code as ordinary production code with additional provenance and verification needs. AI does not exempt a team from change control, vulnerability management, access control or compliance evidence.

Risk clusters

Generated-code defects

AI can reproduce insecure patterns at scale. Require tests, SAST, SCA, linting, review and build gates for every generated change.

Dependency hallucination and slopsquatting

Generated package names may not exist or may be attractive targets. Validate packages against trusted registries and review transitive dependencies.

Data privacy and policy ambiguity

BairesDev reports privacy and security concerns as a major barrier to client AI adoption. Define what data may enter assistants, which providers are approved, retention rules, secrets handling and human-review requirements.

Traceability and auditability

Keep focused commits, PR approvals, test results, deployment logs and—where useful—metadata about the assistant and human modifications. The question is not whether AI was used; it is whether the organization can explain and reproduce the change.

Agentic permissions

For agents that can read repositories, call tools or commit code, apply least privilege, sandboxing, approval boundaries, tool allowlists, output validation and kill switches. This is a synthesis from the blog's verification and governance logic, not a quoted BairesDev agent policy.

AI-specific AppSec control loop

define data/tool boundary -> generate -> validate dependencies -> test
-> SAST/SCA/secrets -> human review -> build gate -> deploy -> monitor
-> evaluate failures -> update policy and prompts

Cross-links

Connect this page to Testing and verification stack and Governance and metrics.